WO2020027207A1 - Procédé de détection d'anomalie, dispositif de traitement d'informations et système de détection d'anomalie - Google Patents

Procédé de détection d'anomalie, dispositif de traitement d'informations et système de détection d'anomalie Download PDF

Info

Publication number
WO2020027207A1
WO2020027207A1 PCT/JP2019/030043 JP2019030043W WO2020027207A1 WO 2020027207 A1 WO2020027207 A1 WO 2020027207A1 JP 2019030043 W JP2019030043 W JP 2019030043W WO 2020027207 A1 WO2020027207 A1 WO 2020027207A1
Authority
WO
WIPO (PCT)
Prior art keywords
time
series data
data
abnormality
attribute information
Prior art date
Application number
PCT/JP2019/030043
Other languages
English (en)
Japanese (ja)
Inventor
小掠　哲義
瞳 嶺岸
佐々木　幸紀
多鹿　陽介
Original Assignee
パナソニックＩｐマネジメント株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by パナソニックＩｐマネジメント株式会社 filed Critical パナソニックＩｐマネジメント株式会社
Publication of WO2020027207A1 publication Critical patent/WO2020027207A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to an abnormality detection method, an information processing device, and an abnormality detection system.
  • Patent Document 1 discloses a method of diagnosing a failure.
  • the failure sign is associated with the failure. Judge that you are.
  • the present disclosure provides an abnormality detection method, an information processing device, and an abnormality detection system that can accurately detect an abnormality.
  • an abnormality detection method provides a plurality of time-series data obtained from a plurality of elements related to manufacturing of a product, and attribute information of each of the plurality of time-series data. Acquiring, and analyzing the interrelationship of the plurality of time-series data, correcting the attribute information based on the result of the analysis, corrected attribute information and the plurality of time-series data And a step of outputting information indicating the detected abnormality and the time of occurrence of the abnormality based on the above.
  • the information processing apparatus includes an acquisition unit configured to acquire a plurality of time-series data obtained from a plurality of elements related to manufacturing of a product and attribute information of each of the plurality of time-series data.
  • An analysis unit that analyzes the mutual relationship between the plurality of time-series data, a correction unit that corrects the attribute information based on the result of the analysis, and the corrected attribute information and the plurality of time-series data.
  • a detection unit that detects an abnormality occurring in the plurality of elements based on the detected abnormality, and an output unit that outputs information indicating the detected abnormality and a time when the abnormality occurs.
  • the abnormality detection system includes the information processing device, a data storage device for storing a plurality of time-series data and a plurality of attribute information acquired by the acquisition unit, and the output unit. And a presentation device for presenting the information output from.
  • an abnormality can be detected with high accuracy.
  • FIG. 1 is a diagram illustrating a configuration of a factory that is a target of the abnormality detection device according to the first embodiment.
  • FIG. 2 is a block diagram illustrating a configuration of the abnormality detection device according to the first embodiment.
  • FIG. 3 is a diagram illustrating an example of time-series data acquired by the abnormality detection device according to the first embodiment.
  • FIG. 4 is a diagram illustrating an example of attribute information acquired by the abnormality detection device according to the first embodiment.
  • FIG. 5 is a flowchart illustrating an operation of the abnormality detection device according to the first embodiment.
  • FIG. 6 is a diagram illustrating an example of a result of the cross-correlation analysis performed by the abnormality detection device according to the first embodiment.
  • FIG. 1 is a diagram illustrating a configuration of a factory that is a target of the abnormality detection device according to the first embodiment.
  • FIG. 2 is a block diagram illustrating a configuration of the abnormality detection device according to the first embodiment.
  • FIG. 3 is
  • FIG. 7 is a flowchart illustrating a process of correcting attribute information in the operation of the abnormality detection device according to the first embodiment.
  • FIG. 8 is a diagram illustrating an example of correction of attribute information by the abnormality detection device according to the first embodiment.
  • FIG. 9 is a diagram illustrating an example of attribute information before correction for each group by the abnormality detection device according to the first embodiment.
  • FIG. 10 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the first embodiment.
  • FIG. 11 is a diagram illustrating an example of information output by the abnormality detection device according to the first embodiment.
  • FIG. 12 is a diagram illustrating a configuration example of a manufacturing apparatus determined by cross-correlation analysis by the abnormality detection device according to the modification of the first embodiment.
  • FIG. 13 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the modification of the first embodiment.
  • FIG. 14 is a diagram illustrating an example of information output by the abnormality detection device according to the modification of the first embodiment.
  • FIG. 15 is a diagram illustrating a configuration of a plurality of compressors that are targets of the abnormality detection device according to the second embodiment.
  • FIG. 16 is a block diagram illustrating a configuration of the abnormality detection device according to the second embodiment.
  • FIG. 17 is a diagram illustrating an example of attribute information acquired by the abnormality detection device according to the second embodiment.
  • FIG. 18 is a flowchart illustrating the operation of the abnormality detection device according to the second embodiment.
  • FIG. 19 is a diagram illustrating an example of a combination created by the abnormality detection device according to the second embodiment.
  • FIG. 20 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the second embodiment.
  • FIG. 21 is a diagram illustrating an example of information output by the abnormality detection device according to the second embodiment.
  • FIG. 22 is a diagram illustrating a configuration of a factory that is a target of the abnormality detection device according to the third embodiment.
  • FIG. 23 is a block diagram illustrating a configuration of the abnormality detection device according to the third embodiment.
  • FIG. 24 is a flowchart illustrating the operation of the abnormality detection device according to the third embodiment.
  • FIG. 25 is a diagram illustrating an example of a result of a multiple regression analysis performed by the abnormality detection device according to the third embodiment.
  • FIG. 26 is a diagram illustrating an example of correction of the attribute information by the abnormality detection device according to the third embodiment.
  • FIG. 27 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the third embodiment.
  • FIG. 28 is a diagram illustrating an example of information output by the abnormality detection device according to the third embodiment.
  • FIG. 29 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the comparative example.
  • FIG. 30 is a diagram illustrating an example of information output by the abnormality detection device according to the comparative example.
  • FIG. 31 is a diagram illustrating a configuration of a thermal model that is a target of the abnormality detection device according to the modification of the third embodiment.
  • FIG. 32 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the modification of the third embodiment.
  • FIG. 33 is a diagram illustrating an example of information output by the abnormality detection device according to the modification of the third embodiment.
  • FIG. 34 is a block diagram illustrating a configuration of an abnormality detection system that executes the abnormality detection method according to the fourth embodiment.
  • FIG. 35 is a block diagram illustrating another example of the configuration of the abnormality detection system that executes the abnormality detection method according to the fourth embodiment.
  • FIG. 36 is a block diagram illustrating a configuration of the abnormality detection system according to the fourth embodiment.
  • an abnormality detection method provides a plurality of time-series data obtained from a plurality of elements related to manufacturing of a product, and an attribute of each of the plurality of time-series data. Obtaining information, analyzing the interrelationship of the plurality of time-series data, correcting the attribute information based on a result of the analysis, and modifying the attribute information and the plurality of time-series data. Detecting an abnormality occurring in the plurality of elements based on the data; and outputting information indicating the detected abnormality and the time of occurrence of the abnormality.
  • the error can be detected by analyzing the correlation between a plurality of time-series data. For example, when different attribute information is associated with the two time-series data despite the strong relationship between the two time-series data, it is determined that at least one of the attribute information is incorrect. can do. As described above, since an error in the attribute information can be detected, the attribute information can be corrected. Further, since the abnormality is detected based on the corrected attribute information, the abnormality can be detected with high accuracy.
  • the analysis may be a statistical analysis.
  • the statistical analysis may be a cross-correlation analysis.
  • the mutual relationship for example, the strength of the relationship
  • the mutual relationship between a plurality of time-series data can be represented by a numerical value such as a cross-correlation coefficient, so that the mutual relationship between the time-series data is easily and accurately determined. be able to. For this reason, an error in the attribute information corresponding to the time-series data can be easily and accurately detected, so that an abnormality can be accurately detected.
  • a cross-correlation coefficient of the plurality of time-series data is calculated, and the plurality of time-series data is classified into a plurality of groups based on the calculated cross-correlation coefficient.
  • a plurality of time-series data included in the group is classified into a minority and a majority based on corresponding attribute information, and the time-series data belonging to the minority
  • the attribute information may be modified to the attribute information of the time-series data belonging to the majority.
  • the attribute of the time-series data belonging to the minority may be modified to attribute information of the time-series data belonging to the majority.
  • the statistical analysis may be a multiple regression analysis.
  • the plurality of elements include a plurality of compressors whose outputs from each are integrated, and in the obtaining step, a plurality of pressure data, which are time-series data of pressures from each of the plurality of compressors, A plurality of flow rate data which is time series data of flow rate, and integrated flow rate data which is time series data of flow rate and integrated pressure data which is time series data of integrated pressure, are obtained as the plurality of time series data,
  • a plurality of combinations of the plurality of pressure data and the plurality of flow rate data, and a plurality of attribute information candidates corresponding to each of the plurality of pressure data and the plurality of flow rate data were created and created.
  • a plurality of pressure data and a plurality of flow rate data constituting the combination are made independent variables, and the integrated pressure data and the integrated
  • the amount data as the dependent variable may be performed multiple regression analysis.
  • the plurality of elements include an air conditioner disposed in each of a plurality of spaces, and a plurality of manufacturing devices each disposed in any one of the plurality of spaces, and the acquisition is performed.
  • the step acquiring the time series data of the power consumption of each of the plurality of air conditioners and the time series data of the parameters of each of the plurality of manufacturing apparatuses as the plurality of time series data, and performing the analysis.
  • multiple regression analysis may be performed using the time series data of the parameter as an independent variable and the time series data of the power consumption as a dependent variable.
  • information indicating the cause of the detected abnormality may be output.
  • information indicating a countermeasure for the abnormality which is determined according to the detected abnormality, may be further output.
  • the countermeasure plan is output, so that the user involved in the manufacture of the product can easily determine what action should be taken for the abnormality that has occurred. For this reason, for example, maintenance work can be performed promptly, or production of a product can be continued using an alternative device or the like, so that a decrease in production efficiency can be suppressed.
  • the abnormality detection method it is possible to assist in suppressing a decrease in production efficiency.
  • the information processing apparatus includes an acquisition unit configured to acquire a plurality of time-series data obtained from a plurality of elements related to manufacturing of a product and attribute information of each of the plurality of time-series data.
  • An analysis unit that analyzes the mutual relationship between the plurality of time-series data, a correction unit that corrects the attribute information based on the result of the analysis, and the corrected attribute information and the plurality of time-series data.
  • a detection unit that detects an abnormality occurring in the plurality of elements, and an output unit that outputs information indicating the detected abnormality and an occurrence time of the abnormality.
  • the abnormality detection system includes the information processing device, a data storage device for storing a plurality of time-series data and a plurality of attribute information acquired by the acquisition unit, and the output unit. And a presentation device for presenting the information output from the device.
  • each drawing is a schematic diagram and is not necessarily strictly illustrated. Further, in each of the drawings, substantially the same configuration is denoted by the same reference numeral, and redundant description will be omitted or simplified.
  • the abnormality detection device detects, for example, an abnormality that occurs in a device included in a manufacturing system in a factory or the like in which a manufacturing system that manufactures a product is installed.
  • FIG. 1 is a diagram showing a configuration of a factory 1 which is a target of the abnormality detection device 100 according to the present embodiment.
  • the factory 1 a plurality of devices related to the manufacture of a product are arranged in each of the three rooms A to C. Specifically, as shown in FIG. 1, in a room A, a mounting machine A, a molding machine A, and an air conditioner A are arranged.
  • a mounting machine A In the room B, a molding machine B, a coating machine B, a welding machine B, a press machine B, and an air conditioner B are arranged.
  • a press C and an air conditioner C are arranged in the room C.
  • the mounting machine A, the molding machines A and B, the coating machine B, the welding machine B, the press machines B and C, and the air conditioners A to C are each an example of a plurality of elements related to the manufacture of a product.
  • the plurality of elements include elements other than the manufacturing apparatus that directly manufactures products, such as the air conditioners A to C.
  • the plurality of elements may include, for example, a repeater that relays communication between the devices. Further, the plurality of elements may include a compressor.
  • the abnormality detection device 100 acquires a plurality of time-series data obtained from the plurality of elements, and detects an abnormality occurring in the plurality of elements based on the acquired plurality of time-series data.
  • the time-series data represents a temporal change of a numerical value of one or more parameters of each element.
  • the one or more parameters include, for example, power consumption, the number of operations, the operation time, the number of inserted components, the number of output components, and the like.
  • Each of the plurality of elements is provided with a sensor or a measuring device for detecting a numerical value of the parameter.
  • the abnormality detection device 100 is communicably connected by wire or wirelessly to a sensor or a measuring device provided for each of the plurality of elements.
  • the abnormality detection device 100 is realized by, for example, a computer device. Specifically, the abnormality detection device 100 is realized by a nonvolatile memory in which a program is stored, a volatile memory that is a temporary storage area for executing the program, an input / output port, a processor that executes the program, and the like. You.
  • FIG. 2 is a block diagram showing a configuration of the abnormality detection device 100 according to the present embodiment.
  • the abnormality detection device 100 includes an acquisition unit 110, an analysis unit 120, a correction unit 130, a detection unit 140, and an output unit 150. Further, the abnormality detection device 100 includes a storage unit 160 for storing various data and information.
  • the acquisition unit 110 acquires a plurality of time-series data obtained from a plurality of elements related to manufacturing of a product and attribute information of each of the plurality of time-series data.
  • the acquiring unit 110 is realized by, for example, an input interface or a communication interface to which output data from a sensor or a measuring instrument attached to each device such as the manufacturing apparatus and the air conditioner illustrated in FIG. 1 is input.
  • the acquiring unit 110 acquires a plurality of time-series data by periodically acquiring output data from each sensor or measuring instrument.
  • the acquisition unit 110 is further realized by a user interface or the like that receives an operation input by an administrator of the factory 1 or the like.
  • the acquisition unit 110 acquires attribute information by receiving an operation input. That is, in the present embodiment, the attribute information is information input by a person such as a manager of the factory 1. For this reason, an error may occur in the correspondence relationship with the time-series data due to a human error or omission in input.
  • the plurality of time-series data and attribute information acquired by the acquisition unit 110 are stored in the storage unit 160.
  • a plurality of time-series data is managed in a time-series data DB (database) 162, and attribute information is managed in an attribute information DB 164.
  • the analysis unit 120 analyzes the cross-correlation of the plurality of time-series data acquired by the acquisition unit 110.
  • the analysis unit 120 specifically refers to the time-series data DB 162 stored in the storage unit 160, and analyzes the mutual relationship using the plurality of time-series data read from the storage unit 160.
  • the analysis unit 120 outputs the result of the analysis to the correction unit 130.
  • the analysis unit 120 performs a statistical analysis. Specifically, the analysis unit 120 performs a cross-correlation analysis. More specifically, analysis section 120 calculates a cross-correlation coefficient of a plurality of time-series data, and classifies the plurality of time-series data into a plurality of groups based on the calculated cross-correlation coefficient.
  • the analysis unit 120 selects two time series data from the plurality of time series data, and calculates a cross-correlation coefficient of the selected time series data.
  • N is a natural number of 2 or more
  • N (N-1) / 2 combinations for selecting two time series data from the N pieces of time series data. Therefore, N (N-1) / 2 cross-correlation coefficients are calculated.
  • the cross-correlation coefficient is represented by a value between ⁇ 1 and +1.
  • the analysis unit 120 classifies the plurality of time-series data into a plurality of groups according to the strength of the correlation by comparing the cross-correlation coefficient with the threshold. Specifically, the analysis unit 120 compares the absolute value of the cross-correlation coefficient with a threshold.
  • the threshold value is, for example, 0.75, but is not limited thereto.
  • a plurality of time-series data in which the absolute value of the cross-correlation coefficient is larger than 0.75 is set as a group having a large correlation.
  • the correction unit 130 corrects the attribute information based on the result of the analysis by the analysis unit 120. Specifically, the correction unit 130 refers to the attribute information DB 164 stored in the storage unit 160, and uses the plurality of pieces of attribute information read from the storage unit 160 and the analysis result obtained from the analysis unit 120. Modify attribute information. The correction unit 130 reflects the result of the correction in the attribute information DB 164.
  • the correction unit 130 classifies, for each group classified by the analysis unit 120, a plurality of time-series data included in the group into a minority group and a majority group based on corresponding attribute information. .
  • the correction unit 130 corrects the attribute information of the time-series data belonging to the minority into the attribute information of the time-series data belonging to the majority.
  • the correction unit 130 deletes the attribute information of the time-series data belonging to the minority. Modify to the attribute information of the time series data belonging to the faction. For example, when the number of time-series data belonging to the minority is equal to or less than 1/4 of the number of time-series data belonging to the majority, the correction unit 130 determines that the total number of time-series data belonging to the group is equal to or less than 20%. , The attribute information of the time-series data belonging to the minority is corrected. The specific operation of the correction will be described later in detail with an example.
  • the detection unit 140 detects an abnormality occurring in a plurality of elements based on the corrected attribute information and the plurality of time-series data. For example, the detection unit 140 detects an abnormality for each abnormality to be detected by using a predetermined abnormality detection model or rule.
  • the detection unit 140 refers to the time-series data DB 162, the attribute information DB 164, and the abnormality detection model DB 166 stored in the storage unit 160, and reads necessary data to detect an abnormality.
  • the detection unit 140 outputs the result of the detection of the abnormality to the output unit 150.
  • Abnormalities to be detected include, for example, abnormalities that occur in a manufacturing apparatus such as a mounting machine, a molding machine, a coating machine, a welding machine or a press machine.
  • the abnormality to be detected may include an abnormality that occurs in an air conditioner such as an air conditioner, or an abnormality that occurs in a space (specifically, a room) in which air conditioning is controlled by the air conditioner.
  • the abnormality to be detected may include an abnormality occurring in the compressor. The specific operation of the abnormality detection will be described later in detail with an example.
  • the analysis unit 120, the correction unit 130, and the detection unit 140 are each realized by, for example, software executed by a processor. Alternatively, at least one of the analysis unit 120, the correction unit 130, and the detection unit 140 may be realized by hardware such as an electronic circuit including a plurality of circuit elements.
  • the output unit 150 outputs information indicating the detected abnormality and the time when the abnormality occurs.
  • output unit 150 further outputs information indicating the cause of the abnormality or information indicating a countermeasure for the abnormality that is predetermined according to the detected abnormality.
  • the output unit 150 refers to the countermeasure information DB 168 stored in the storage unit 160, determines a countermeasure corresponding to the detected abnormality, and outputs information indicating the determined countermeasure.
  • the output unit 150 is realized by, for example, a display for displaying information, but is not limited thereto.
  • the output unit 150 may be realized by a speaker that outputs information as sound.
  • the output unit 150 may be realized by an output interface or a communication interface for outputting information to an external display, a speaker, or the like.
  • the storage unit 160 is realized by, for example, a semiconductor memory or a HDD (Hard Disk Drive). In the present embodiment, the storage unit 160 stores various data and information necessary for executing the abnormality detection method in a database. Specifically, as shown in FIG. 2, the storage unit 160 stores a time-series data DB 162, an attribute information DB 164, an abnormality detection model DB 166, and a countermeasure information DB 168.
  • the time series data DB 162 includes a plurality of time series data acquired by the acquisition unit 110.
  • the time series data DB 162 is generated and updated when the acquisition unit 110 acquires the time series data.
  • a specific example of the time-series data DB 162 will be described later with reference to FIG.
  • the attribute information DB 164 includes a plurality of pieces of attribute information acquired by the acquisition unit 110.
  • the plurality of pieces of attribute information respectively correspond to the plurality of pieces of time-series data.
  • the attribute information DB 164 is generated and updated when the acquisition unit 110 acquires the attribute information.
  • the attribute information DB 164 is updated by the correction unit 130. A specific example of the attribute information DB 164 will be described later with reference to FIG.
  • the anomaly detection model DB 166 includes a model or a rule used by the detection unit 140 for anomaly detection.
  • available models or rules are associated with each abnormality to be detected.
  • the measure information DB 168 includes a plurality of measure plans.
  • one or more predetermined countermeasures are associated with each detected abnormality.
  • the one or more countermeasures include, for example, stopping the device in which the abnormality is detected, instructing maintenance of the device, and presenting an alternative device that can be used in place of the device.
  • time-series data DB 162 and the attribute information DB 164 stored in the storage unit 160 will be described.
  • FIG. 3 is a diagram illustrating an example of a plurality of time-series data acquired by the abnormality detection device 100 according to the present embodiment. Specifically, FIG. 3 shows a part of the time-series data DB 162 stored in the storage unit 160.
  • time-series data DB 162 a plurality of time-series data are managed in association with each time.
  • the time is represented by, for example, date and time (date and time), but may not include the date.
  • time-series data DB 162 the time-series data is shown as data for one column.
  • An identifier (specifically, data ID) is attached to the time-series data.
  • the data ID is composed of a combination of “Data-” and a two-digit numerical value, such as “Data-01” in the example shown in FIG.
  • the configuration of the data ID is not particularly limited.
  • One piece of time-series data includes a numerical value of a parameter (for example, power consumption) for each fixed period.
  • the numerical value may be, for example, an instantaneous value of the parameter, or a cumulative value or an average value of the parameter within a period.
  • the period is, for example, one second or more and several ten minutes or less, but is not limited thereto.
  • the time-series data indicates a numerical value every 10 minutes.
  • FIG. 4 is a diagram illustrating an example of attribute information acquired by the abnormality detection device 100 according to the present embodiment. Specifically, FIG. 4 illustrates the attribute information DB 164 stored in the storage unit 160.
  • attribute information is associated with each data ID representing time-series data.
  • the attribute information is classified into a plurality of items.
  • the items are an installation room, a device name, and a data type.
  • the installation room and the device name indicate the room in which the device to which the sensor or the measuring device for measuring the numerical value of the corresponding time-series data is mounted is installed, and the name of the device.
  • the type of data corresponds to the content of the parameter of the device.
  • the attribute information DB 164 is generated by, for example, input by a person such as an administrator of the factory 1, a designer, an operator of each device, or a maintenance worker. For this reason, an erroneous input due to a human error may occur, and the correspondence between the attribute information and the time-series data may include an error.
  • the attribute information in which an error has occurred and needs to be corrected is shown in bold letters, and the background is shaded with dots.
  • the error is detected by the analysis by the analysis unit 120, and is corrected by the correction unit 130.
  • FIG. 5 is a flowchart showing the operation of the abnormality detection device 100 according to the present embodiment.
  • the acquisition unit 110 first acquires the time-series data and the attribute information (S10).
  • the acquisition unit 110 acquires a sensor value (that is, a numerical value of each parameter) from, for example, a sensor or a measuring device provided in each device of the factory 1 every 10 minutes, and stores the acquired sensor value in the storage unit 160, for example.
  • a sensor value that is, a numerical value of each parameter
  • the acquisition unit 110 acquires attribute information corresponding to a plurality of time-series data by receiving an input from a manager of the factory 1 or the like. Either the acquisition of the time-series data or the acquisition of the attribute information may be performed first, or may be performed simultaneously.
  • the analysis unit 120 analyzes the cross-correlation of the plurality of time-series data (S11).
  • analysis unit 120 selects two time-series data from time-series data DB 162 stored in storage unit 160, and calculates a cross-correlation coefficient between the selected two time-series data.
  • the analysis unit 120 repeats the selection of two time series data for which the cross-correlation coefficient has not been calculated from the N time series data stored in the storage unit 160, so that N (N ⁇ 1) / 2 Is calculated.
  • the analysis unit 120 classifies the plurality of time-series data into a plurality of groups by comparing the calculated cross-correlation coefficient with a threshold. For example, the analysis unit 120 determines that two time-series data in which the absolute value of the cross-correlation coefficient is greater than 0.75 belong to the same group, and classifies the N time-series data into a plurality of groups.
  • FIG. 6 is a diagram showing an example of the result of the cross-correlation analysis by the abnormality detection device 100 according to the present embodiment. Specifically, FIG. 6 schematically shows a state in which 20 time-series data Data-01 to Data-20 are classified into four groups A to D. The remaining Data-21 to Data-23 are not shown in FIG. 6 because they are classified into single groups E to G, respectively.
  • FIG. 6 shows not only a group having a large correlation but also a correlation (medium correlation or small correlation) between the groups.
  • the difference in the correlation strength is determined by using a plurality of thresholds. The details will be described later as a modification of the present embodiment.
  • the correction unit 130 corrects the attribute information as shown in FIG. 5 (S12). In the present embodiment, the correction unit 130 corrects the device name in the attribute information.
  • FIG. 7 is a flowchart showing a process of correcting attribute information in the operation of abnormality detection apparatus 100 according to the present embodiment.
  • FIG. 8 is a diagram showing an example of the correction of the attribute information by the abnormality detection device 100 according to the present embodiment.
  • a grouping result obtained by the cross-correlation analysis and various types of information associated when performing the attribute information correction process are added to the attribute information DB 164.
  • FIG. 9 is a diagram showing an example of attribute information before correction for each group by the abnormality detection device 100 according to the present embodiment. Specifically, FIG. 9 corresponds to the grouping result of FIG. 6, and shows, instead of the data ID, the device name and the type of data associated with the data ID.
  • the correction unit 130 sets a correction flag (temporary) based on the grouping result obtained by the cross-correlation analysis (S20). Specifically, the correction unit 130 classifies the time-series data into a minority group and a majority group based on the attribute information for each group. Here, the time-series data is classified into a minority group and a majority group based on the device name which is the attribute information to be corrected.
  • the device names of the five time-series data are “mounting machines”, and all of them are classified as majority.
  • the device name of the four time-series data Data-06, Data-09, Data-16 and Data-19 is “molding machine”, whereas the device name of one time-series data Data-10 is Is the “mounting machine”. That is, in the group B, the four time-series data Data-06, Data-09, Data-16, and Data-19 are classified into the majority, and one time-series data Data-10 is classified into the minority.
  • the time-series data Data-13 is classified as a minority
  • the time-series data Data-08 is classified as a minority.
  • the correction unit 130 sets the correction flag (provisional) of each of the time-series data Data-08, Data-10, and Data-13 classified as the minority to “TRUE”, and sets the remaining 20 time-series data.
  • Each correction flag (temporary) is set to “FALSE”.
  • the correction flag (temporary) is information indicating whether or not the device name of the associated time-series data is to be corrected. When the correction flag (temporary) is “TRUE”, a process of correcting the device name of the time-series data associated with the correction flag (temporary) is performed.
  • the correction unit 130 sets the correction flag (provisional) for the minority only when the ratio of the time-series data classified to the minority to the total number of the time-series data in the group is sufficiently small. Is set to “TRUE”. For example, when the ratio of the time series data is 20% or less, the correction flag (temporary) of the minority is set to “TRUE”. As shown in FIGS. 8 and 9, the proportion of the minority in all of the groups B to D is 20% or less. For this reason, the correction unit 130 sets the correction flag (temporary) of each of the time series data Data-08, Data-10, and Data-13, which are the minorities of the groups B to D, to “TRUE”.
  • the correction unit 130 provisionally sets the candidate information (S21).
  • the candidate information is, specifically, a device name that becomes a correction candidate.
  • the device name that is a correction candidate is the device name of the time-series data belonging to the majority of the group. Therefore, as shown in FIG. 8, the device name of the correction candidate of the time-series data Data-13 belonging to the group B is “applicator” which is the device name of the time-series data belonging to the majority of the group B.
  • the device name of the correction candidate of the time-series data Data-10 belonging to the group C is “molding machine”.
  • the device name of the correction candidate of the time-series data Data-08 belonging to the group D is “press machine”.
  • the correction unit 130 sets a duplication flag (S23). Specifically, the correction unit 130 sets “TRUE” when the set of the installation room, the device name, and the data type after correction overlaps with the set of the installation room, the device name, and the data type of another data ID. If no duplicate data exists, "FALSE" is set. For example, in the time-series data Data-08, the installation room, the device name, and the data type after the correction are “RoomB”, “press machine”, and “operation time”, respectively, and do not overlap with other data. Therefore, the correction unit 130 sets the overlap flag to “FALSE”.
  • the correction unit 130 extracts a representative value of the time-series data (S24).
  • the representative value is, for example, at least one of a minimum value, a maximum value, and an average value of numerical values included in the time-series data.
  • the correction unit 130 reads the minimum value, the maximum value, and the average value of the time-series data Data-08 with reference to the time-series data DB 162.
  • the correction unit 130 extracts an allowable representative value (S25).
  • the permissible representative value is a value determined based on, for example, the specifications of the device or a past operation history, and is stored in the storage unit 160.
  • the permissible representative value is at least one of a minimum value and a maximum value of a range permitted as the operation of the apparatus, and an assumed average value.
  • the correction unit 130 associates the allowable representative value read from the storage unit 160 with the data ID. When the allowable representative value is unknown, the correction unit 130 associates that the allowable representative value is unknown with the data ID.
  • the correction unit 130 determines whether or not inconsistency has occurred between the allowable representative value and the representative value of the time-series data (S26). For example, if the minimum value of the time series data is equal to or larger than the minimum value of the allowable representative value, no inconsistency occurs. If the maximum value of the time series data is equal to or less than the maximum value of the allowable representative value, no inconsistency occurs. If the average value of the time-series data is equal to the average value of the allowable representative values (for example, the difference is within ⁇ 10% to 20%), no inconsistency occurs.
  • the correction unit 130 corrects the device name in the attribute information DB 164 to a device name of a correction candidate (S27). By setting the correction flag to “TRUE”, the correction of the attribute information of the time-series data is completed.
  • the correction unit 130 sets another candidate information and executes the above-described processing (S23 to S27). You may. For example, when the minimum value of the time-series data is smaller than the minimum value of the allowable representative value, a contradiction occurs. Alternatively, when the maximum value of the time-series data is larger than the maximum value of the allowable representative values, a contradiction occurs. If the average value of the time-series data is not equal to the average value of the allowable representative values (for example, the difference is within ⁇ 10% to 20%), a contradiction occurs. Alternatively, the correction unit 130 corrects the attribute information by causing the output unit 150 to output information notifying that the correction has not been correctly performed, and causing the administrator of the factory 1 to input the attribute information. Is also good.
  • the correction unit 130 may set another candidate information and execute the above-described processing (S23 to S27). Alternatively, information notifying that the correction was not correctly performed may be output from the output unit 150, and the information may be corrected by a manager of the factory 1 or the like.
  • the detection unit 140 detects an abnormality (S13).
  • the detection of the abnormality may be performed immediately after the attribute information is corrected, or may be performed at a predetermined timing. Further, the detection of the abnormality may be periodically repeated.
  • the detection unit 140 reads data from each of the time-series data DB 162, the attribute information DB 164, and the abnormality detection model DB 166 of the storage unit 160 for each abnormality to be detected, and detects an abnormality based on the read data. The presence / absence and the time when an abnormality has occurred are detected.
  • the detection unit 140 detects an abnormality of the molding machine will be described as an example.
  • the detection unit 140 reads and uses the abnormality detection model of the molding machine from the abnormality detection model DB 166.
  • the abnormality detection model of the molding machine it is determined that no abnormality has occurred in the molding machine when the ratio between the number of parts supplied to the molding machine and the power consumption of the molding machine falls within a predetermined range. When the ratio deviates from the predetermined range, it is determined that an abnormality has occurred in the molding machine.
  • abnormality detection model of the molding machine for example, when the number of inputs [pieces] / power consumption [kWh] is in the range of 1.2 or more and 2.0 or less, it is determined that no abnormality has occurred, and When [unit] / power consumption [kWh] is less than 1.2 or greater than 2.0, it is determined that an abnormality has occurred.
  • the detection unit 140 specifies the time-series data corresponding to each of the power consumption and the number of inputs of the target molding machine by referring to the attribute information DB 164. As shown in FIG. 8, it is specified that the power consumption of the molding machine is time-series data Data-06, and the number of injections of the molding machine is time-series data Data-10.
  • the attribute information DB 164 before correction shown in FIG. 4 does not include time-series data corresponding to the number of injections of the molding machine. Therefore, although it actually exists, the time series data corresponding to the number of injections of the molding machine is not specified, and the abnormality detection process cannot be performed.
  • the time-series data Data-10 corresponding to the number of injections of the molding machine is specified. For this reason, the abnormality detection process can be performed with high accuracy.
  • FIG. 10 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device 100 according to the present embodiment.
  • the horizontal axis represents time.
  • the vertical axis of FIG. 10A represents the power consumption [kWh] and the number of inputs [pieces]. That is, FIG. 10A is a two-dimensional graph of time-series data Data-06 indicating power consumption and time-series data Data-10 indicating the number of inputs.
  • the vertical axis in FIG. 10B represents the number of inputs / power consumption [pcs / kWh].
  • the detecting unit 140 determines that an abnormality indicating that the productivity has decreased has occurred in the molding machine in the period T1.
  • the detecting unit 140 determines that an abnormality indicating that useless power consumption occurs has occurred in the molding machine in the period T2.
  • the type of the abnormality is predetermined in the abnormality detection model of the molding machine based on the value of the number of inputs / power consumption. For example, when the number of inputs / power consumption is 0 and the power consumption is greater than 0, the type of abnormality is determined to be useless power consumption. When the number of inputs / power consumption is larger than 0 and smaller than 1.2, it is determined that the type of abnormality is productivity reduction. When the number of inputs / power consumption is greater than 2.0, the type of abnormality is defined as excessive supply of components or a sensor failure.
  • the output unit 150 After the abnormality is detected by the detection unit 140 in this way, as shown in FIG. 5, the output unit 150 outputs information indicating the detected abnormality and the occurrence time of the abnormality (S14). In addition, the output unit 150 determines a countermeasure against the abnormality by referring to the countermeasure information DB 168 stored in the storage unit 160 based on the detected abnormality, and outputs information indicating the determined countermeasure. I do.
  • FIG. 11 is a diagram illustrating an example of information output by the abnormality detection device 100 according to the present embodiment.
  • the output unit 150 generates an image representing the detected abnormality, the occurrence time of the abnormality, and a countermeasure for the abnormality, and displays the generated image on a display or the like, so that the administrator of the factory 1 or the like. To present.
  • the processing up to the correction of the attribute information is performed as a preceding step of the abnormality detection method, and the processing after the detection of the abnormality is performed as a subsequent step. If the attribute information has been corrected at least once and it has been confirmed that the attribute information has been correctly corrected, the cross-correlation analysis (S11) and the attribute information correction (S12) may not be performed. Good. In this case, acquisition of time-series data (S10), detection of abnormality (S13), and output of information (S14) may be periodically repeated.
  • Detection of an abnormality and output of information may be performed in real time in parallel with manufacture of a product. Accordingly, when an abnormality is detected, a response such as maintenance can be promptly performed, so that a decrease in production efficiency can be suppressed.
  • the detection of the abnormality and the output of the information may be performed every period such as one week or one month. This makes it possible to specify a device or the like in which an abnormality is likely to occur when a product is manufactured for a long period of time. For this reason, it can be used for formulation of a future production plan, such as a change in the design of a production line or replacement of a production device.
  • the abnormality detection method and the abnormality detection device are capable of suppressing not only a short-term decrease in production efficiency with emphasis on real-time performance but also a decrease in production efficiency from a medium- to long-term perspective. Can help.
  • the analysis unit 120 selects two time-series data from a plurality of time-series data, and calculates a cross-correlation coefficient and a delay of the selected time-series data. By calculating the delay, the relationship between groups having relatively small correlation can be determined. Specifically, the analysis unit 120 determines the line configuration of each manufacturing apparatus based on the delay.
  • the analysis unit 120 compares the absolute value of the cross-correlation coefficient with a plurality of thresholds.
  • the plurality of threshold values include not only 0.75 but also 0.6 and 0.4. This makes it possible to classify the groups according to the strength of the correlation. For example, by using three thresholds, a plurality of time-series data can be classified into a large correlation group, a medium correlation group, a small correlation group, and no correlation. Further, when a plurality of groups having a large correlation are formed, the strength of the correlation between the plurality of groups can be determined.
  • the analysis unit 120 determines that there is a small correlation between the two time-series data. .
  • the analysis unit 120 determines that the manufacturing apparatuses corresponding to the time-series data included in the group determined to have a small correlation form the same manufacturing line.
  • the classification result based on a plurality of thresholds of the 20 time-series data shown in FIGS. 6 and 9 will be described.
  • the absolute value of the cross-correlation coefficient between the time-series data classified into group A and the time-series data classified into group B is larger than 0.65 and 0.75 or less. For this reason, it can be determined that the group A and the group B have a moderate correlation.
  • the threshold value used for classifying the group is set to 0.65 instead of 0.75, ten pieces of time-series data are classified into one group including the group A and the group B. The same applies to the groups A and C, and the same applies to the groups C and D.
  • the absolute value of the cross-correlation coefficient between the time-series data classified into group B and the time-series data classified into group C is larger than 0.40 and 0.65 or less. Therefore, it is determined that group A and group C have a small correlation. Further, the absolute value of the cross-correlation coefficient between the time-series data classified into group A and the time-series data classified into group D is 0.40 or less, and there is almost no correlation between group A and group D. It is determined that there is not.
  • each of the groups A to D shown in FIG. 6 has a small correlation or a moderate correlation with each other. Therefore, the analysis unit 120 determines that the mounting machine, the molding machine, the coating machine, and the press machine constitute one manufacturing line.
  • the analysis unit 120 determines the order of each manufacturing apparatus based on the delay. Specifically, the analysis unit 120 determines that the delay of the second time-series data with respect to the first time-series data is a positive value and the absolute value thereof is larger, the manufacturing time corresponding to the second time-series data is larger. The apparatus determines that the apparatus is located downstream of the manufacturing line from the manufacturing apparatus corresponding to the first time-series data. Similarly, as the delay of the second time-series data with respect to the first time-series data is a negative value and the absolute value thereof is larger, the analysis unit 120 determines that the manufacturing apparatus corresponding to the second time-series data has a larger value. , Is located upstream of the manufacturing line with respect to the manufacturing apparatus corresponding to the first time-series data.
  • the configuration of the production line is determined based on the classification result shown in FIG.
  • the determination of the configuration of the production line is performed by the analysis unit 120 after the attribute information is corrected as described in the first embodiment. Therefore, the time-series data included in each of the groups A to D corresponds to each of the mounting machine, the coating machine, the molding machine, and the press machine.
  • FIG. 12 is a diagram illustrating a configuration example of a manufacturing apparatus determined by a cross-correlation analysis by the abnormality detection apparatus 100 according to the present modification.
  • the configuration of the manufacturing line can be determined by the cross-correlation analysis by the analysis unit 120. Therefore, the detection unit 140 can detect the abnormality with higher accuracy.
  • a model for detecting an abnormality of a coating machine will be described.
  • the ratio between the number of parts produced by the coating machine and the number of parts supplied to the mounting machine after the delay time of ⁇ t is within a predetermined range, It is determined that no abnormality has occurred in the coating machine.
  • the ratio deviates from the predetermined range it is determined that an abnormality has occurred.
  • the abnormality detection model of the coating machine is such that when the production number [pieces] of the coating machine / the number [pieces] of the mounting machine after 30 seconds is included in the range of 0.8 or more and 1.2 or less, the abnormality If it is determined that no occurrence has occurred, and the number of coating machines produced [pieces] / the number of loading machines after 30 seconds [pieces] is less than 0.8 or greater than 1.2, an abnormality occurs. It is determined that there is.
  • FIG. 13 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device 100 according to the present modification.
  • the horizontal axis represents time.
  • the vertical axis indicates the number of production machines [pcs] and the number of mounting machines [pcs].
  • the production number of the applicator became zero at 13:12, and the production of the applicator was stopped. Thereafter, at 13:13, the number of mounted machines becomes zero. Since the production number of coating machines / the number of mounting machines is 0 or a value that cannot be calculated, the detection unit 140 determines that an abnormality has been detected.
  • the detection unit 140 determines the period from when the production number of the coating machine becomes 0 to when the number of mounting machines becomes 0, and the delay between the coating machine and the mounting machine calculated by the analysis unit 120. Compare with time. When the period from when the production number of the coating machine becomes 0 to when the number of the mounting machines becomes 0 is equal to the delay time (for example, the difference is twice or less), the detection unit 140 detects the production line. It is determined that an abnormality has occurred in the device located upstream and no abnormality has occurred in the device located downstream. Thus, in the examples shown in FIGS. 12 and 13, it is determined that an abnormality has occurred in the coating machine and no abnormality has occurred in the mounting machine.
  • the output unit 150 Based on the detection result of such an abnormality, the output unit 150 outputs the detected abnormality, the occurrence time of the abnormality, and a measure.
  • FIG. 14 is a diagram illustrating an example of information output by the abnormality detection device 100 according to the present modification.
  • the output unit 150 generates an image indicating the detected abnormality, the occurrence time of the abnormality, and a countermeasure for the abnormality, and displays the image on a display or the like, so that the administrator of the factory 1 or the like. To present.
  • the output unit 150 displays not the maintenance of the mounting machine but the maintenance of the coating machine.
  • FIG. 15 is a diagram illustrating a configuration of a plurality of compressors A to C that are targets of the abnormality detection device 200 according to the present embodiment.
  • Each of the plurality of compressors A to C is an example of a plurality of elements involved in manufacturing a product.
  • the plurality of compressors A to C are devices that compress and output a gas such as air.
  • the outputs from each of the plurality of compressors A to C are integrated. Specifically, the output of the compressor A, the output of the compressor B, and the output of the compressor C are integrated and output.
  • the abnormality detection device 200 acquires pressure data, which is time-series data of the output pressure of each of the plurality of compressors A to C, and flow rate data, which is time-series data of the flow rate. Further, the abnormality detection device 200 acquires power data, which is time-series data of power consumption, by acquiring sensor values of power consumption in each compressor from sensors attached to the plurality of compressors A to C. Further, the abnormality detection device 200 acquires integrated pressure data, which is time series data of the integrated pressure of the plurality of compressors A to C, and integrated flow rate data, which is time series data of the flow rate. The abnormality detection device 200 detects an abnormality occurring in the plurality of compressors A to C based on the acquired plurality of time-series data.
  • FIG. 16 is a block diagram showing a configuration of an abnormality detection device 200 according to the present embodiment.
  • the abnormality detection device 200 includes an acquisition unit 210, an analysis unit 220, a correction unit 230, a detection unit 240, and an output unit 150. Further, the abnormality detection device 200 includes a storage unit 260 for storing various data and information.
  • the acquisition unit 210 acquires a plurality of time-series data obtained from each of the plurality of compressors A to C and attribute information of each of the plurality of time-series data. Specifically, the acquiring unit 210 acquires pressure data and flow rate data from each of the plurality of compressors A to C, and integrated pressure data and integrated flow rate data. For example, a pressure gauge and a flow meter are attached to each output line of the plurality of compressors A to C and the output line after integration.
  • the acquisition unit 210 acquires a plurality of pressure data, a plurality of flow data, an integrated pressure data, and a pressure sensor value and a flow sensor value by periodically acquiring a pressure sensor value and a flow sensor value from a pressure gauge and a flow meter attached to each output line. Obtain integrated flow data. Further, the acquisition unit 210 acquires a plurality of power data by periodically acquiring power values from sensors attached to the plurality of compressors A to C.
  • the plurality of time-series data and attribute information acquired by the acquisition unit 210 are stored in the storage unit 260.
  • a plurality of pressure data, a plurality of flow data, a plurality of power data, integrated pressure data, and integrated flow data are managed in the time-series data DB 262, and attribute information is managed in the attribute information DB 264.
  • the analysis unit 220 analyzes the cross-correlation of the plurality of time-series data acquired by the acquisition unit 210.
  • analysis section 220 performs multiple regression analysis. Specifically, the analysis unit 220 creates a plurality of combinations of pressure data and flow rate data from each of the plurality of compressors A to C, and candidates for attribute information corresponding to each of the pressure data and flow rate data. The analysis unit 220 determines the likelihood of the created combination by performing multiple regression analysis, and determines an optimal solution (optimal combination).
  • the analysis unit 220 performs a multiple regression analysis using a plurality of pressure data and a plurality of flow data constituting the combination as independent variables, and using the integrated pressure data and the integrated flow data as dependent variables.
  • the analysis unit 220 calculates a determination coefficient (that is, an R value) by multiple regression analysis for each combination.
  • the analysis unit 220 determines the combination corresponding to the largest determination coefficient among the plurality of calculated determination coefficients as the optimal combination.
  • the analysis unit 220 outputs information indicating the optimal combination to the correction unit 230.
  • the correction unit 230 corrects the attribute information based on the result of the analysis by the analysis unit 220. Specifically, the correction unit 230 refers to the attribute information DB 264 stored in the storage unit 260, and stores a plurality of pieces of attribute information read from the storage unit 260 and information indicating an optimal combination obtained from the analysis unit 220. To correct attribute information. The correction unit 230 reflects the result of the correction in the attribute information DB 264. The specific operation of the correction by the correction unit 230 will be described later using an example.
  • the detection unit 240 detects an abnormality occurring in the compressors A to C based on the corrected attribute information and the pressure data, the flow rate data, the power data, the integrated pressure data, and the integrated flow rate data. For example, the detection unit 240 detects an abnormality of the compressors A to C based on an abnormality detection model regarding the power efficiency of the compressor.
  • the detection unit 240 refers to the time-series data DB 262, the attribute information DB 264, and the abnormality detection model DB 166 stored in the storage unit 260, and reads necessary data to detect an abnormality.
  • the detection unit 240 outputs a detection result of the abnormality to the output unit 150. The specific operation of the abnormality detection will be described later in detail with an example.
  • the storage unit 260 is realized by, for example, a semiconductor memory or an HDD.
  • the storage unit 260 stores various data and information necessary for executing the abnormality detection method in a database.
  • the storage unit 260 stores a time-series data DB (database) 262, an attribute information DB 264, an abnormality detection model DB 166, and a countermeasure information DB 168.
  • the time series data DB 262 includes a plurality of pressure data, a plurality of flow rate data, integrated pressure data, and integrated flow rate data acquired by the acquisition unit 210.
  • the time-series data DB 262 includes power data for each compressor acquired by the acquisition unit 210.
  • the time-series data DB 262 includes, for each data ID assigned to the time-series data, a numerical value for each time (specifically, a pressure value, a flow rate value, an electric power value, etc.), similarly to the time-series data DB 162 illustrated in FIG. ).
  • the attribute information DB 264 includes a plurality of pieces of attribute information acquired by the acquisition unit 210.
  • the plurality of pieces of attribute information respectively correspond to the plurality of pieces of time-series data.
  • the attribute information DB 264 is generated and updated when the acquisition unit 210 acquires the attribute information.
  • the attribute information DB 264 is updated by the correction unit 230.
  • FIG. 17 is a diagram illustrating an example of attribute information acquired by the abnormality detection device 200 according to the present embodiment. Specifically, FIG. 17 illustrates the attribute information DB 264 stored in the storage unit 260.
  • attribute information is associated with each data ID representing time-series data.
  • the data ID is represented by P1 to P3, V1 to V3, Pt, Vt, and U1 to U3.
  • the attribute information is classified into a plurality of items.
  • the items are an installation room, a device name, and a data type.
  • the installation room and the device name indicate the room where the compressor to which the sensor for measuring the numerical value of the corresponding time-series data is mounted is installed, and the name of the compressor.
  • the type of data is the pressure or flow rate of the compressor.
  • attribute information in which an error has occurred and needs to be corrected is shown in bold letters and the background is shaded with dots.
  • the error is detected by the analysis of the analysis unit 220 and corrected by the correction unit 230.
  • FIG. 18 is a flowchart showing the operation of the abnormality detection device 200 according to the present embodiment.
  • the acquisition unit 210 acquires time-series data and attribute information (S30).
  • the acquisition unit 210 includes, for example, a pressure gauge and a flow meter provided at each output of the plurality of compressors A to C and an output after integration, and sensors provided at the plurality of compressors A to C (specifically, For example, by acquiring a sensor value from a power meter every 10 minutes and storing the sensor value in the storage unit 260, a plurality of time-series data for a certain period such as one day or one week is acquired.
  • the acquiring unit 210 acquires attribute information corresponding to a plurality of time-series data by receiving inputs from managers of the plurality of compressors A to C and the like. Either the acquisition of the time-series data or the acquisition of the attribute information may be performed first, or may be performed simultaneously.
  • the analysis unit 220 creates a plurality of combinations based on the time-series data DB 262 (S31). For example, as shown in FIG. 19, the analysis unit 220 combines each of the three pressure data P1 to P3 and each of the three flow rate data V1 to V3 so as not to overlap, thereby providing six combinations. Create
  • FIG. 19 is a diagram illustrating an example of a combination created by the abnormality detection device 200 according to the present embodiment. Further, it is assumed that the integrated pressure data, the integrated flow rate data, and the power data have no error and need not be corrected. It is also assumed that there is no error between the pressure data and the flow rate data.
  • the combination (P, V) indicates that they correspond to the same compressor.
  • the pressure data P1 and the flow rate data V1 correspond to the first compressor
  • the pressure data P2 and the flow rate data V2 correspond to the second compressor
  • the pressure data P3 and the flow rate data V3. Represents that it corresponds to the third compressor.
  • Pattern-2 to Pattern-6 the combination of Pattern-2 and Pattern-6 in the combination.
  • the analysis unit 220 performs a multiple regression analysis for each created combination (S32). Specifically, for each combination, the analysis unit 220 sets the integrated output (Pt, Vt) as a dependent variable Y, and separates each time-series data (P1, V1), (P2, V2), and (P3, V3) independently. Multiple regression analysis is performed using the variables X1 to X3. Thereby, the analysis unit 220 calculates the determination coefficient (that is, the R value) for each combination.
  • the analysis unit 220 selects an optimal solution, that is, an optimal combination (S33). Specifically, the analysis unit 220 selects a combination of the largest determination coefficient among the plurality of calculated determination coefficients as an optimal combination (that is, a correct combination). In the example shown in FIG. 19, the combination Pattern-1 is selected as the optimum combination.
  • the correction unit 230 corrects the attribute information based on the analysis result (S34). Specifically, the correction unit 230 extracts a group in which the device name (specifically, the name of the compressor) is different for each pair of the pressure data and the flow rate data based on the optimum combination.
  • the device name specifically, the name of the compressor
  • the correction unit 230 determines that at least one of the device names in the set (P3, V3) is incorrect and needs to be corrected.
  • the correction unit 230 determines whether or not inconsistency occurs when one device name of the pressure data and the flow rate data is corrected to the other device name in the set (P3, V3), for example. Specifically, even if the device name of the pressure data P3 is corrected to “compressor C” which is the device name of the flow rate data V3, no inconsistency occurs. On the other hand, when the device name of the flow rate data V3 is corrected to “compressor B” which is the device name of the pressure data P3, the pressure data of the compressor B already exists and contradicts. Therefore, the correction unit 230 corrects the device name of the pressure data P3 to “compressor C” so that no inconsistency occurs.
  • the correction unit 230 corrects both the pressure data and the flow rate data to the name of the compressor that does not exist in the attribute information DB 264 (for example, “compressor C”), It may be determined whether or not a contradiction occurs. Alternatively, the correction unit 230 corrects the attribute information by causing the output unit 150 to output information indicating that the correction has not been correctly performed, and causing the manager of the factory 1 to input the attribute information. Is also good.
  • the detection unit 240 detects an abnormality as shown in FIG. 18 (S35).
  • the detection of the abnormality may be performed immediately after the attribute information is corrected, or may be performed at a predetermined timing. Further, the detection of the abnormality may be periodically repeated.
  • the detection unit 240 reads data from each of the time-series data DB 262, the attribute information DB 264, and the abnormality detection model DB 166 of the storage unit 260 for each abnormality to be detected, and detects an abnormality based on the read data. The presence / absence and the time when an abnormality has occurred are detected.
  • detection section 240 uses an abnormality detection model based on power efficiency A (k) expressed by the following equation (1).
  • a (k) is the power efficiency of the k-th compressor.
  • U (k, t) is the power consumption of the k-th compressor at time t.
  • V (k, t) is the flow rate of the k-th compressor at time t.
  • P (k, t) is the pressure of the k-th compressor at time t.
  • the compressors A to C are the first to third compressors, respectively.
  • the power efficiency A (k) is the reciprocal of the ratio of the product of the pressure P (k, t) and the flow rate V (k, t) to the input power U (k, t). is there.
  • the detection unit 240 calculates the power efficiency of each compressor based on the equation (1), and when the calculated power efficiency exceeds a predetermined threshold, determines that an abnormality has occurred in the compressor. . When the calculated power efficiency is equal to or smaller than the threshold, the detection unit 240 determines that no abnormality has occurred in the compressor.
  • U (k, t), V (k, t) and P (k, t) are all obtained by referring to the time-series data DB 262.
  • the detection unit 240 cannot calculate the power efficiency A (3) for the compressor C, and cannot detect an abnormality.
  • the power efficiency A (3) of the compressor C can be calculated, and it is possible to determine whether the compressor C is abnormal.
  • FIG. 20 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device 200 according to the present embodiment.
  • the horizontal axis represents the pressure P [MPa].
  • the vertical axis represents the power U [kWh].
  • the power efficiency A (k) corresponds to the slope of the plot for each compressor in FIG.
  • Each plot corresponds to pressure data and power data at a predetermined time.
  • the slope of the compressor A increases during the period from 11:20 to 12:40. That is, the power efficiency of the compressor A is degraded. For this reason, the detection unit 240 determines that an abnormality has occurred in the compressor A during the period.
  • the output unit 150 outputs information indicating the detected abnormality and the occurrence time of the abnormality (S36). Further, based on the detected abnormality, the output unit 150 refers to the measure information DB 168 stored in the storage unit 260 to determine a measure for the abnormality, and outputs information indicating the determined measure. I do.
  • FIG. 21 is a diagram illustrating an example of information output by the abnormality detection device 200 according to the present embodiment.
  • the output unit 150 generates an image representing the detected abnormality, the occurrence time of the abnormality, and a countermeasure for the abnormality, and displays the image on a display or the like to manage the compressors A to C. To others.
  • the occurrence of an abnormality such as a decrease in the operation efficiency of the compressor A and the operation of the compressors B and C are displayed as a countermeasure against the abnormality. That is, since the operation efficiency of the compressor A is reduced, a countermeasure for recommending the operation of the compressors B and C, which are alternative devices, is displayed.
  • FIG. 22 is a diagram showing the configuration of the factory 2 to which the abnormality detection device 300 according to the present embodiment is applied.
  • the factory 2 differs from the factory 1 according to the first embodiment in the devices arranged in each room. Specifically, in each of the three rooms A to C, a plurality of devices related to the manufacture of a product are arranged. Specifically, as shown in FIG. 22, in the room A, a mounting machine A1, a coating machine A1, a mounting machine A2, a coating machine A2, and an air conditioner A are arranged. In the room B, a molding machine B, a coating machine B, and an air conditioner B are arranged. In the room C, a press C and an air conditioner C are arranged.
  • the rooms A to C do not have to be physically separated rooms. That is, in the factory 2, the rooms A to C may be included in one large room, or may be a space virtually divided in association with the air conditioner. That is, the rooms A to C are an example of a space in which each of the air conditioners A to C is arranged.
  • the abnormality detection device 300 acquires a plurality of time-series data obtained from each device arranged in each room and generates a plurality of time-series data based on the acquired plurality of time-series data, as in the first embodiment. Detect abnormalities.
  • the abnormality detection device 300 is communicably connected to a sensor or a measuring device provided in each device by wire or wirelessly.
  • FIG. 23 is a block diagram showing a configuration of an abnormality detection device 300 according to the present embodiment.
  • the abnormality detection device 300 is different from the abnormality detection device 100 according to the first embodiment in that the analysis unit 320, the correction unit 130, and the detection unit 140 are replaced with an analysis unit 320 and a correction unit. The difference is that a point 330 and a detecting section 340 are provided.
  • the analysis unit 320 analyzes the cross-correlation of the plurality of time-series data acquired by the acquisition unit 110.
  • analysis section 320 performs multiple regression analysis.
  • analysis unit 320 performs multiple regression analysis using time-series data of power consumption of air conditioners A to C as dependent variables and time-series data of parameters of other devices as independent variables. Based on the result of the multiple regression analysis, an optimal combination of the air conditioners A to C (that is, the rooms A to C) and each device is determined.
  • the correction unit 330 corrects the attribute information based on the result of the multiple regression analysis. Specifically, the correction unit 330 refers to the attribute information DB 164 stored in the storage unit 160, and stores the plurality of pieces of attribute information read from the storage unit 160 and the result of the multiple regression analysis obtained from the analysis unit 320. The attribute information is corrected using the indicated information. For example, the correction unit 330 determines whether there is an error in the correspondence between the installation room and the time-series data in the attribute information DB 164, and if there is an error, corrects the erroneous installation room to a correct installation room. The correction unit 330 reflects the result of the correction in the attribute information DB 264. The specific operation of the correction by the correction unit 330 will be described later with an example.
  • the detection unit 340 detects an abnormality occurring in each device based on the information indicating the corrected installation room and a plurality of time-series data. Further, the detection unit 340 determines a device that can perform an alternative operation instead of the device in which the abnormality has occurred. Specifically, based on the information indicating the corrected installation room, the detection unit 340 determines a device that is installed in the same room as the device in which the abnormality has occurred and that is of the same type as a device that can perform an alternative operation. The detection unit 340 outputs the detection result of the abnormality and the determination result of the device that can perform the alternative operation to the output unit 150. The specific operation of the abnormality detection will be described later in detail with an example.
  • FIG. 24 is a flowchart showing the operation of the abnormality detection device 300 according to the present embodiment.
  • the acquisition unit 110 acquires time-series data and attribute information (S40).
  • the specific acquisition process is the same as step S10 according to the first embodiment.
  • the analysis unit 320 performs a multiple regression analysis using the plurality of time-series data (S41). Specifically, analysis unit 320 performs multiple regression analysis using the time series data of the power consumption of each of air conditioners A to C as a dependent variable and the remaining time series data as independent variables.
  • the analysis unit 320 calculates a coefficient for each independent variable by performing multiple regression analysis using the time-series data Data-21 of the power consumption of the air conditioner A as a dependent variable and the remaining time-series data as independent variables. Similarly, a coefficient for each independent variable is calculated using the time series data Data-22 of the power consumption of the air conditioner B and the time series data Data-23 of the power consumption of the air conditioner C as dependent variables.
  • the analysis unit 320 determines an optimal combination of each device and a room based on the result of the multiple regression analysis (S42). Specifically, the analysis unit 320 determines, for each time-series data (independent variable), the dependent variable when the calculated coefficient is the largest, and determines the dependent variable in the room where the air conditioner corresponding to the determined dependent variable is located. It is determined that a device corresponding to the time-series data is arranged.
  • FIG. 25 is a diagram illustrating an example of a result of a multiple regression analysis performed by the abnormality detection device according to the present embodiment.
  • FIG. 25 shows only four data (Data-01, Data-06, Data-07 and Data-20) out of the 20 time-series data Data-01 to Data-20 excluding the air conditioners A to C. The calculated coefficients are shown.
  • the coefficient at the time-series data Data-21 is the largest. For this reason, it is determined that the device (specifically, the mounting device) corresponding to the time-series data Data-01 is located in the same space as the air conditioner A. In FIG. 25, cells that represent the combination with the largest coefficient are shaded with dots.
  • time-series data and a room are associated with each other as follows.
  • the time-series data located in the same room as the air conditioner A includes time-series data Data-01, Data-02, Data-03, Data-04, Data-05, Data-07, Data-11, Data-11. -13, Data-14 and Data-18.
  • the time-series data located in the same room as the air conditioner B are time-series data Data-06, Data-09, Data-10, Data-16, and Data-19.
  • the time-series data located in the same room as the air conditioner C is time-series data Data-08, Data-12, Data-15, Data-17, and Data-20.
  • the analysis unit 320 may determine a plurality of dependent variables whose calculated coefficients exceed a threshold value for each time-series data constituting the independent variable. That is, the analysis unit 320 may determine a plurality of rooms (air conditioners) corresponding to the time-series data. For example, when the spaces where the air conditioning is controlled by a plurality of air conditioners overlap, the coefficient of the time-series data of the device located in the overlapped space has a large value in each of the plurality of air conditioners.
  • the correction unit 330 corrects the attribute information based on the combination determined in this way (S43).
  • FIG. 26 is a diagram illustrating an example of the correction of the attribute information by the abnormality detection device according to the present embodiment.
  • the correction unit 330 detects an error of the installation room in the attribute information DB 164 based on the combination of the room and the time-series data described above, and corrects the detected error.
  • the installation room corresponding to five time series data Data-07, Data-11, Data-13, Data-14, and Data-18 is “RoomB”. "And is different from the installation room” RoomA "corresponding to the time-series data of the air conditioner A. Therefore, the correction unit 330 corrects the installation room corresponding to the five time-series data Data-07, Data-11, Data-13, Data-14, and Data-18 to the same “RoomA” as the air conditioner A. The same applies to time-series data corresponding to the same room as the air conditioner B and time-series data corresponding to the same room as the air conditioner C.
  • the detection unit 340 detects an abnormality (S44).
  • the detection of the abnormality may be performed immediately after the attribute information is corrected, or may be performed at a predetermined timing. Further, the detection of the abnormality may be periodically repeated.
  • the detection unit 340 reads data from each of the time-series data DB 162, the attribute information DB 164, and the abnormality detection model DB 166 of the storage unit 160 for each abnormality to be detected, and detects an abnormality based on the read data. The presence / absence and the time when an abnormality has occurred are detected.
  • the detection unit 340 uses an abnormality detection model of a mounting machine and a coating machine. For example, in the abnormality detection model of the mounting machine, when the production number of the components produced by the mounting machine is equal to or greater than the threshold, it is determined that no abnormality has occurred in the mounting machine, and when the production number falls below the threshold, , It is determined that an abnormality has occurred in the mounting machine. In addition, in the abnormality detection model of the coating machine, when the number of components to be supplied to the coating machine is equal to or greater than the threshold value, it is determined that no abnormality has occurred in the coating machine, and when the number of input components falls below the threshold value. Then, it is determined that an abnormality has occurred in the coating machine.
  • the detection unit 340 determines at least one of the plurality of mounting machines as a device capable of performing an alternative operation of another mounting machine. In addition, when a plurality of applicators exist in the same room, the detection unit 340 determines at least one of the plurality of applicators as a device capable of performing an alternative operation to another applicator.
  • FIG. 27 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the present embodiment.
  • the horizontal axis represents time.
  • the vertical axis indicates the number of production machines [pieces] and the number of application machines [pieces].
  • FIG. 22 it is assumed that components produced by the mounting machine A1 are supplied to the coating machine A1 and components produced by the mounting machine A2 are supplied to the coating machine A2.
  • the detection unit 340 detects that an abnormality has occurred in the mounting machine A2. As a result of the analysis by the analysis unit 320, it has been found that the mounting machine A2 and the mounting machine A1 are provided in the same room A together with the coating machine A1 and the coating machine A2. For this reason, when an abnormality has occurred in the mounting machine A2, the detection unit 340 determines the mounting machine A1 as a device capable of performing an alternative operation.
  • the supply of components produced by the mounting machine A2 to the coating machine A2 is stopped by stopping the mounting machine A2. Instead, a part of the components produced by the mounting machine A1 is supplied to the coating machine A2. For example, by operating the mounting machine A1 at the maximum capacity, it is possible to produce 1.5 times the number of components in a normal state. Therefore, the coating machine A2 can be supplied with half the number of components from the normal state from the mounting machine A1. The applicator A2 can lower the operation capability as compared with the normal operation and reduce power consumption.
  • the output unit 150 outputs information indicating the detected abnormality and the time of occurrence of the abnormality, and The information indicating the measure is output (S45).
  • FIG. 28 is a diagram showing an example of information output by the abnormality detection device according to the present embodiment. As shown in FIG. 28, as a countermeasure, the maintenance of the abnormally stopped mounting machine A2 and the alternative operation performed by the mounting machine A1 are displayed on a display or the like, as a countermeasure.
  • the manager can perform processing for the abnormality in real time.
  • the administrator can suppress a decrease in production efficiency by causing the mounting machine A1 to perform an alternative operation.
  • the mounting machine A1 to perform an alternative operation.
  • the technician by performing maintenance on the mounting machine A2 during the period when the mounting machine A1 is performing the alternative operation, it is possible to quickly perform a recovery operation while suppressing a decrease in production efficiency.
  • the room in which each device is installed can be correctly determined by analyzing the time-series data, so that not only can abnormalities be detected with high accuracy, but also alternative operations can be performed. Can propose new equipment.
  • FIG. 29 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the comparative example.
  • FIG. 30 is a diagram illustrating an example of information output by the abnormality detection device according to the comparative example.
  • the coating machine A1, the coating machine A2, the mounting machine A1, and the mounting machine A2 are provided in the same room. .
  • the abnormality detection device according to the comparative example the abnormality cannot be accurately detected, and the production efficiency of the manufacturing system may decrease.
  • the detection unit 340 detects an abnormality based on a thermal model of each room by the air conditioner.
  • the process of correcting the attribute information except for the difference in the abnormality detection method is the same as in the third embodiment.
  • the following description focuses on differences from the third embodiment, and description of common points is omitted or simplified.
  • FIG. 31 is a diagram showing a configuration of a thermal model that is an object of the abnormality detection device 300 according to the present modification. Based on the thermal model shown in FIG. 31, heat transfer in the space where the air conditioner is provided is represented by the following equation (2).
  • Iac is the power consumption of the air conditioner.
  • P k is the power consumption of each device provided in the same room as the air conditioner.
  • the term obtained by multiplying the total value ( ⁇ P k ) of the power consumption of each device by the coefficient K corresponds to the total heat generation of the devices provided in the same room as the air conditioner.
  • R corresponds to the thermal resistance of the room provided with the air conditioner.
  • Vr is the room temperature of the room provided with the air conditioner.
  • V o is the outside air temperature.
  • the heat capacity Q is represented by the product of the time derivative value of the coefficient C and V r -V o.
  • Detection unit 340 the dependent variable I ac of the formula (2), and .SIGMA.P k, and (V r -V o), as independent variables and the time differential value of (V r -V o), a multiple regression Perform analysis.
  • the acquisition unit 110 acquires time-series data of power consumption of each air conditioner, time-series data of power consumption of each device, time-series data of outside temperature, and time-series data of room temperature. I do.
  • FIG. 32 is a diagram illustrating an example of an abnormality detection process performed by the abnormality detection device according to the present modification.
  • the horizontal axis represents time.
  • the vertical axis represents the values of K, R, and C coefficients. Each of K, R, and C becomes a value close to 1 when no abnormality occurs.
  • the detection unit 340 compares each of K, R, and C with a threshold, and determines that an abnormality has occurred in the thermal environment of the modeled room when at least one of K, R, and C is below the threshold. I do.
  • the threshold value is, for example, 0.800, but is not limited to this.
  • the thermal resistance R decreases during the period from 14:00 to 15:00.
  • the countermeasure information DB 168 is associated with a countermeasure for checking a window and a door of a room for an abnormality such as a decrease in thermal resistance.
  • FIG. 33 is a diagram illustrating an example of information output by the abnormality detection device according to the present modification.
  • the abnormality detection method is executed by the abnormality detection device 100, 200, or 300 realized by one computer device.
  • the abnormality detection method according to the present embodiment is executed by distributed processing by a plurality of devices.
  • FIG. 34 is a block diagram showing a configuration of an abnormality detection system 400 that executes the abnormality detection method according to the present embodiment.
  • the abnormality detection system 400 includes an information processing device 401, a data storage device 402, and an information presentation device 403.
  • each device included in the abnormality detection system 400 is provided between the network 404 and each manufacturing device, that is, inside the network 404.
  • the network 404 is a communication network for connecting to an external device such as the Internet, for example.
  • the abnormality detection method according to the present embodiment may be executed by abnormality detection system 410 shown in FIG.
  • FIG. 35 is a block diagram showing a configuration of an abnormality detection system 410 that executes the abnormality detection method according to the present embodiment.
  • the abnormality detection system 410 includes an information processing device 401, a data storage device 402, and an information presentation device 403. Each device included in the abnormality detection system 410 is provided outside the network 404.
  • each device included in the abnormality detection system 400 or 410 may be provided inside or outside the network 404.
  • the information processing device 401 may be provided inside the network 404, and the data storage device 402 and the information presentation device 403 may be provided outside the network 404.
  • the data storage device 402 may be provided inside the network 404, and the information processing device 401 and the information presentation device 403 may be provided outside the network 404.
  • only the information presentation device 403 may be provided inside the network 404, and the information processing device 401 and the data storage device 402 may be provided outside the network 404.
  • FIG. 36 is a block diagram showing a configuration of an abnormality detection system 400 according to the present embodiment.
  • the configuration of the abnormality detection system 410 is the same as that of the abnormality detection system 400.
  • the information processing apparatus 401 includes an acquisition unit 110, an analysis unit 120, a correction unit 130, a detection unit 140, and an output unit 150.
  • the information processing device 401 is, for example, a computer device.
  • the information processing device 401 is realized by a nonvolatile memory storing a program, a volatile memory serving as a temporary storage area for executing the program, an input / output port, a processor executing the program, and the like.
  • the data storage device 402 is a storage device that stores the time-series data DB 162, the attribute information DB 164, the abnormality detection model DB 166, and the countermeasure information DB 168.
  • the data storage device 402 is realized by an HDD, a semiconductor memory, or the like.
  • the information presenting device 403 is a device that presents information output from the output unit 150 to a user such as a factory manager.
  • the information presentation device 403 is, for example, a display or a speaker.
  • the abnormality detection method is distributed by a plurality of devices.
  • the processing amount in each device is reduced, and processing can be performed in a short period of time by distributed processing.
  • each of the information processing device 401 and the data storage device 402 may not be a single device, and may be implemented by a plurality of devices.
  • a correction candidate may be presented to a user, and the user (administrator) may make a correction.
  • the abnormality detection device may include an input interface that receives a correction instruction from a user.
  • the detection unit may determine the type of the abnormality by clustering the detected abnormality. By determining the type of the abnormality, the accuracy of estimating the cause of the abnormality and selecting a measure for the abnormality can be further improved.
  • the analysis may be an analysis method other than the statistical analysis. Any analysis method may be used as long as the mutual relationship between a plurality of time-series data can be determined.
  • the communication method between the devices described in the above embodiment is not particularly limited.
  • the wireless communication method is, for example, ZigBee (registered trademark), Bluetooth (registered trademark), or short-range wireless communication such as wireless LAN (Local Area Network).
  • the wireless communication method may be communication via a wide area communication network such as the Internet. Wired communication may be performed between the devices instead of wireless communication.
  • the wired communication is power line communication (PLC) or communication using a wired LAN.
  • another processing unit may execute the process executed by the specific processing unit. Further, the order of the plurality of processes may be changed, or the plurality of processes may be executed in parallel.
  • the distribution of the components included in the abnormality detection system to a plurality of devices is an example. For example, components included in one device may be included in another device. Further, the abnormality detection system may be realized as a single device.
  • the processing described in the above embodiment may be realized by centralized processing using a single device (system), or may be realized by distributed processing using a plurality of devices. Good.
  • the number of processors that execute the program may be one or more. That is, centralized processing or distributed processing may be performed.
  • all or a part of the components such as the control unit may be configured by dedicated hardware, or may be realized by executing a software program suitable for each component. Is also good.
  • Each component may be realized by a program execution unit such as a CPU (Central Processing Unit) or a processor reading and executing a software program recorded on a recording medium such as an HDD (Hard Disk Drive) or a semiconductor memory. Good.
  • a program execution unit such as a CPU (Central Processing Unit) or a processor reading and executing a software program recorded on a recording medium such as an HDD (Hard Disk Drive) or a semiconductor memory. Good.
  • the components such as the control unit may be configured by one or a plurality of electronic circuits.
  • Each of the one or more electronic circuits may be a general-purpose circuit or a dedicated circuit.
  • the one or more electronic circuits may include, for example, a semiconductor device, an IC (Integrated Circuit), an LSI (Large Scale Integration), and the like.
  • the IC or LSI may be integrated on one chip, or may be integrated on a plurality of chips.
  • the term is referred to as an IC or an LSI, but the term varies depending on the degree of integration, and may be called a system LSI, a VLSI (Very Large Scale Integration), or a ULSI (Ultra Large Scale Integration).
  • an FPGA Field Programmable Gate Array programmed after manufacturing the LSI can be used for the same purpose.
  • general or specific aspects of the present disclosure may be realized by a system, an apparatus, a method, an integrated circuit, or a computer program.
  • the present invention may be realized by a non-transitory computer-readable recording medium such as an optical disk, an HDD, or a semiconductor memory in which the computer program is stored.
  • the present invention may be realized by any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.
  • the present disclosure can be used as an abnormality detection method that can detect an abnormality with high accuracy, and can be used, for example, for manufacturing management and maintenance in a factory or the like.

Landscapes

  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Automation & Control Theory (AREA)
  • Manufacturing & Machinery (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

Un procédé de détection d'anomalie selon un aspect de la présente invention comprend : une étape (S10) consistant à acquérir une pluralité d'éléments de données chronologiques obtenues à partir d'une pluralité d'éléments se rapportant à la fabrication d'un objet fabriqué, et des informations d'attribut pour chaque élément de la pluralité d'éléments de données chronologiques; une étape (S11) consistant à analyser des corrélations entre la pluralité d'éléments de données chronologiques; une étape (S12) consistant à corriger des informations d'attribut sur la base des résultats de l'analyse; une étape (S13) consistant à détecter une anomalie se produisant dans la pluralité d'éléments, sur la base des informations d'attribut corrigées et de la pluralité d'éléments de données chronologiques; et une étape (S14) consistant délivrer des informations indiquant l'anomalie détectée et le moment auquel l'anomalie s'est produite.
PCT/JP2019/030043 2018-08-03 2019-07-31 Procédé de détection d'anomalie, dispositif de traitement d'informations et système de détection d'anomalie WO2020027207A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2018147075 2018-08-03
JP2018-147075 2018-08-03

Publications (1)

Publication Number Publication Date
WO2020027207A1 true WO2020027207A1 (fr) 2020-02-06

Family

ID=69231100

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2019/030043 WO2020027207A1 (fr) 2018-08-03 2019-07-31 Procédé de détection d'anomalie, dispositif de traitement d'informations et système de détection d'anomalie

Country Status (1)

Country Link
WO (1) WO2020027207A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112257755A (zh) * 2020-09-24 2021-01-22 北京航天测控技术有限公司 航天器运行状态的分析方法和装置
CN114169631A (zh) * 2021-12-15 2022-03-11 中国石油大学胜利学院 一种基于数据分析的油田电力负荷管控系统
TWI765430B (zh) * 2020-11-24 2022-05-21 思維環境科技有限公司 用於環境感測裝置之巡檢機制優化方法及其系統

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09189579A (ja) * 1996-01-11 1997-07-22 Toshiba Corp 監視データ収集装置
JP2007148890A (ja) * 2005-11-29 2007-06-14 Hitachi Ltd 機器診断装置
JP2010501091A (ja) * 2006-05-07 2010-01-14 アプライド マテリアルズ インコーポレイテッド 故障診断のための範囲を定めた故障サイン
WO2011039823A1 (fr) * 2009-10-02 2011-04-07 株式会社日立製作所 Equipement de diagnostic d'installation
JP2012164109A (ja) * 2011-02-07 2012-08-30 Hitachi Ltd センシング装置
JP2015088154A (ja) * 2014-02-10 2015-05-07 株式会社日立パワーソリューションズ ヘルスマネージメントシステム及びヘルスマネージメント方法
WO2017164095A1 (fr) * 2016-03-23 2017-09-28 日本電気株式会社 Dispositif d'analyse de facteurs, procédé d'analyse de facteurs et support de stockage sur lequel est stocké un programme

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09189579A (ja) * 1996-01-11 1997-07-22 Toshiba Corp 監視データ収集装置
JP2007148890A (ja) * 2005-11-29 2007-06-14 Hitachi Ltd 機器診断装置
JP2010501091A (ja) * 2006-05-07 2010-01-14 アプライド マテリアルズ インコーポレイテッド 故障診断のための範囲を定めた故障サイン
WO2011039823A1 (fr) * 2009-10-02 2011-04-07 株式会社日立製作所 Equipement de diagnostic d'installation
JP2012164109A (ja) * 2011-02-07 2012-08-30 Hitachi Ltd センシング装置
JP2015088154A (ja) * 2014-02-10 2015-05-07 株式会社日立パワーソリューションズ ヘルスマネージメントシステム及びヘルスマネージメント方法
WO2017164095A1 (fr) * 2016-03-23 2017-09-28 日本電気株式会社 Dispositif d'analyse de facteurs, procédé d'analyse de facteurs et support de stockage sur lequel est stocké un programme

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112257755A (zh) * 2020-09-24 2021-01-22 北京航天测控技术有限公司 航天器运行状态的分析方法和装置
CN112257755B (zh) * 2020-09-24 2023-07-28 北京航天测控技术有限公司 航天器运行状态的分析方法和装置
TWI765430B (zh) * 2020-11-24 2022-05-21 思維環境科技有限公司 用於環境感測裝置之巡檢機制優化方法及其系統
CN114169631A (zh) * 2021-12-15 2022-03-11 中国石油大学胜利学院 一种基于数据分析的油田电力负荷管控系统
CN114169631B (zh) * 2021-12-15 2022-10-25 山东石油化工学院 一种基于数据分析的油田电力负荷管控系统

Similar Documents

Publication Publication Date Title
TWI632443B (zh) 異常資料的重要度判定裝置以及異常資料的重要度判定方法
US11531688B2 (en) Time-series data processing device, time-series data processing system, and time-series data processing method
WO2020027207A1 (fr) Procédé de détection d'anomalie, dispositif de traitement d'informations et système de détection d'anomalie
JP4207915B2 (ja) 品質変動表示装置、品質変動表示方法、品質変動表示プログラム及び該プログラムを記録した記録媒体
JP6141235B2 (ja) 時系列データにおける異常を検出する方法
JP6856443B2 (ja) 設備機器の異常診断システム
US20090271150A1 (en) System, method and algorithm for data-driven equipment performance monitoring
US20100258246A1 (en) Plasma Processing System
CN107710089B (zh) 工厂设备诊断装置以及工厂设备诊断方法
TWI662424B (zh) 領先輔助參數的選擇方法以及結合關鍵參數及領先輔助參數進行設備維護預診斷的方法
JP6702297B2 (ja) プロセスの異常状態診断方法および異常状態診断装置
CN105793789A (zh) 用于过程单元中的全部过程区段的自动的监视和状态确定的计算机实现的方法和系统
GB2515115A (en) Early Warning and Prevention System
WO2020166236A1 (fr) Procédé d'évaluation d'efficacité de travail, dispositif d'évaluation d'efficacité de travail et programme
WO2022212545A1 (fr) Systèmes et procédés pour analyser des alarmes pour traiter des problèmes de systèmes électriques
JP7026012B2 (ja) 機器状態監視システム及び機器状態監視方法
KR20190060548A (ko) 변수 구간별 불량 발생 지수를 도출하여 공정 불량 원인을 파악하고 시각화하는 방법
JP6312955B1 (ja) 品質分析装置及び品質分析方法
JP2011054804A (ja) 半導体製造装置の管理方法およびシステム
JPWO2019049521A1 (ja) リスク評価装置、リスク評価システム、リスク評価方法、及び、リスク評価プログラム
KR101977214B1 (ko) 이상치 탐지 방법, 이를 이용하는 장치 및 시스템
JP6290777B2 (ja) データ関連情報処理装置及びプログラム
JP5948998B2 (ja) 異常診断装置
WO2020204043A1 (fr) Dispositif d'évaluation d'anomalie de haut fourneau, procédé d'évaluation d'anomalie de haut fourneau et procédé de fonctionnement de haut fourneau
WO2021015094A1 (fr) Dispositif d'estimation de facteur de défaillance et procédé d'estimation de facteur de défaillance

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19844720

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19844720

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP