JP7358755B2 - Diagnostic device, diagnostic method, and diagnostic program - Google Patents

Description

The present invention relates to a diagnostic device, a diagnostic method, and a diagnostic program.

In processing equipment that processes objects to be processed, methods are already known in which physical quantities such as information on the current value of the machine's motor, vibration, or force are detected and output in order to detect abnormalities in the equipment ( Patent Document 1).

However, the technology described in Patent Document 1 is not able to efficiently store acquired data and provide data in a format that is easy for users to use, such as how to communicate detected abnormalities to users. do not have.

The present invention has been made in view of the above-mentioned problems, and provides a diagnostic device, a diagnostic method, and a diagnostic program that efficiently accumulate acquired data and provide the data in a form that is easy for users to use. The purpose is to

In order to solve the above-mentioned problems and achieve the purpose, the present invention acquires detection information output from a detection unit that detects a physical quantity that changes according to the operation of a target device that repeats a cycle including multiple processing steps. a first acquisition unit that acquires from the target device a signal indicating an interval in which the target device is to be operated; a data extraction unit that determines a predetermined section corresponding to a monitoring target processing step set by a user among the plurality of processing steps in the step and extracts the detection information acquired in the predetermined section; a score calculation unit that calculates an abnormality score that is a score value in which abnormalities in the detection information are accumulated based on the detection information; and a secondary storage device that stores at least one of the detection information and the abnormality score in the predetermined section. a data storage unit that stores data in at least one of a cloud server; and an output control unit that causes an output device to output at least one of the accumulated detection information and the abnormality score of the predetermined section, and the data extraction unit The unit determines whether or not the detection information acquired by the first acquisition unit includes a time for performing processing, and when it is determined that the detection information includes the time for performing processing. , determine whether or not the processing execution time corresponds to a processing step to be monitored set by the user; When it is determined that it is time to perform processing corresponding to the processing step, the first acquisition unit extracts the acquired detection information, and the data storage unit accumulates the detection information of the predetermined section. In this case, the detection information extracted by the data extraction unit is stored in at least one of the secondary storage device and the cloud server.

According to the present invention, it is possible to efficiently accumulate acquired data and provide the data in a form that is easy for users to use.

FIG. 1 is a diagram schematically showing the configuration of a diagnostic system according to an embodiment. FIG. 2 is a diagram showing, in functional blocks, an example of the configuration of the processing machine and the diagnostic device according to the embodiment. FIG. 3 is a diagram showing an example of the hardware configuration of the processing machine according to the embodiment. FIG. 4 is a diagram showing an example of the hardware configuration of the diagnostic device according to the embodiment. FIG. 5 is a diagram showing an example of detection information and a ladder signal of the processing machine according to the embodiment. FIG. 6 is a diagram schematically illustrating feature information extracted from detection information by the diagnostic device according to the embodiment using frequency components. FIG. 7 is a flow diagram illustrating an example of a procedure of processing related to data acquisition in the diagnostic device according to the embodiment. FIG. 8 is a diagram illustrating a tool setting operation in the diagnostic device according to the embodiment. FIG. 9 is a diagram illustrating a tool setting operation in the diagnostic device according to the embodiment. FIG. 10 is a diagram illustrating a tool setting operation in the diagnostic device according to the embodiment. FIG. 11 is a diagram illustrating a tool setting operation in the diagnostic device according to the embodiment. FIG. 12 is a diagram illustrating a tool setting operation in the diagnostic device according to the embodiment. FIG. 13 is a diagram illustrating a process step setting operation in the diagnostic device according to the embodiment. FIG. 14 is a diagram illustrating a setting operation of a processing step in the diagnostic device according to the embodiment. FIG. 15 is a diagram illustrating a setting operation of a processing step in the diagnostic device according to the embodiment. FIG. 16 is a diagram illustrating a setting operation of a processing step in the diagnostic device according to the embodiment. FIG. 17 is a diagram illustrating a setting operation of a processing step in the diagnostic device according to the embodiment. FIG. 18 is a diagram illustrating an alert threshold setting operation in the diagnostic device according to the embodiment. FIG. 19 is a diagram illustrating an alert threshold setting operation in the diagnostic device according to the embodiment. FIG. 20 is a diagram illustrating an alert threshold setting operation in the diagnostic device according to the embodiment. FIG. 21 is a flow diagram illustrating an example of a procedure for selecting a storage destination for data acquired by the diagnostic device according to the embodiment. FIG. 22 is a diagram illustrating an operation for setting a data storage destination in the diagnostic device according to the embodiment. FIG. 23 is a diagram illustrating an operation for setting a data storage destination in the diagnostic device according to the embodiment. FIG. 24 is a flow diagram illustrating an example of the procedure of data analysis processing by the diagnostic device according to the embodiment. FIG. 25 is a diagram illustrating updating of the abnormality score graph by the diagnostic device according to the embodiment. FIG. 26 is a diagram illustrating updating of an abnormality score graph by the diagnostic device according to the embodiment. FIG. 27 is a diagram illustrating updating of the abnormality score graph by the diagnostic device according to the embodiment. FIG. 28 is a diagram illustrating display of an alert icon by the diagnostic device according to the embodiment. FIG. 29 is a diagram illustrating display of an alert icon by the diagnostic device according to the embodiment. FIG. 30 is a diagram illustrating display of an alert icon by the diagnostic device according to the embodiment. FIG. 31 is a diagram illustrating display of an alert icon by the diagnostic device according to the embodiment. FIG. 32 is a diagram illustrating display of an alert icon by the diagnostic device according to the embodiment. FIG. 33 is a flow diagram illustrating an example of a procedure for displaying history data in the diagnostic device according to the embodiment. FIG. 34 is a diagram showing an example of a history data screen displayed by the diagnostic device according to the embodiment. FIG. 35 is a flow diagram illustrating an example of a procedure for abnormality score display processing in the diagnostic device according to the embodiment. FIG. 36 is a diagram illustrating a graph display of an abnormality score by the diagnostic device according to the embodiment. FIG. 37 is a diagram illustrating a graph display of an abnormality score by the diagnostic device according to the embodiment. FIG. 38 is a diagram illustrating a graph display of an abnormality score by the diagnostic device according to the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of a diagnostic apparatus, a diagnostic method, and a diagnostic program according to the present invention will be described in detail below with reference to the drawings. The present invention is not limited to the following embodiments, and the constituent elements in the following embodiments include those that can be easily figured out by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent scope. It will be done. Furthermore, various omissions, substitutions, changes, and combinations of constituent elements can be made without departing from the gist of the following embodiments.

(Overall configuration of diagnostic system)
FIG. 1 is a diagram schematically showing the configuration of a diagnostic system 1 according to an embodiment. As shown in FIG. 1, the diagnostic system 1 of the embodiment includes a processing machine 200, a diagnostic device 100, and a cloud server CS. The processing machine 200 and the diagnostic device 100 are connected to each other via a communication line CM. Diagnostic device 100 is connected to cloud server CS via network NT.

FIG. 2 is a diagram showing, in functional blocks, an example of the configuration of the processing machine 200 and the diagnostic device 100 according to the embodiment. The processing machine 200 is a machine tool that performs processing such as cutting, grinding, or polishing on a processing object using a tool. The processing machine 200 is an example of a target device to be diagnosed by the diagnostic device 100. The diagnostic device 100 is connected to the processing machine 200 so as to be able to communicate with the processing machine 200, and is a device that diagnoses abnormalities in the operation of the processing machine 200.

The processing machine 200 includes a numerical control section 201, a communication control section 221, a drive control section 223, a drive section 224, and a detection section 225.

The numerical control unit 201 is a functional unit that executes processing by the drive unit 224 using numerical control (NC). For example, the numerical control section 201 generates and outputs numerical control data that controls the operation of the drive section 224. Further, the numerical control unit 201 outputs, for example, a ladder signal to the communication control unit 221 as context information indicating the operating state of the drive unit 224 that drives the tool. The ladder signal is an ON/OFF signal that indicates the processing execution time. The machining execution time is the period from the start of the feeding motion of the tool to the workpiece until the end of the actual machining process. The context information is a plurality of pieces of information defined for each type of operation of the processing machine 200. In addition to the ladder signal described above, the context information includes, for example, identification information of the processing machine 200, identification information of the drive unit 224 such as tool identification information, the diameter of the tool driven by the drive unit 224, the material of the tool, etc. Configuration information and processing conditions such as the operating state of the drive unit 224, the cumulative usage time since the start of use of the drive unit 224, the load applied to the drive unit 224, the rotation speed of the drive unit 224, and the machining speed of the drive unit 224. It may also include information indicating information.

For example, the numerical control unit 201 sequentially transmits context information corresponding to the current operation of the processing machine 200 to the diagnostic device 100 via the communication control unit 221. The numerical control unit 201 changes the type of drive unit 224 to be driven, or the driving state such as the number of rotations and rotation speed of the drive unit 224, depending on the processing step when processing the workpiece. Each time the numerical control unit 201 changes the type of operation, it sequentially transmits context information corresponding to the changed type of operation to the diagnostic device 100 via the communication control unit 221.

The communication control unit 221 is a functional unit that controls communication with external devices such as the diagnostic device 100. The communication control unit 221 transmits, for example, context information corresponding to the current operation to the diagnostic device 100.

The drive control unit 223 is a functional unit that drives and controls the drive unit 224 based on the numerical control data obtained by the numerical control unit 201.

The drive unit 224 is a functional unit that is subject to drive control by the drive control unit 223. The drive unit 224 drives the tool under the control of the drive control unit 223. The drive unit 224 is an actuator (motor) or the like whose drive is controlled by the drive control unit 223. Note that the drive unit 224 may be any actuator as long as it is used for machining and is subject to numerical control. Furthermore, two or more driving units 224 may be provided.

The detection unit 225 is a functional unit that detects a physical quantity generated in the processing machine 200 and outputs information on the detected physical quantity to the diagnostic device 100 as detection information. The physical quantity generated by the processing machine 200 includes vibrations, sounds, etc. generated by the processing machine 200. Such vibrations, sounds, etc. are generated, for example, when a tool installed in the processing machine 200 and a workpiece come into contact during a processing operation. Alternatively, such vibrations, sounds, etc. are generated by the tool or processing machine 200 itself. The number of detection units 225 is arbitrary. For example, a plurality of detection units 225 that detect the same physical quantity may be provided, or a plurality of detection units 225 that detect mutually different physical quantities may be provided. For example, if the blade of a tool used for machining is broken or chipped, vibrations and sounds during machining will change. Therefore, abnormalities in the operation of the processing machine 200 can be detected by detecting vibration data and acoustic data with the detection unit 225 and making a determination using a model or the like that determines normal vibrations and sounds.

The diagnostic device 100 includes a communication control section 111, a detection information reception section 112, a processing information acquisition section 101, a diagnosis section 102, a data management section 103, a setting management section 104, a storage section 113, and an input section 114. , a display control section 105 , and a display section 115 .

The communication control unit 111 is a functional unit that controls communication between the processing machine 200, the cloud server CS, and the diagnostic device 100. For example, the communication control unit 111 receives detection information and context information from the processing machine 200 via the communication control unit 221. Furthermore, the communication control unit 111 transmits data and the like based on this information to the cloud server CS.

The detection information receiving unit 112 as a first acquisition unit is a functional unit that receives detection information from the detection unit 225 installed in the processing machine 200. The detection information receiving unit 112 temporarily stores the received detection information in a primary storage device that constitutes the storage unit 113.

The processing information acquisition unit 101 as a second acquisition unit is a functional unit that acquires context information received by the communication control unit 111 from the processing machine 200. The processing information acquisition unit 101 temporarily stores the acquired context information in a primary storage device that constitutes the storage unit 113.

The diagnosis unit 102 is a functional unit that includes a feature extraction unit 102a, a model generation unit 102b, and a score calculation unit 102c, and analyzes information detected by these components to determine abnormalities in the operation of the processing machine 200. .

The feature extraction unit 102a is a functional unit that extracts feature information indicating the characteristics of the detection information from the detection information. The characteristic information may be any information as long as it indicates the characteristics of the detected information.

The model generation unit 102b is a functional unit that generates a model used to determine whether processing has been performed normally. A model is generated for each context information, for example. Note that when the model is generated by an external device, the model generation unit 102b may not be provided.

The score calculation unit 102c uses, for example, the feature information extracted by the feature extraction unit 102a and the model for each context information generated by the model generation unit 102b, and calculates a score in which abnormalities of the extracted feature information are accumulated. Calculate the anomaly score, which is the value. In other words, the abnormality score indicates how much the extracted feature information deviates from the model, and thereby enables diagnosis of whether or not the operation of the processing machine 200 is normal.

The data management unit 103 is a functional unit that includes a data extraction unit 103a and a data storage 103b, and manages detection information, context information, feature information, anomaly scores, and the like.

The data extraction unit 103a calculates a predetermined section corresponding to a predetermined processing step in the processing machine 200 from the context information. The predetermined processing step is, for example, a processing step to be monitored set by the user. Further, the data extraction unit 103a extracts the detection information acquired in the above-mentioned predetermined section from among the detection information acquired by the detection information receiving unit 112. The above-mentioned diagnosis unit 102 performs various analyzes using, for example, the detected information of the extracted predetermined section to diagnose an abnormality of the processing machine 200.

The data storage unit 103b stores data such as the detection information, context information, feature information, and anomaly score in the predetermined section in at least one of the secondary storage device and the cloud server CS (see FIG. 1) that constitute the storage unit 113. It is stored and accumulated. Further, the data storage unit 103b reads the stored data from the secondary storage device and the cloud server CS according to instructions from the user.

The settings management unit 104 is a functional unit that manages various settings made by the user. The setting management unit 104 allows the user to set a predetermined tool as a tool to be monitored. Further, the setting management unit 104 allows the user to set a predetermined processing step as a processing step to be monitored. Further, the setting management unit 104 saves the contents of these settings made by the user in a secondary storage device that constitutes the storage unit 113.

The storage unit 113 is a functional unit that stores detection information, feature information, models, and anomaly scores in association with context information. Furthermore, the storage unit 113 holds various settings made by the user.

The input unit 114 is a functional unit that performs operations such as inputting characters and numbers, selecting various instructions, and moving a cursor.

The display control section 105 as an output control section is a functional section that controls the display operation of the display section 115. Specifically, the display control unit 105 causes the display unit 115 to display various information such as detection information, feature information, models, and abnormality scores. The display control unit 105 may display the accumulated various information on the display unit 115 as history information. The display unit 115 is a functional unit that displays various information under the control of the display control unit 105.

Note that the functional units of the diagnostic device 100 and the processing machine 200 are conceptually illustrated, and are not limited to such configurations. For example, a plurality of functional units illustrated as independent functional units in FIG. 2 may be configured as one functional unit. On the other hand, the function of one functional section in FIG. 2 may be divided into a plurality of parts and configured as a plurality of functional parts.

Moreover, the processing machine 200 and the diagnostic device 100 may be connected in any connection form. For example, the processing machine 200 and the diagnostic device 100 may be connected by a dedicated connection line, a wired network such as a wired LAN (Local Area Network), or a wireless network.

Further, although FIG. 2 shows an example in which one processing machine 200 is connected to the diagnostic device 100, the present invention is not limited to this, and a plurality of processing machines 200 are connected to the diagnostic device 100. may be connected so that they can communicate with each other.

(Hardware configuration of processing machine)
Next, an example of the hardware configuration of the processing machine 200 according to the embodiment will be described using FIG. 3. FIG. 3 is a diagram showing an example of the hardware configuration of the processing machine 200 according to the embodiment.

As shown in FIG. 3, the processing machine 200 includes a CPU (Central Processing Unit) 20, a ROM (Read Only Memory) 20a, a RAM (Random Access Memory) 20b, a communication I/F (interface) 21, and a drive The control circuit 23 is configured to be communicably connected to the control circuit 23 via the bus 2B.

The CPU 20 is a calculation device that controls the entire processing machine 200. For example, the CPU 20 controls the operation of the entire processing machine 200 and realizes processing functions by executing a program stored in the ROM 20a or the like using the RAM 20b as a work area. The numerical control unit 201 in FIG. 2 is realized by a program running on the CPU 20, for example.

The communication I/F 21 is an interface used for communication with external devices such as the diagnostic device 100. The communication I/F 21 is, for example, a NIC (Network Interface Card) compatible with TCP (Transmission Control Protocol)/IP (Internet Protocol). The communication control unit 221 in FIG. 2 is realized by, for example, the communication I/F 21 and a program running on the CPU 20.

The drive control circuit 23 is a circuit that controls the drive of the motor 24. The motor 24 drives a tool 24a used for machining. The tools 24a include a drill, an end mill, a cutting tip, a grindstone, etc., and a table on which a workpiece is placed and moved according to the work. The drive control section 223 in FIG. 2 is realized by, for example, the drive control circuit 23. The drive unit 224 in FIG. 2 is realized by the motor 24, for example.

The sensor 25 includes, for example, a microphone device, a vibration sensor, an acceleration sensor, an AE (Acoustic Emission) sensor, or the like, and is installed near a tool that can detect vibrations, sounds, etc., for example. The sensor amplifier 25a to which the sensor 25 is connected is communicably connected to the diagnostic device 100. The sensor 25 and the sensor amplifier 25a may be provided in the processing machine 200 in advance, or may be attached to the processing machine 200, which is a completed machine, afterwards. Further, the sensor amplifier 25a is not limited to being installed in the processing machine 200, but may be installed in the diagnostic device 100 side. The detection unit 225 in FIG. 2 is realized by, for example, the sensor 25 and the sensor amplifier 25a.

Note that the hardware configuration shown in FIG. 3 is an example, and the processing machine 200 does not need to include all the components, and may include other components. For example, the numerical control unit 201 and the communication control unit 221 shown in FIG. 2 may be realized by having the CPU 20 shown in FIG. 3 execute a program, that is, by software, or by hardware such as an IC (Integrated Circuit). Alternatively, it may be implemented using a combination of software and hardware.

(Hardware configuration of diagnostic equipment)
Next, an example of the hardware configuration of the diagnostic device 100 according to the embodiment will be described using FIG. 4. FIG. 3 is a diagram showing an example of the hardware configuration of the diagnostic device 100 according to the embodiment.

As shown in FIG. 4, the diagnostic device 100 includes a CPU 10, a ROM 10a, a RAM 10b, a communication I/F 11, a sensor I/F 12, an auxiliary storage device 13, an input device 14, and a display 15. The configuration is such that they are communicably connected via 1B.

The CPU 10 is an arithmetic unit that controls the entire diagnostic device 100. The CPU 10 controls the overall operation of the diagnostic apparatus 100 and realizes a diagnostic function by executing a program such as a diagnostic program stored in the ROM 10a or the like using the RAM 10b as a work area. The processing information acquisition unit 101, diagnosis unit 102, data management unit 103, setting management unit 104, and display control unit 105 in FIG. 2 are realized by, for example, a program running on the CPU 10.

The communication I/F 11 is an interface used for communication with external devices such as the processing machine 200. The communication I/F 11 is, for example, a NIC compatible with TCP/IP. The communication control unit 111 in FIG. 2 is realized, for example, by the communication I/F 11 shown in FIG. 4 and a program running on the CPU 10.

The sensor I/F 12 is an interface that receives detection information from the sensor 25 installed in the processing machine 200 via the sensor amplifier 25a. The detection information receiving unit 112 in FIG. 2 is realized by, for example, the sensor I/F 12 and a program running on the CPU 10.

The auxiliary storage device 13 is an HDD (Hard Disk Drive) or SSD that stores various data such as setting information of the diagnostic device 100, detection information and context information received from the processing machine 200, an OS (Operating System), and application programs. (Solid State Drive) or EEPROM (Electrically Erasable Programmable Read-Only Memory).

Although the auxiliary storage device 13 is assumed to be included in the diagnostic device 100, it is not limited thereto, and may be, for example, a storage device installed outside the diagnostic device 100, or may be provided in the diagnostic device 100. The storage device may be a storage device included in a server device such as a cloud server CS (see FIG. 1) that is capable of data communication with 100.

The storage unit 113 in FIG. 2 is realized by, for example, the RAM 10b, the auxiliary storage device 13, and the like. Here, the RAM 10b functions as a primary storage device that temporarily stores, for example, detection information acquired by the detection information receiving unit 112. Further, among the auxiliary storage devices 13, the HDD, SSD, EEPROM, or the like functions as a secondary storage device that stores data such as detection information, context information, characteristic information, and abnormality scores in a predetermined section over a long period of time. Further, the cloud server CS of the auxiliary storage device 13 also has a function of long-term storage of data such as detection information, context information, characteristic information, and anomaly score in a predetermined section.

The input device 14 is an input device such as a mouse or a keyboard for inputting characters, numbers, etc., selecting various instructions, and moving a cursor. The input unit 114 in FIG. 2 is realized by, for example, the input device 14.

The display 15 is a display device such as a CRT (Cathode Ray Tube) display, an LCD (Liquid Crystal Display), or an organic EL (Electro-Luminescence) display that displays characters, numbers, various screens, operation icons, and the like. The display unit 115 in FIG. 2 is realized by the display 15, for example.

Note that the hardware configuration shown in FIG. 4 is an example, and the diagnostic device 100 does not need to include all of the component devices, and may include other component devices. For example, each functional unit (processing information acquisition unit 101, diagnosis unit 102, data management unit 103, setting management unit 104, and display control unit 105) of the diagnostic device 100 shown in FIG. 2 causes the CPU 10 to execute a program. That is, it may be realized by software, it may be realized by hardware such as an IC, or it may be realized by using software and hardware together. Furthermore, when the diagnostic device 100 specializes in diagnosing the processing machine 200 and transmits the diagnostic results to an external server device or the like, the input device 14 and the display 15 may not be provided.

(Example of diagnostic device functions)
Next, a functional example of the diagnostic device 100 will be described using FIGS. 5 and 6.

FIG. 5 is a diagram showing an example of detection information and ladder signals of the processing machine 200 according to the embodiment. As shown in FIG. 5, the detection information includes a waveform portion indicating a non-processing section and a waveform portion indicating a processing section. The non-machining section is the section before and after the tool starts the feeding operation to the object to be machined. The machining section is a section where the tool is in contact with the object to be machined to perform processing such as cutting. In other words, the machining section is the period during which machining is actually performed.

On the other hand, the processing machine 200, for example, turns on the ladder signal at the start of a processing operation using a tool, performs a feed operation to send the tool to the object to be processed, and turns off the ladder signal when the actual processing is completed. In other words, the section in FIG. 5 in which the ladder signal is in the ON state is the time when machining is performed. The machining execution time includes a non-machining section where the tool is not in contact with the workpiece, and a machining section where the tool is in contact with the workpiece and machining is being performed.

When manufacturing a plurality of predetermined processed products, the processing machine 200 may repeatedly perform a cycle including a plurality of processing steps P1 to P3. At this time, the diagnostic device 100 acquires sensor data such as vibrations and sounds in the processing machine 200, that is, detection information, along with context information such as a ladder signal. The data extraction unit 103a of the data management unit 103 determines the processing execution time corresponding to each processing step P1 to P3 from the ladder signal, and further determines the processing execution time corresponding to a predetermined processing step based on the user's settings. Extract the acquired detection information. The diagnosis unit 102 performs abnormality diagnosis of the processing machine 200 by variously analyzing the detection information of the extracted section.

FIG. 6 is a diagram schematically illustrating feature information extracted from detection information by the diagnostic device 100 according to the embodiment using frequency components. The section shown in FIG. 6 is, for example, the time of processing corresponding to the processing step P2 set as a monitoring target by the user.

When the detection information is frequency data collected by a vibration sensor or a microphone device, the feature extraction unit 102a extracts energy, frequency spectrum, MFCC (Mel frequency cepstral coefficient), etc. as feature information. As shown in FIG. 6, in this embodiment, the extracted feature information will be described as a frequency spectrum.

The feature extraction unit 102a extracts feature information by, for example, performing Fourier transform on the detection information for each frame. Here, a frame refers to the amount of data for a predetermined period of time, such as 20 [ms], 40 [ms], etc., of detection information, and the feature information is a frequency spectrum obtained by Fourier transforming the detection information. This corresponds to the amount of data for the window length in a certain case. The feature information shown in FIG. 6 is associated with the time of the corresponding detection information frame.

The model generation unit 102b performs correlation analysis for each processing step of feature information extracted by the feature extraction unit 102a from detection information during normal operation of the processing machine 200, machine learning or deep learning using the feature information, etc., for example. Generate a model for each context information.

The score calculation unit 102c calculates an abnormality score for the detection information to be determined as an abnormality. Specifically, the score calculation unit 102c compares the model generated by the model generation unit 102b with the feature information extracted from the detection information of the abnormality determination target, and calculates the cumulative value of the abnormality of the feature information, that is, An anomaly score indicating how far the feature information deviates from the model is calculated. Then, the score calculation unit 102c compares the calculated abnormality score with a predetermined alert threshold, and determines that the processing machine 200 is normal if the abnormality score is equal to or less than the alert threshold. Furthermore, the score calculation unit 102c determines that the processing machine 200 is abnormal if the abnormality score exceeds the alert threshold.

Note that the score calculation unit 102c calculates the abnormality score by comparing the characteristic information obtained during normal operation of the processing machine 200 and the characteristic information obtained from the detection information of the abnormality determination target without using a model. It may be calculated. Alternatively, the score calculation unit 102c may calculate the abnormality score by comparing the detection information obtained during normal operation of the processing machine 200 and the detection information of the abnormality determination target.

(Example of setting process in diagnostic equipment)
When diagnosing an abnormality in the processing machine 200 using the diagnostic device 100, the user can make several settings for the diagnostic device 100. Examples of setting processing in the diagnostic device 100 will be described below with reference to FIGS. 7 to 23.

FIG. 7 is a flow diagram illustrating an example of a procedure of processing related to data acquisition in the diagnostic device 100 according to the embodiment. As shown in FIG. 7, the user can configure tools, process steps, and alert thresholds for the diagnostic device 100.

Specifically, the setting management unit 104 of the diagnostic device 100 receives the content of the user's setting of a predetermined tool as a monitoring target on the tool setting screen displayed on the display unit 115 by the display control unit 105 (step S101).

Further, the setting management unit 104 of the diagnostic device 100 selects a predetermined process from among the process processes using the tool set as a monitoring target by the user on the process process setting screen displayed on the display unit 115 by the display control unit 105. The contents of setting a process as a monitoring target are accepted (step S102).

In addition, the setting management unit 104 of the diagnostic device 100 allows the user to set the alert threshold for the abnormality score in the processing process set as the monitoring target on the alert threshold setting screen displayed on the display unit 115 by the display control unit 105. It is accepted (step S103).

8 to 12 are diagrams illustrating tool setting operations in the diagnostic device 100 according to the embodiment.

As shown in FIG. 8, on the initial screen for tool settings, a list of tool names of various tools included in the processing machine 200 is displayed. The user can load a list of settings for a desired tool by pressing the "Load Delete" button.

As shown in FIG. 9, a list of settings for the tools loaded by the user is displayed. The user can load desired setting contents from among the predetermined setting contents by selecting the predetermined setting contents and pressing the "Load" button.

As shown in FIG. 10, by pressing the "OK" button, the user can reflect the loaded setting contents in subsequent data acquisition in the diagnostic apparatus 100.

As shown in FIG. 11, if the desired settings for a desired tool do not exist, the user can create new settings for that tool. After inputting new settings for the tool, the user can save the newly input settings by inputting a name indicating the settings and pressing the "OK" button. In other words, the settings management unit 104 stores new settings in a secondary storage device such as an HDD included in the diagnostic device 100, for example. The setting contents saved in the secondary storage device are added to the list in FIG. 9.

As shown in FIG. 12, the user can also delete unnecessary settings for a desired tool. By selecting a predetermined setting content on the list screen of FIG. 9 and pressing the "Delete" button, and further pressing the "OK" button on the screen of FIG. 12, the setting content is deleted. The deleted setting contents are also deleted from the list in FIG. 9.

13 to 17 are diagrams illustrating setting operations for processing steps in the diagnostic device 100 according to the embodiment.

As shown in FIG. 13, on the initial screen for process setting, a list of process processes in which the monitoring target tool set on the tool setting screen is used is displayed. The user can load a list of settings for a desired process step by pressing the "Load Delete" button.

As shown in FIG. 14, a list of settings for the processing steps loaded by the user is displayed. The user can load desired setting contents from among the predetermined setting contents by selecting the predetermined setting contents and pressing the "Load" button.

As shown in FIG. 15, by pressing the "OK" button, the user can reflect the loaded setting contents in subsequent data acquisition in the diagnostic apparatus 100. That is, the diagnostic device 100 thereafter performs analysis processing and the like on the detection information of the processing execution time corresponding to the processing step selected by the user.

As shown in FIG. 16, if the desired settings for a desired processing step do not exist, the user can create new settings for that processing step. After inputting new settings for the processing step, the user can save the newly input settings by inputting a name indicating the settings and pressing the "OK" button. In other words, the settings management unit 104 stores new settings in a secondary storage device such as an HDD included in the diagnostic device 100, for example. The setting contents saved in the secondary storage device are added to the list in FIG. 14.

As shown in FIG. 17, the user can also delete unnecessary settings for a desired processing step. By selecting a predetermined setting content on the list screen of FIG. 14 and pressing the "Delete" button, and further pressing the "OK" button on the screen of FIG. 17, the setting content is deleted. The deleted setting contents are also deleted from the list in FIG. 14.

18 to 20 are diagrams illustrating the alert threshold setting operation in the diagnostic device 100 according to the embodiment.

As shown in FIG. 18, the user can display an alert threshold setting screen by pressing the "Alert Settings" button on a display screen of analysis results of data acquired in the past. The analysis screen shown in FIG. 18 is a screen in which the abnormality score calculated for the predetermined data by the score calculation unit 102c is displayed in a graph.

As shown in FIG. 19, on the alert threshold setting screen, the user sets "Alert" to "Enable", enters a predetermined value in "Alert Threshold", and presses the "OK" button to set the desired alert. Thresholds can be set.

As shown in FIG. 20, when a predetermined alert threshold is set by the user, a line segment AT indicating the alert threshold is displayed on the graph of the abnormality score, which is past data. In the example of FIG. 20, an abnormality score value of 1.2 is set as the alert threshold. Thereby, the user can reflect the set alert threshold value in subsequent data acquisition by the diagnostic device 100. That is, the diagnostic device 100 determines that an abnormality has occurred in the processing machine 200 if the abnormality score exceeds the set alert threshold in the processing process that is to be monitored from now on. Furthermore, the diagnostic device 100 displays an alert icon on the display unit 115, for example, to notify the user of an abnormality in the processing machine 200.

FIG. 21 is a flow diagram illustrating an example of a procedure for selecting a storage destination for data acquired by the diagnostic device 100 according to the embodiment. As described above, the diagnostic device 100 uses a primary storage device such as the RAM 10b that constitutes the storage unit 113 as a temporary data storage location. As shown in FIG. 21, the user can set a long-term data storage destination for the diagnostic device 100.

Specifically, the setting management unit 104 of the diagnostic apparatus 100 accepts the content selected by the user as the data storage destination on the data storage destination setting screen displayed on the display unit 115 by the display control unit 105 ( Step S201). The user selects, as the data storage destination, either a secondary storage device such as an HDD that constitutes the storage unit 113 of the diagnostic device 100, or a cloud server CS connected to the diagnostic device 100 via the network NT. be able to.

The diagnostic device 100 communicates with a storage device selected by the user, such as a secondary storage device or a cloud server CS, and checks the capacity of the storage device to determine whether data can be written to the storage device. Determination is made (step S202).

For example, when the storage device at the storage destination is in a state where data cannot be written (step S202: No), such as when there is no free space in the storage device, the display control unit 105 displays a screen that allows the user to select another storage device, etc. Go there and try to resolve the problem.

If data can be written (step S202: Yes), the user's setting of the data storage destination is enabled (step S203).

FIGS. 22 and 23 are diagrams illustrating an operation for setting a data storage destination in the diagnostic device 100 according to the embodiment.

As shown in FIG. 22, on the initial setting screen, the user can display candidates for data storage locations by setting "External Output" to "Enable" and pressing the "Select" button.

As shown in FIG. 23, on a screen displaying candidates for data storage locations, the user can select either a secondary storage device such as an HDD or a cloud server CS, and set it as the data storage location. . The diagnostic device 100 selects the detection information of the processing execution time corresponding to the processing step set by the user, the characteristic information, the model, the abnormality score, etc. based on the detection information from among the data acquired after this, as selected by the user. Store in storage destination.

With the above, the setting process that is a prerequisite for acquiring, analyzing, and accumulating data in the diagnostic apparatus 100 is completed.

(Example of data analysis processing of diagnostic equipment)
Next, an example of data analysis processing by the diagnostic device 100 will be described using FIGS. 24 to 32. FIG. 24 is a flow diagram illustrating an example of the procedure of data analysis processing by the diagnostic device 100 according to the embodiment.

As shown in FIG. 24, upon receiving a press of the recording start button from the user (step S301), the diagnostic apparatus 100 starts acquiring information from the processing machine 200 (step S302). That is, the detection information receiving unit 112 of the diagnostic device 100 acquires detection information from the processing machine 200. The machining information acquisition unit 101 acquires context information from the machining machine 200.

The detection information receiving unit 112 and the processing information acquiring unit 101 check the free space of the primary storage device such as the RAM 10b that constitutes the storage unit 113 (step S303), and if there is no free space (step S303: No), old data is deleted. is deleted (step S304).

When there is free space (step S303: Yes) or after deleting old data, the detection information receiving unit 112 and the processing information acquisition unit 101 temporarily save the acquired detection information and context information in the primary storage device. (Step S305).

The data extraction unit 103a determines whether or not the acquired detection information includes the processing implementation time based on the ladder signal included in the context information temporarily stored in the primary storage device (step S306 ). If the processing implementation time is not included (step S306: No), the process returns to step S302 and waits for the next information to be acquired.

If the processing execution time is included (step S306: Yes), the data extraction unit 103a determines, based on the context information, that the processing execution time corresponds to the processing step to be monitored set by the user. It is determined whether it is time to perform machining (step S307). If it is not the processing execution time corresponding to the processing step to be monitored (step S307: No), the process returns to step S302 and waits for the next information to be acquired.

If it is the processing execution time corresponding to the processing step to be monitored (step S307: Yes), the data extraction unit 103a extracts the detection information acquired at the processing execution time. The data storage unit 103b sends the extracted detection information along with the corresponding context information to a secondary storage device such as an HDD constituting the storage unit 113 or to the cloud server CS, whichever is set by the user as a data storage destination. Save (step S308).

Furthermore, the diagnosis unit 102 variously analyzes the detection information extracted by the data extraction unit 103a (step S309). That is, the feature extraction unit 102a extracts feature information from the detection information. The model generation unit 102b generates a model from the feature information. The data storage unit 103b stores analysis information such as feature information and models along with corresponding context information in the secondary storage device or cloud server CS set by the user as a data storage destination (step S310).

Furthermore, the score calculation unit 102c calculates an abnormality score from the feature information and the like (step S311). The data storage unit 103b stores the calculated abnormality score together with the corresponding context information in the secondary storage device or cloud server CS set by the user as a data storage destination (step S312).

The score calculation unit 102c also determines whether the calculated abnormality score exceeds the alert threshold (step S313). If the abnormality score does not exceed the alert threshold (step S313: No), the process returns to step S302 and waits for the next information to be acquired.

If the abnormality score exceeds the alert threshold (step S313: Yes), the score calculation unit 102c determines that the detection information generated at the time of the processing to be monitored is abnormal, that is, there is an abnormality in the processing machine 200. It is determined that this is occurring (step S314).

When the score calculation unit 102c determines that there is an abnormality, the display control unit 105 causes the display unit 115 to display an alert icon (step S315).

As described above, the diagnostic device 100 sequentially acquires detection information and context information from the processing machine 200, and analyzes the detection information regarding the processing execution time to be monitored. In addition, the diagnostic device 100 sequentially accumulates detection information about the processing execution time of the monitored object in the secondary storage device or the cloud server CS. In addition, the diagnostic device 100 sequentially updates the feature information, model, and abnormality score that are analysis results, and accumulates them in the secondary storage device or cloud server CS.

FIGS. 25 to 27 are diagrams illustrating updating of the abnormality score graph by the diagnostic device 100 according to the embodiment.

As shown in FIG. 25, the display control unit 105 of the diagnostic device 100 causes the display unit 115 to display a graph of the abnormality score, for example, in accordance with the user's instructions. On the anomaly score graph display screen, anomaly scores based on detection information that has been acquired and analyzed are plotted.

As shown in FIG. 26, when the abnormality score plot reaches the right end of the graph and new abnormality score data to be added is generated, the display control unit 105 sets a predetermined value to the width of the graph area. The plot display of the anomaly score is shrunk and shifted to the left by the ratio of . The abnormality score data is added, for example, at the timing when the processing execution time of the monitored object ends, that is, at the timing when the ladder signal is turned off.

As shown in FIG. 27, the display control unit 105 adds the newly generated abnormality score to the empty area of the graph. After the plot display is reduced to a predetermined ratio in order to add a new abnormal score plot, new pages are added without further reducing the plot display.

28 to 32 are diagrams illustrating the display of alert icons by the diagnostic device 100 according to the embodiment.

As shown in FIG. 28, in this example, the alert threshold is set to, for example, the abnormality score value of 1.2. Initially, the anomaly score was kept below 1.2, and no anomaly was detected.

As shown in FIG. 29, if the value of the abnormality score exceeds 1.2 after a predetermined number of cycles, an alert icon AI is displayed, for example, in the upper right corner of the screen. As a result, the user is notified that an abnormality has occurred in the processing machine 200.

As shown in FIG. 30, after confirming the abnormality in the processing machine 200 using the alert icon AI, the user presses the "Turn Off Alert" button on the screen shown in FIG. 29, and then presses the "OK" button on the screen shown in FIG. You can turn off the alert and hide the alert icon by pressing .

As shown in FIG. 31, when the alert is turned off by the user, the alert icon AI is hidden. However, from now on, when data having an abnormality score exceeding the alert threshold is newly acquired, the alert icon AI is displayed again.

As shown in FIG. 32, data for the next cycle is acquired, an abnormality score exceeding the alert threshold is calculated again, and the alert icon AI is redisplayed.

(Example of data read processing from storage device)
The user can arbitrarily cause the diagnostic device 100 to display graphs of abnormality scores as described above and other analysis results. Further, history data accumulated in the secondary storage device or cloud server CS can be arbitrarily displayed on the diagnostic device 100. Next, an example of a process for reading data from the diagnostic device 100 will be described using FIGS. 33 to 38.

FIG. 33 is a flow diagram illustrating an example of a procedure for displaying history data in the diagnostic device 100 according to the embodiment.

As shown in FIG. 33, the display control unit 105 of the diagnostic apparatus 100 transitions the display on the display unit 115 from a predetermined screen to a history screen according to a user's instruction (step S401).

The data storage unit 103b reads detection information and analysis results from the secondary storage device or cloud server CS where the data is stored, according to the user's selection from the history screen (step S402).

The display control unit 105 causes the display unit 115 to display the read data as history data (step S403).

FIG. 34 is a diagram showing an example of a history screen displayed by the diagnostic device 100 according to the embodiment. As shown in FIG. 34, on the history screen, the user can select the type of graph or data to be displayed, such as a waveform or frequency spectrum. Furthermore, the user can specify a number and select a processing step for data to be displayed. In the example of FIG. 34, the waveforms of detection information in all processing steps are selected and displayed.

FIG. 35 is a flow diagram illustrating an example of a procedure for displaying an abnormality score in the diagnostic device 100 according to the embodiment.

As shown in FIG. 35, the display control unit 105 of the diagnostic apparatus 100 transitions the display on the display unit 115 from a predetermined screen to an analysis screen according to a user's instruction (step S501).

The data storage unit 103b reads out the target abnormality score from the secondary storage device or cloud server CS where the data is stored, according to the user's selection from the analysis screen (step S502).

The display control unit 105 converts the read abnormality score into a graph, for example, and displays the graph on the display unit 115 (step S503).

36 to 38 are diagrams illustrating graphical display of abnormality scores by the diagnostic device 100 according to the embodiment.

As shown in FIG. 36, on the initial screen for displaying the analysis results, a list of data numbers indicating the analysis results obtained so far is displayed. The "!" mark displayed to the right of the data number indicates that the data includes an abnormal score that exceeds the alert threshold. A check mark displayed to the right of the data number indicates that the data does not include an abnormal score exceeding the alert threshold and no abnormality has occurred. If nothing is displayed to the right of the data number, this indicates that no alert threshold has been set for that data. The user can display the abnormality score of desired data by pressing the icon of any data number from among these. Note that the alert icon AI at the top right of the screen indicates that an abnormality has occurred in at least one of the data in the list.

As shown in FIG. 37, when the user selects data with data number "2001", for example, a graph of the abnormality score of that data is displayed. Data number "2001" is data with a check mark and no abnormality. Therefore, all plots of anomaly scores are kept below the alert threshold. The alert icon AI is displayed at the top right of the screen because an abnormality has occurred in other data.

As shown in FIG. 38, when the user selects data with data number "1115", for example, a graph of the abnormality score of that data is displayed. Data number "1115" is data with an "!" mark and in which an abnormality has occurred. Indeed, at the right end of the graph, the anomaly score plot exceeds the alert threshold. The alert icon AI is displayed at the top right of the screen because an abnormality has occurred in some data including the currently displayed data.

As described above, the diagnostic processing by the diagnostic apparatus 100 is executed through the series of processing in FIGS. 7, 21, 24, 33, and 35.

For example, Patent Document 1 discloses an abnormality detection device that includes a sound wave conversion means, a storage device for recorded data, and an abnormality signal output means for the purpose of detecting abnormalities during machining of a machine tool. ing. However, the abnormality detection device of Patent Document 1 only stores the acquired data without sorting it, and cannot efficiently accumulate the data. Further, regarding the method of notifying the user of abnormalities, no improvements have been made in the GUI (Graphical User Interface), and data cannot be provided in a format that is easy for the user to use.

In the diagnostic apparatus 100 of the embodiment, a user can set a processing step to be monitored, and detection information and the like corresponding to the processing step to be monitored are extracted and accumulated. In other words, the section specified by the user of the processing execution time is determined, necessary data is transferred to the secondary storage device or cloud server CS, and unnecessary data is immediately discarded. Thereby, among the acquired data, data that the user considers to be particularly important can be efficiently accumulated. Therefore, the limited resources in the storage unit 113 and the like can be effectively utilized, and by allowing the user to read out the data at will, the data can be immediately presented to the user.

In the diagnostic apparatus 100 of the embodiment, data is accumulated in a storage destination selected by the user, such as a secondary storage device or a cloud server CS. This allows data to be stored for a long period of time, and can also be used in cases where data analysis needs to be performed in bulk at a later date.

In the diagnostic device 100 of the embodiment, an abnormality score is calculated and displayed for the detection information of the monitoring target. This makes it possible to provide analysis results in a format that is easy for the user to understand and use. By conveying the analysis results to the user in an easy-to-understand manner, it is possible to contribute to improving the productivity of the processing machine 200 and reducing downtime when a tool breaks.

In the diagnostic device 100 of the embodiment, the abnormality score is displayed in a graph. It is also possible to display the history of accumulated data. This makes it possible to predict when consumables such as tools will wear out, and to reduce downtime by incorporating tool replacement timing into the schedule.

In the diagnostic device 100 of the embodiment, a predetermined alert threshold can be set for the abnormality score. This allows the user to immediately notice if an abnormality occurs.

In the diagnostic device 100 of the embodiment, the settings of the tool and the settings of the process set by the user are saved, and the user can read out the settings. Thereby, previous settings etc. can be used without any effort on the part of the user.

(Modified example)
In the above-described embodiment, the detection information of processing execution time corresponding to the processing step set as a monitoring target by the user is stored and accumulated over a long period of time, but the present invention is not limited to this. Detection information other than the processing execution time, that is, detection information acquired by the detection information receiving unit 112, for example, may be directly associated with the corresponding context information and stored and accumulated for a long period of time.

In the embodiment described above, the display control unit 105 of the diagnostic device 100 causes the display unit 115 to display various data, but the data output method is not limited to this. Various data may be printed out to a printer or the like connected to the diagnostic device 100. Various data may be output as audio data through a speaker or the like connected to the diagnostic device 100, such as when an alarm is issued when an alert threshold is exceeded.

In the embodiment described above, the detection information is, for example, vibration data or acoustic data, but other data such as the current value of the motor, load, torque, etc. can also be used as the detection information.

In the above-described embodiment, the processing machine 200 is diagnosed using various detection information, but only the processing section that is the actual processing section may be used for analysis to accumulate and store scores. .

In the above embodiment, the device to be diagnosed is, for example, the processing machine 200, but the target device may also be other machine tools such as assembly machines, measuring machines, inspection machines, or machines such as cleaning machines. .

Note that the programs such as the diagnostic program executed by the diagnostic system 1 of the above-described embodiment and each modification may be configured to be provided in advance in a ROM or the like.

Furthermore, the programs such as the diagnostic program executed by the diagnostic system 1 of the above-described embodiment and each modification are stored in installable or executable format files on a CD-ROM (Compact Disc Read Only Memory) or a flexible disk. It may also be configured to be recorded on a computer-readable recording medium such as (FD), CD-R (Compact Disk-Recordable), or DVD (Digital Versatile Disk) and provided as a computer program product.

Furthermore, programs such as the diagnostic program executed by the diagnostic system 1 of the above-described embodiment and each modification are stored on a computer connected to a network such as the Internet, and provided by being downloaded via the network. may be configured. Furthermore, the programs executed by the diagnostic systems of the embodiments and modifications described above may be provided or distributed via a network such as the Internet.

Further, the programs such as the diagnostic program executed in the diagnostic system 1 of the above-described embodiment and each modification have a module configuration including each of the above-mentioned functional units, and as actual hardware, the CPU executes the program from the ROM. By reading and executing , each of the above-mentioned functional units is loaded onto the main memory, and each functional unit is generated on the main memory.

1 Diagnostic system 1B bus 10 CPU
10a ROM
10b RAM
11 Communication I/F
12 Sensor I/F
13 Auxiliary storage device 14 Input device 15 Display 2B Bus 20 CPU
20a ROM
20b RAM
21 Communication I/F
23 Drive control circuit 24 Motor 24a Tool 25 Sensor 25a Sensor amplifier 100 Diagnosis device 101 Machining information acquisition section 102 Diagnosis section 102a Feature extraction section 102b Model generation section 102c Score calculation section 103 Data management section 103a Data extraction section 103b Data accumulation section 104 Setting Management section 105 Display control section 111 Communication control section 112 Detection information receiving section 113 Storage section 114 Input section 115 Display section 200 Processing machine 201 Numerical control section 221 Communication control section 223 Drive control section 224 Drive section 225 Detection section

Japanese Patent Application Publication No. 6-11387

Claims (12)

A first acquisition unit that acquires detection information output from a detection unit that detects a physical quantity that changes according to the operation of a target device that repeats a cycle including multiple processing steps, and temporarily stores it in a primary storage device. and,
a second acquisition unit that acquires from the target device a signal indicating a section in which the target device is operated;
A data extraction unit that determines from the signal a predetermined section corresponding to a processing step to be monitored set by a user among the plurality of processing steps in the target device, and extracts the detected information acquired in the predetermined section. and,
a score calculation unit that calculates an abnormality score that is a score value in which abnormalities in the detection information are accumulated based on the detection information in the predetermined section;
a data storage unit that stores at least one of the detection information and the abnormality score of the predetermined section in at least one of a secondary storage device and a cloud server;
an output control unit that causes an output device to output at least one of the accumulated detection information and the abnormality score of the predetermined section ,
The data extraction unit is
Determining whether or not the detection information acquired by the first acquisition unit includes processing implementation time;
If it is determined that the detection information includes a processing execution time, whether or not the processing execution time is a processing execution time corresponding to a processing step to be monitored set by the user. determine,
extracting the detection information acquired by the first acquisition unit when it is determined that the processing execution time corresponds to the processing step to be monitored set by the user;
The data storage unit is
When accumulating the detection information of the predetermined section, accumulating the detection information extracted by the data extraction unit in at least one of the secondary storage device and the cloud server;
Diagnostic equipment. The first acquisition unit and the second acquisition unit are
checking the free space of the primary storage device, and if there is no free space, deleting the detection information stored in the primary storage device;
The diagnostic device according to claim 1. The data storage unit distributes and stores the detection information in the secondary storage device or the cloud server according to a user's selection.
The diagnostic device according to claim 1 or claim 2. comprising a settings management unit that stores settings made by the user regarding the processing steps of the monitoring target in the secondary storage device;
The diagnostic device according to any one of claims 1 to 3. The target device includes a plurality of tools,
In the processing step to be monitored, a predetermined tool set by the user is used,
The settings management unit stores settings related to the predetermined tool by the user in the secondary storage device.
The diagnostic device according to claim 4. The output control unit causes the output device to output an alert when the abnormality score exceeds an alert threshold set by the user;
The settings management unit stores settings related to the alert threshold by the user in the secondary storage device.
The diagnostic device according to claim 4 or claim 5. The output control unit graphs the abnormality score and causes the output device to output the graph.
The diagnostic device according to any one of claims 1 to 6. the output control unit causes the output device to output a history of information accumulated in the secondary storage device;
The diagnostic device according to any one of claims 1 to 7. The secondary storage device stores not only the detection information of the predetermined section but also the detection information acquired by the first acquisition unit according to a user's selection.
The diagnostic device according to any one of claims 1 to 8. The processing execution time is a period in which the signal is in an on state;
The diagnostic device according to any one of claims 1 to 8. A first acquisition step of acquiring detection information output from a detection unit that detects a physical quantity that changes according to the operation of a target device that repeats a cycle including multiple processing steps, and temporarily storing it in a primary storage device. and,
a second acquisition step of acquiring from the target device a signal indicating a section in which the target device is operated;
a data extraction step of determining from the signal a predetermined section corresponding to a monitoring target processing step set by the user among the plurality of processing steps in the target device, and extracting the detected information acquired in the predetermined section; and,
a score calculation step of calculating an abnormality score, which is a score value in which abnormalities in the detection information are accumulated, based on the detection information in the predetermined section;
a data accumulation step of accumulating at least one of the detection information and the abnormality score of the predetermined section in at least one of a secondary storage device and a cloud server;
an output control step of causing an output device to output at least one of the accumulated detection information and the abnormality score of the predetermined section ,
In the data extraction step,
Determining whether the acquired detection information includes processing execution time,
If it is determined that the detection information includes a time for processing, whether or not the time for processing corresponds to a processing step to be monitored set by the user. determine,
extracting the acquired detection information when it is determined that the processing execution time is the processing execution time corresponding to the processing step to be monitored set by the user;
In the data accumulation step, when accumulating the detection information of the predetermined section,
accumulating the extracted detection information in at least one of the secondary storage device and the cloud server;
Diagnostic method. to the computer,
A first acquisition step of acquiring detection information output from a detection unit that detects a physical quantity that changes according to the operation of a target device that repeats a cycle including multiple processing steps, and temporarily storing it in a primary storage device. and,
a second acquisition step of acquiring from the target device a signal indicating a section in which the target device is operated;
a data extraction step of determining from the signal a predetermined section corresponding to a monitoring target processing step set by the user among the plurality of processing steps in the target device, and extracting the detected information acquired in the predetermined section; and,
a score calculation step of calculating an abnormality score, which is a score value in which abnormalities in the detection information are accumulated, based on the detection information in the predetermined section;
a data accumulation step of accumulating at least one of the detection information and the abnormality score of the predetermined section in at least one of a secondary storage device and a cloud server;
performing an output control step of causing an output device to output at least one of the accumulated detection information and the abnormality score of the predetermined section ;
In the data extraction step,
Determining whether the acquired detection information includes processing execution time,
If it is determined that the detection information includes a time for processing, whether or not the time for processing corresponds to a processing step to be monitored set by the user. determine,
extracting the acquired detection information when it is determined that the processing execution time is the processing execution time corresponding to the processing step to be monitored set by the user;
In the data accumulation step, when accumulating the detection information of the predetermined section,
accumulating the extracted detection information in at least one of the secondary storage device and the cloud server;
Diagnostic program.

Images