WO2017149598A1 - Dispositif de classification d'appareil - Google Patents

Dispositif de classification d'appareil Download PDF

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Publication number
WO2017149598A1
WO2017149598A1 PCT/JP2016/056050 JP2016056050W WO2017149598A1 WO 2017149598 A1 WO2017149598 A1 WO 2017149598A1 JP 2016056050 W JP2016056050 W JP 2016056050W WO 2017149598 A1 WO2017149598 A1 WO 2017149598A1
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Prior art keywords
data
classification
equipment
unit
devices
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PCT/JP2016/056050
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English (en)
Japanese (ja)
Inventor
泰弘 遠山
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三菱電機株式会社
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Application filed by 三菱電機株式会社 filed Critical 三菱電機株式会社
Priority to PCT/JP2016/056050 priority Critical patent/WO2017149598A1/fr
Priority to CN201680082718.7A priority patent/CN108700872B/zh
Priority to JP2017541413A priority patent/JP6366852B2/ja
Priority to TW105124046A priority patent/TWI621951B/zh
Publication of WO2017149598A1 publication Critical patent/WO2017149598A1/fr

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    • 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 invention relates to a device classification apparatus that classifies facilities in units of devices constituting the facilities.
  • the index used for classification is elevator operation information, elevator use, and scale.
  • the classification accuracy can be improved by classifying a lot of information and other information.
  • Such information is not necessarily quantitative data consisting of numerical values, but may be qualitative data including character information.
  • evaluation of how similar the different qualitative data are not considered. As a result, there is a problem that the cause analysis of the abnormality cannot be performed sufficiently, leading to a decrease in abnormality detection accuracy.
  • the present invention has been made to solve such a problem, and an object of the present invention is to provide a device classification apparatus that can accurately analyze a failure or abnormality of a device.
  • a device classification apparatus is a data acquisition unit that acquires device classification index data that is information unique to each device obtained from monitoring data of each device in a plurality of facilities each composed of a single device or a plurality of devices
  • a classification index quantification unit that converts qualitative data included in the equipment classification index data into quantitative data that indicates the degree of similarity between the qualitative data, and equipment that classifies equipment in units of equipment using the quantitative data
  • a classification unit that converts qualitative data included in the equipment classification index data into quantitative data that indicates the degree of similarity between the qualitative data, and equipment that classifies equipment in units of equipment using the quantitative data.
  • the device classification device converts the qualitative data included in the device classification index data into quantitative data indicating the similarity between the qualitative data, and uses this quantitative data to classify the equipment in units of devices. It is what you do. Thereby, it is possible to accurately analyze the failure or abnormality of the device.
  • FIGS. 7A, 7B, and 7C are explanatory diagrams illustrating examples of prior information of the device classification apparatus according to Embodiment 1 of the present invention. It is explanatory drawing which shows the example of classification
  • FIG. 1 is a configuration diagram of a monitoring system including a device classification device 100 according to the present embodiment.
  • the device classification device 100 is connected to a data collection management device 200, and the data collection management device 200 is connected to a monitoring target 400 via a network 300.
  • the device classification apparatus 100 includes a data acquisition unit 101, a classification index quantification unit 102, and a device classification unit 103.
  • the data acquisition unit 101 is a processing unit that acquires device classification index data from the device classification index database 201 managed by the data collection management device 200.
  • the classification index quantification unit 102 is a processing unit that converts qualitative data included in the device classification index data into quantitative data.
  • the device classification unit 103 is a processing unit that classifies facilities in units of devices using the quantitative data generated by the classification index quantification unit 102.
  • the data collection management device 200 is a device that collects monitoring data from the monitoring target 400 and accumulates and manages it as the device classification index database 201.
  • the monitoring data accumulated in the device classification index database 201 is obtained directly or indirectly from the monitoring target 400, such as data (for example, maintenance performance data) created by inspections of maintenance personnel on the monitoring target 400 and facility information.
  • FIG. 2 shows an example of maintenance result data taking an elevator as an example of the equipment classification index data stored in the equipment classification index database 201.
  • FIG. 2 shows an example of maintenance performance data obtained from maintenance personnel inspections and equipment information for one piece of equipment in one piece of equipment.
  • the maintenance result data example as an example of data items, facility ID, model ID, device ID, installation area, worker name, maintenance work content, presence / absence of abnormality, etc. are described.
  • the values of these data items are examples.
  • the data item can be changed in order to store an item of maintenance result data collected from actual facilities and equipment.
  • data of a plurality of facilities and devices may be aggregated into a single table.
  • the data of one device of one facility may be divided into a plurality of tables.
  • the device classification index data stored in the device classification index database 201 may be any information as long as it is information unique to the device.
  • the monitoring target 400 is a facility composed of a single device or a plurality of devices such as an elevator and an air conditioner.
  • the monitoring target 400 assumes that there are two or more facilities composed of similar devices.
  • a configuration in which the monitoring target 400 and the data collection management device 200 are directly connected without connecting to the network 300 may be used. Regardless of the connection method between the monitoring target 400 and the data collection management device 200, the data collection management device 200 and the device classification device 100 may be connected via a network.
  • FIG. 3 is a block diagram illustrating a hardware configuration for realizing the device classification apparatus according to the present embodiment.
  • FIG. 3 shows an example in which the device classification device 100 and the data collection management device 200 of FIG. 1 are configured on a single piece of hardware.
  • the device classification device 100 and the data collection management device 200 include a processor 11, a memory 12, a communication I / F (interface) device 13, a storage 14, and an output device 15.
  • the processor 11 is a processor for realizing the functions of the device classification device 100 and the data collection management device 200.
  • the memory 12 is used as a program memory for storing various programs corresponding to the functions of the device classification device 100 and the data collection management device 200, a work memory used when the processor 11 performs data processing, a memory for developing signal data, and the like.
  • a storage unit such as a ROM and a RAM.
  • the communication I / F device 13 is a communication interface with the outside such as the network 300.
  • the storage 14 is a storage device for storing various data and programs.
  • the output device 15 is a device for outputting processing results to the outside.
  • Data accumulated in the device classification index database 201 is stored in the storage 14 from the monitoring target 400 via the network 300 through the communication I / F device 13.
  • the processing result of the device classification unit 103 is stored in the storage 14 as necessary and output to the outside by the output device 15.
  • the device classification device 100 and the data collection management device 200 may be configured on different hardware.
  • the data collection management device 200 continuously or intermittently inputs the device classification index data obtained from the monitoring target 400 to the device classification index database 201.
  • the device classification device 100 acquires device classification index data from the device classification index database 201 and performs processing.
  • FIG. 4 is a flowchart showing processing of the device classification apparatus 100.
  • the data acquisition unit 101 acquires device classification index data from the device classification index database 201 (step ST1).
  • the flow of FIG. 4 is executed for each data item. For example, when a device ID is input as an index of device classification index data, a list of classified device IDs is output.
  • the format of the list is not limited, but as an example, there is an output in a table format in which a classification ID is assigned to each classification and each device ID and the corresponding classification ID is stored in one line. As another example of the list, there is a method in which one file is created for each category and the device IDs belonging to the category are stored in the file.
  • the classification index quantification unit 102 converts the qualitative data included in the device classification index data obtained from each device into quantitative data composed of numerical values in a format in which similarity can be determined.
  • step ST2 it is determined whether or not the input device classification index data is quantitative data, and the subsequent processing is branched. If it is quantitative data in step ST2 (step ST2: YES), the classification index quantification unit 102 ends the process. That is, the device classification index data input to the classification index quantification unit 102 is output to the device classification unit 103 as it is. On the other hand, when it is not quantitative data in step ST2 (step ST2: NO), the process of step ST3 is executed.
  • the distance between the qualitative data is calculated as the similarity between the qualitative data, and a value corresponding to the distance is assigned to each data to obtain quantitative data.
  • the distance between qualitative data is calculated by a character string analysis method such as n-gram hierarchical cluster analysis, and a numerical value corresponding to the distance is used as quantitative data.
  • the qualitative data represents a classification with a larger group as the character in the front, so the influence on the distance is large, and the character with a smaller group in the rear character has a small effect on the distance.
  • processing such as weighting may be performed on a character having a large influence on the distance when calculating the distance. For example, in the case of a device ID character string where the first half represents a major update version number and the second half represents a minor update version number, the influence on the distance may be greater as the previous character string.
  • FIG. 5 shows an example of quantitative data obtained by converting qualitative data.
  • FIG. 5 shows, as a simple example, quantitative data examples in which the name of the device ID is expressed by connecting the major update version number and the minor update version number of the device with a hyphen symbol “-”.
  • devices whose device IDs are AAA-01, AAA-02, and AAA-03 have the same major update version number and differ only in the minor update version number, so close values are assigned.
  • the device IDs BBB-01 and BBB-02 have different major update version numbers from AAA-01, AAA-02, and AAA-03, and therefore are assigned distant values.
  • the device classification unit 103 uses the multivariate analysis method, the machine learning method, or the like to input the input value, that is, the multivalue data. Classification is performed for each device having a similar feature amount in variable analysis or the like (step ST4). A specific classification example will be described later.
  • the similarity between qualitative data is known as prior information
  • the similarity of prior information may be applied.
  • advance information you may specify only about a part of qualitative data. For example, only the similarity of the major update number is specified in the device ID.
  • a weighting rule for each character position of qualitative data may be specified. For example, the weight ratio between the major update number and the minor update number in the device ID is specified.
  • qualitative data that is not converted into quantitative data may be given as prior information.
  • FIG. 6 shows the classification index quantification flow when there is prior information.
  • the classification index quantification unit 102 determines the similarity in advance regarding the input device classification index data. It is determined whether there is information, and the subsequent processing is branched (step ST5). If there is no prior information on similarity in step ST5 (step ST5: NO), the process of step ST3 is performed in the same manner as the flow of FIG.
  • step ST6 When there is prior information of similarity (step ST5: YES), a numerical value corresponding to the similarity of the given prior information is assigned in the process of step ST6.
  • the similarity of prior information is not qualitative data but qualitative data
  • step ST3 the distance between qualitative data is calculated as the similarity between qualitative data, and according to the distance.
  • Quantitative data is obtained by assigning values to each data.
  • the distance between the qualitative data is calculated by a method for calculating the distance between words such as n-gram hierarchical cluster analysis, and a numerical value corresponding to the distance is used as quantitative data.
  • a method for calculating the distance between qualitative data a method different from step ST3 may be used.
  • FIG. 7 shows an example of advance information.
  • An example of designating the qualitative data similarity is shown in FIG. 7A.
  • FIG. 7A shows an example of designating the similarity of the three characters in front of the device ID.
  • Devices with device IDs AAA and BBB have relatively high similarities
  • devices with device ID CCC have device IDs AAA and BBB. This indicates that the degree of similarity is relatively low.
  • FIG. 7B shows an example in which a weighting rule for each character position of qualitative data is designated.
  • FIG. 7B is an example of a weighting rule for each character position of the device ID.
  • the weight is set to 10 to increase the weight of the first to third characters of the device ID, and the weight of the fifth to sixth characters is lighter than the first to third characters. Therefore, it is an example in the case of setting to 1.
  • FIG. 7C shows an example of specifying qualitative data that is not converted to quantitative data.
  • FIG. 7C shows a case where the equipment ID is not quantified.
  • FIG. 7A, FIG. 7B, and FIG. 7C are examples of information to be specified, and the way of giving information may be changed.
  • free text written as a result of equipment maintenance work may be used as equipment classification index data.
  • words such as “abnormal”, “completed”, “cause is event A” included in the free text described as a result of maintenance work are extracted by morphological analysis, and close to text with many similar morphemes For example, assigning numerical values.
  • the quantitative data converted by the classification index quantification unit 102 is input for a plurality of devices, and each device whose quantitative data has a similar value is input.
  • Classify into As quantitative data, only one data item may be input, or a plurality of data items may be input collectively.
  • a general multivariate analysis method such as a hierarchical cluster analysis such as a dendrogram or a non-hierarchical cluster analysis such as a k-means method, or a general machine such as a support vector machine.
  • a learning method may be used.
  • An example of classification is shown in FIG.
  • FIG. 8 shows a feature amount as a feature amount space when multi-variate analysis methods such as principal component analysis are performed by inputting quantitative data of three devices for a plurality of data items.
  • 1 and feature quantity 2 are schematically displayed on a two-dimensional scatter diagram.
  • FIG. 8 shows that the feature value 801 and the feature value 802 are collected as one classification 804 because the distance on the scatter diagram is short.
  • the feature value 803 indicates that the classification 805 is different from the classification 804 because the distance on the scatter diagram is far from the feature value 801 and the feature value 802.
  • the distance between the feature value values 801, 802, and 803 is calculated, the nearest neighbor method for classifying by the threshold of the distance, the k-means method for determining the number of classifications in advance, etc.
  • a general cluster analysis method may be used.
  • One application of the present invention is device failure / abnormality analysis. For example, when predicting the time of future failure in order to create a maintenance plan for equipment, the probability (failure risk) of future failure will be predicted from statistical failure frequency and deterioration tendency from data obtained from the equipment. However, there is a method for estimating when maintenance is necessary. Here, since devices having similar features are likely to have similar failure probabilities and deterioration trends, it is useful to classify devices according to similar features to predict failure risk. . Classification for each similar device leads to an improvement in failure risk prediction accuracy.
  • the data used for calculating the failure risk may be the same as the data used in the device classification apparatus 100 of the present embodiment, or other data may be used.
  • Embodiment 1 As an example of the feature amount for each device, for two devices, device 1 and device 2, data is collected from three facilities of facility a, facility b, and facility c, and the feature amount is calculated. It is expressed on a two-dimensional scatter diagram created from feature quantities.
  • the equipment feature quantity of the device 1 is shown as 901
  • the equipment feature quantity of the equipment 2 is shown as 902.
  • equipment a, equipment b, and equipment c are classified as equipment having the same characteristics due to the classification of equipment units, they are classified as the same regardless of the characteristics of equipment 1 and equipment 2.
  • the equipment a and equipment b are classified into one classification and the equipment c is classified into another classification.
  • the equipment c can be classified in units of equipment, for example, the equipment c is classified as one classification and the equipment b is classified as another classification.
  • each device By classifying each device with the same characteristics, it is possible to improve the accuracy of device failure risk prediction and failure / abnormality detection. Also, by classifying similar devices, when a failure / abnormality is found in a certain device, it is possible to prevent failure / abnormality in other devices by extracting and maintaining devices with the same characteristics. Maintenance efficiency can be expected by scheduling maintenance work for each device. For example, when confinement occurs due to a decrease in the torque of the door opening / closing motor of elevator A, checking and maintaining the other door opening / closing motors of other elevators with the same characteristics for signs of a torque decrease, It can be expected to reduce.
  • the device is information unique to each device obtained from monitoring data of each device in a plurality of facilities each composed of a single device or a plurality of devices.
  • the device classification unit 103 performs the classification for each device based on the device classification index data quantified by the classification index quantification unit 102.
  • the device classification index data quantified by the classification index quantification unit 102 is converted into a feature amount in order to emphasize the difference in the features of each device before being input to the device classification unit 103, and the device classification The unit 103 may classify each device based on the feature amount, which will be described as a second embodiment.
  • the purpose of converting to feature values is to clarify the differences between devices when devices are classified from multiple device classification index data.
  • device classification is performed from only one device classification index data, similar values are assigned to similar device classification index data when converted to quantitative data, so classification is possible only by the value of the device classification index data.
  • device classification is performed from a plurality of device classification index data, even if one device classification index data is a device with a similar value, another device classification index data may have a far value. In such a case, since the difference between devices cannot be clearly understood if the value of the device classification index data is used as it is, the device cannot be correctly classified.
  • the feature amount may be a general method such as a multivariate analysis method such as each principal component in principal component analysis, a regression coefficient and error in regression analysis, or a similarity in pattern matching method.
  • FIG. 10 is a configuration diagram of a monitoring system to which the device classification apparatus 100a according to the second embodiment is applied.
  • the device classification apparatus 100a according to the second embodiment includes a data acquisition unit 101, a classification index quantification unit 102, a device classification unit 103a, and a feature amount conversion unit 104.
  • the data acquisition unit 101 and the classification index quantification unit 102 are the same as those in the first embodiment.
  • the feature amount conversion unit 104 is a processing unit that converts the device classification index data quantified by the classification index quantification unit 102 into feature amounts.
  • the device classification unit 103 a is a processing unit that classifies devices using the feature values converted by the feature value conversion unit 104.
  • the data collection management device 200, the network 300, and the monitoring target 400 are the same as those in the first embodiment shown in FIG.
  • the feature amount conversion unit 104 converts the quantitative data into the feature amount. Convert.
  • the device classification unit 103a acquires the feature amount from the feature amount conversion unit 104, and classifies devices having similar feature value values as devices having similar features.
  • the feature amount conversion unit 104 may convert only a part of the quantitative data generated by the classification index quantification unit 102 instead of converting it into feature amounts. When only a part is converted, the device classification unit 103a performs classification using both the converted feature quantity and quantitative data.
  • device classification is information unique to each device obtained from monitoring data of each device in a plurality of facilities each composed of a single device or a plurality of devices.
  • a data acquisition unit that acquires index data, a classification index quantification unit that converts qualitative data included in device classification index data into quantitative data that indicates the degree of similarity between qualitative data, and quantitative data Since it has a feature value conversion unit that converts the features of the devices into feature values and a device classification unit that classifies the equipment with similar features as devices with similar features. Abnormality and the like can be analyzed with higher accuracy.
  • the equipment classification device classifies each equipment for each equipment possessed by a plurality of equipment, and the same kind of equipment such as an elevator or an air conditioner is in a different environment. It is suitable for use with multiple facilities.

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Abstract

La présente invention a trait à un dispositif de classification d'appareil qui comporte une unité d'acquisition de données (101) obtenant des données d'index de classification d'appareil à partir d'une base de données d'index de classification d'appareil (201). Une unité de quantification d'index de classification (102) convertit des données qualitatives incluses dans les données d'index de classification d'appareil en données quantitatives indiquant le degré de similitude entre des données qualitatives. Une unité de classification d'appareil (103) utilise les données quantitatives pour classer un équipement par unité d'appareil.
PCT/JP2016/056050 2016-02-29 2016-02-29 Dispositif de classification d'appareil WO2017149598A1 (fr)

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PCT/JP2016/056050 WO2017149598A1 (fr) 2016-02-29 2016-02-29 Dispositif de classification d'appareil
CN201680082718.7A CN108700872B (zh) 2016-02-29 2016-02-29 机器分类装置
JP2017541413A JP6366852B2 (ja) 2016-02-29 2016-02-29 機器分類装置
TW105124046A TWI621951B (zh) 2016-02-29 2016-07-29 Machine sorting device

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JP2019028931A (ja) * 2017-08-03 2019-02-21 日立アプライアンス株式会社 異常検知方法および異常検知システム
WO2019077656A1 (fr) * 2017-10-16 2019-04-25 富士通株式会社 Dispositif, procédé et programme de surveillance d'installations de production
JPWO2019077656A1 (ja) * 2017-10-16 2020-07-30 富士通株式会社 生産設備監視装置、生産設備監視方法及び生産設備監視プログラム
US11650579B2 (en) 2017-10-16 2023-05-16 Fujitsu Limited Information processing device, production facility monitoring method, and computer-readable recording medium recording production facility monitoring program

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