Analytical equipment and analytical programs

Abstract

To provide an analysis apparatus which can effectively analyze occurrence causes of abnormality that occurred in a production process.SOLUTION: An analysis apparatus (10) according to the present invention has: a device abnormality data detecting unit (14a) for detecting device abnormality data; an operator abnormality data detecting unit (14b) for detecting operator abnormality data; a classification unit (14c) for classifying, based on detection results of the device abnormality data by the device abnormality data detecting unit (14a) and the operator abnormality data by the operator abnormality data detecting unit (14b), abnormality occurrence causes into a group of causes due to device and a group of causes due to an operator; and a display unit (12) for displaying a result of the classification of the classification unit (14c).SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to an analytical device and an analysis program.

Background Art

[0002] In the production process of a factory, it is known to record logs of various data such as the operation of devices and the actions of workers.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a demand for realizing an analytical device capable of effectively analyzing the cause of an abnormality that occurred in a production process using log data related to the production process.

[0005] The present disclosure has been made in view of the above problems, and an object thereof is to provide an analytical device capable of effectively analyzing the cause of an abnormality that occurred in a production process.

Means for Solving the Problems

[0006] In order to solve the above problems, the present disclosure employs the following configuration.

[0007] An analytical device relating to one aspect of this disclosure is an analytical device for analyzing the cause of an abnormality that occurs in a production process, comprising: an equipment abnormality data detection unit for detecting equipment abnormality data included in an equipment log, which is a log of data relating to at least one of the operation or state of the equipment in the production process; an operator abnormality data detection unit for detecting operator abnormality data included in an operator log, which is a log of data relating to at least one of the actions or biological information of an operator in the production process; a classification unit for classifying the cause of the abnormality into whether it is caused by the equipment or the operator, based on the detection result of the equipment abnormality data by the equipment abnormality data detection unit and the detection result of the operator abnormality data by the operator abnormality data detection unit; and an output unit for outputting the classification result by the classification unit.

[0008] According to the above configuration, the cause of an abnormality that occurs in the production process can be classified as either equipment-related or operator-related, thus enabling an effective analysis of the cause of the abnormality that occurs in the production process.

[0009] In the analytical apparatus relating to the above aspect, the sorting unit may, when both the apparatus abnormality data and the operator abnormality data are detected, sort the cause of the abnormality based on the time-series relationship between the apparatus abnormality data and the operator abnormality data.

[0010] According to the above configuration, the sorting unit sorts the causes of abnormalities that occurred in the production process based on the time-series relationship between equipment abnormality data and worker abnormality data, thus enabling accurate sorting.

[0011] In the analytical apparatus relating to the above aspect, the apparatus abnormality data detection unit and the operator abnormality data detection unit may assign an abnormality level to the apparatus abnormality data and the operator abnormality data, indicating the degree of abnormality according to the type and state of the apparatus abnormality data and the operator abnormality data, and the sorting unit may sort the cause of the abnormality based on the abnormality level.

[0012] According to the above configuration, the sorting unit sorts the causes of abnormalities that occurred in the production process based on the degree of abnormality, thus enabling accurate sorting.

[0013] In the analytical apparatus relating to the above aspect, an abnormality occurrence time detection unit may be further provided, which detects the time of occurrence of the abnormality from the apparatus log.

[0014] With the above configuration, the abnormality occurrence time detection unit in the analysis device automatically detects the time of occurrence of an abnormality that occurred in the production process from the device log, making it easier to analyze the cause of the abnormality that occurred in the production process.

[0015] The analysis device relating to the above aspect is an analysis device that can analyze the cause of an abnormality that occurred in the production process using a recorded video record or a continuous image record, and may further include a synchronized display unit that synchronizes and displays at least one of the device log or the worker log with the video record or the continuous image record, and an abnormality occurrence time reception unit that receives a user's specification of the time the abnormality occurred.

[0016] According to the above configuration, the synchronized display unit in the analysis device synchronizes and displays at least one of the device log or operator log with a video record or a continuous image record, allowing the user to easily specify the time of occurrence of an anomaly in the production process relative to the device log or operator log. This makes it easier to analyze the cause of an anomaly that occurred in the production process.

[0017] In the analytical apparatus relating to the above aspect, the apparatus abnormality data detection unit may detect the apparatus abnormality data from the apparatus log including the time before and after the occurrence of the abnormality, and the operator abnormality data detection unit may detect the operator abnormality data from the operator log including the time before and after the occurrence of the abnormality.

[0018] With the above configuration, the analytical instrument can efficiently detect both instrument abnormality data and operator abnormality data.

[0019] In addition, an analysis program according to an aspect of the present disclosure is an analysis program for causing a computer to function as the above analysis device, and causes the computer to function as the device abnormality data detection unit, the operator abnormality data detection unit, the classification unit, and the output unit.

[0020] According to the above configuration, it is possible to realize an analysis device that can effectively analyze the cause of an abnormality occurring in a production process.

Advantages of the Invention

[0021] According to the present disclosure, it is possible to provide an analysis device that can effectively analyze the cause of an abnormality occurring in a production process.

Brief Description of the Drawings

[0022] [Figure 1] It is a diagram for explaining a configuration example of a field network system including an analysis device according to an embodiment of the present disclosure. [Figure 2] It is a functional block diagram showing a configuration example of the above analysis device. [Figure 3] It is a flowchart showing an operation example of the above analysis device. [Figure 4] It is a diagram for explaining an example of a specific analysis result displayed on the display unit shown in FIG. 2.

Modes for Carrying Out the Invention

[0023] §1 Application Example Hereinafter, an example of a scene to which the present disclosure is applied will be described with reference to FIGS. 1 and 2. FIG. 1 is a diagram for explaining a configuration example of a field network system 1 including an analysis device 10 according to an embodiment of the present disclosure. FIG. 2 is a functional block diagram showing a configuration example of the above analysis device 10.

[0024] First, in order to facilitate understanding of the analytical apparatus 10 according to the embodiment of this disclosure, an overview of the field network system 1 including the analytical apparatus 10 will be described using Figure 1.

[0025] The field network system 1 is a system having a master-slave network and includes a master device 2. The field network system 1 includes a sensor 3, a robot 4, a servo 5, an image processing device 6, a vital sensor 7, and a logger 8 with recording function as slave devices connected to the master device 2 via a network 60. The master device 2 controls each of the above-mentioned slave devices by executing a control program.

[0026] In the field network system 1, the logger 8 with recording function collects device data relating to at least one of the operation or state of device D at each control cycle. Specifically, the logger 8 with recording function collects device logs from the sensor 3, robot 4, and servo 5, which constitute device D. The device log consists of a series of time-series data, time-stamped versions of the device data output by device D.

[0027] Furthermore, the logger 8 with recording function collects worker data relating to at least one of the worker S's actions or biological information at each control cycle. Specifically, the logger 8 with recording function collects worker logs of worker S from the image processing device 6 and the vital sensor 7. The worker log consists of a series of time-series data of worker S, time-stamped with the worker data mentioned above.

[0028] A camera 6A is connected to the image processing device 6. The image processing device 6 processes the images of the device D and the worker S captured by the camera 6A to obtain determination results regarding the actions of the device D and the worker S. The image processing device 6 then outputs the obtained determination results as a device log and a worker log to the logger 8 with recording function.

[0029] The analysis device 10 of this embodiment is connected to a logger 8 with a recording function and receives device logs and worker logs from the logger 8. The analysis device 10 of this embodiment analyzes the received device logs and worker logs to analyze the cause of abnormalities that occurred in the production process to which the field network system 1 is applied. Hereinafter, "abnormalities that occurred in the production process" will also be referred to as "abnormal events".

[0030] The analysis device 10 of this embodiment includes a device abnormality data detection unit 14a, an operator abnormality data detection unit 14b, a sorting unit 14c, and a display unit 12. The device abnormality data detection unit 14a detects device abnormality data included in the device log based on the device log from the logger 8 with recording function. Here, device abnormality data refers to device data that deviates from the normal device data. Device abnormality data may include, for example, device data of instantaneous fluctuations that are unrelated to the abnormal event, and device data that deviates from the normal state regardless of the abnormal event. In other words, device abnormality data refers to device data that may be related to an abnormality (abnormal event) caused by the device that occurred in the production process.

[0031] The worker anomaly data detection unit 14b detects worker anomaly data contained in the worker log from the logger 8 with recording function, based on the worker log. Here, worker anomaly data is worker data that deviates from normal worker data. Worker anomaly data may include, for example, worker data of instantaneous fluctuations that are unrelated to the anomaly event, or worker data that deviates from the normal state regardless of the anomaly event. In other words, worker anomaly data is worker data that may be related to an anomaly (anomaly event) caused by a worker that occurred in the production process.

[0032] The sorting unit 14c sorts the cause of an anomaly that occurred in the production process into whether it was caused by the equipment or by the worker, based on the detection results of equipment anomaly data by the equipment anomaly data detection unit 14a and the detection results of worker anomaly data by the worker anomaly data detection unit 14b. The display unit 12 is an example of the output unit of the analysis device 10 and displays the sorting results by the sorting unit 14c.

[0033] Therefore, according to this embodiment, the analysis device 10 can determine whether an abnormality that occurred in the production process is caused by at least one of the device or the worker, using the detection results of the device abnormality data and the detection results of the worker abnormality data. The display unit 12 outputs the results of the sorting by the sorting unit 14c, so that the user can recognize the sorting results and know whether the cause of the abnormality that occurred in the production process is due to the device or the worker.

[0034] §2 Example Configuration <Configuration of Field Network System 1> The following describes an example configuration of the field network system 1 and the analytical device 10 of this embodiment. The following description will focus on the case where the field network system 1 of this embodiment is applied to an industrial network installed in a factory production process.

[0035] Master device 2 is configured, for example, using a programmable logic controller (PLC). Master device 2 controls the entire field network system 1.

[0036] In network 60, a network system conforming to standards such as EtherCAT® (Ethernet for Control Automation Technology) or EtherNet / IP can be applied as the network system between the master device 2 and the sensor 3, robot 4, servo 5, image processing device 6, vital sensor 7, and logger 8 with recording function (ETHERNET: registered trademark, Ethernet: registered trademark).

[0037] Sensor 3 may include various sensors capable of detecting at least one of the operation or state of device D. Specifically, sensor 3 can be, for example, a vibration sensor. If such a vibration sensor is provided, devices such as servo 5 can detect vibrations and even detect abnormalities caused by vibrations. Sensor 3 may also include multiple sensors that detect different targets. Sensor 3 outputs its detection results as a device log to logger 8 with recording function.

[0038] Robot 4 is a robot included in device D and includes movable parts such as a robotic arm. Robot 4 outputs a device log to the logger 8 with recording function, which includes data showing the results of the robot 4's operation.

[0039] Servo 5 is a drive member (e.g., a servo motor) for driving the drive unit included in device D. Servo 5 outputs a device log to the logger 8 with recording function, which includes data showing the operation results of the servo 5.

[0040] The image processing device 6 includes, for example, a CPU (Central Processing Unit) or ASIC (Application Specific Integrated Circuit), RAM (Random Access Memory), and ROM (Read Only Memory). The image processing device 6 performs predetermined image processing on the image of the worker S captured by the camera 6A to determine the actions of the worker S.

[0041] Specifically, the image processing device 6 acquires data indicating, for example, the position of the worker S's gaze, hand movements, movement path, and blink count, based on the image capture results of the camera 6A, as discrimination result data. The image processing device 6 then outputs the acquired data as a worker log to the logger 8 with recording function. In other words, the image processing device 6 functions as a sensor that detects the movements of the worker S together with the camera 6A.

[0042] The vital sensor 7 is, for example, a head-mounted display worn by worker S, which acquires data indicating changes such as worker S's eye movements and outputs this acquired data as a worker log to the logger 8 with recording function. Alternatively, the vital sensor 7 may include, for example, a wearable sensor attached to worker S. The vital sensor 7 acquires data indicating worker S's biological information and outputs this acquired data as a worker log to the logger 8 with recording function.

[0043] Specifically, the type of vital sensor 7 is not particularly limited, as long as it can measure physiological parameters relating to at least one of the worker S's perceptual activity and physical activity, and may be appropriately selected depending on the embodiment.

[0044] The behavior of sensory organs is expressed in various ways, such as electroencephalogram (EEG), cerebral blood flow, pupil diameter, gaze direction, facial expressions, voice, electrocardiogram (ECG), blood pressure, electromyogram (EMG), and galvanic skin reflex (GSR).

[0045] Therefore, one or more sensors for measuring perceptual activity may include, for example, an electroencephalograph (EEG), a magnetoencephalograph (MEG), a magnetic resonance imaging system configured to image blood flow related to brain activity using functional magnetic resonance imaging (fMRI), a brain activity measurement device configured to measure cerebral blood flow using functional near-infrared spectroscopy (fNIRS), a gaze sensor configured to measure pupil diameter and gaze direction, an electrooculography sensor, a microphone, an electrocardiograph, a blood pressure monitor, an electromyograph, a skin electroreceptor, a camera, or a combination thereof.

[0046] On the other hand, physical activity manifests itself in the musculoskeletal system, such as fingers, hands, legs, neck, waist, joints, and muscles. Therefore, one or more sensors for measuring physical activity may include, for example, a camera, motion capture device, load cell, or a combination thereof.

[0047] The logger 8 with recording function is configured, for example, using a factory drive recorder. The logger 8 also has a camera 9a connected to it, which images the device D and stores the image results in a storage means (not shown) as appropriate. Furthermore, the logger 8 with recording function has a camera 9b connected to it, which images the worker S and stores the image results in a storage means (not shown) as appropriate. The logger 8 then outputs the stored video or continuous image data of the device D and worker S to the analysis device 10.

[0048] In the above description, a logger 8 with recording function using a factory drive recorder was described, but the field network system 1 of this embodiment is not limited to this. In other words, the field network system 1 of this embodiment is not limited in any way to any device (logger) that can collect device logs of equipment D in the production process and worker logs of worker S and output them to the analysis device 10.

[0049] <Specific configuration of the analytical device 10> The analytical device 10 is typically implemented using a general-purpose computer; therefore, an analytical program that enables the general-purpose computer to function as an analytical device is executed on the general-purpose computer.

[0050] The analysis device 10 is connected to the logger 8 with recording function via a communication cable, such as a USB (Universal Serial Bus) cable. The analysis device 10 is an information processing device that analyzes the cause of abnormalities that occur in the production process based on the monitoring results of the device D installed in the production process and the monitoring results of the worker S performing work in the production process.

[0051] The analysis device 10 comprises a communication unit 11, a display unit 12, a storage unit 13, and a control unit 14. The communication unit 11 is a communication interface that performs bidirectional communication with the logger 8 with recording function. The communication unit 11 receives device logs and operator logs from the logger 8 with recording function. The communication unit 11 also receives video records or continuous image records from at least one of the cameras 9a or 9b from the logger 8 with recording function.

[0052] The display unit 12 includes a display panel such as an LCD panel and displays information such as the device log, operator log, or the results of the sorting described above, according to the instructions of the control unit 14. Furthermore, by providing a touch panel function in the display unit 12, it can function as an operation reception unit that receives user instructions, together with operation buttons (not shown) provided on the analysis device 10.

[0053] The storage unit 13 is configured using storage means such as non-volatile memory. The storage unit 13 stores device logs and worker logs received through the communication unit 11. The storage unit 13 also stores the video records or continuous image records received through the communication unit 11. Furthermore, the storage unit 13 stores data such as the sorting results by the sorting unit 14c.

[0054] Furthermore, the memory unit 13 can store device data for device D under normal conditions and worker data for worker S under normal conditions. This device data for device D under normal conditions and worker data for worker S under normal conditions can be downloaded to the analysis device 10 as appropriate, for example, via the communication cable, in accordance with the production process.

[0055] The control unit 14 is a functional block that includes, for example, a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), etc., and controls each component according to information processing. The control unit 14 includes the above-mentioned device abnormality data detection unit 14a, operator abnormality data detection unit 14b, and sorting unit 14c. As will be described in detail later, the sorting unit 14c sorts the cause of the abnormality based on the degree of abnormality assigned to the device abnormality data from the device abnormality data detection unit 14a and the degree of abnormality assigned to the operator abnormality data from the operator abnormality data detection unit 14b.

[0056] Furthermore, the control unit 14 includes an abnormality occurrence time detection unit 14d, a synchronization display unit 14e, and an abnormality occurrence time reception unit 14f. The abnormality occurrence time detection unit 14d is a functional block that detects the time of an abnormality from the device log stored in the storage unit 13. The synchronization display unit 14e is a functional block that synchronizes at least one of the device log or operator log stored in the storage unit 13 with a video record or a continuous image record and displays them on the display unit 12. The abnormality occurrence time reception unit 14f is a functional block that accepts the user's specification of the time of an abnormality by having the user operate the above operation reception unit.

[0057] In addition to the above explanation, for example, instead of a general-purpose computer, an HMI (Human Machine Interface) can be used as the analysis device 10. In this case, information such as the analysis results of the analysis device 10 will be displayed on the display unit of the HMI.

[0058] §3 Example of Operation <Operation of analyzer 10> Next, with reference to Figure 3, the operation of the analysis device 10 of this embodiment will be described in detail. Figure 3 is a flowchart showing an example of the operation of the analysis device 10. In the following description, the case in which the synchronization display unit 14e of the analysis device 10 displays the device log from the logger with recording function 8, the operator log, the video record from camera 9a, and the video record from camera 9b on the display unit 12 will be mainly described.

[0059] As shown in step S1 of Figure 3, in the analysis device 10 of this embodiment, the user first detects the occurrence of an abnormal event in the device D (i.e., an abnormality in the production process) by viewing the display unit 12. Then, the user notifies the analysis device 10 of the abnormality by operating the operation reception unit.

[0060] Specifically, in the analysis device 10, the synchronization display unit 14e synchronizes and displays at least one of the device log or operator log, along with a video record or a continuous image record, on the display unit 12. When the user views the video record or continuous image record displayed on the display unit 12 and determines that an abnormal event has occurred, the abnormal event time reception unit 14f accepts the user's specification of the time the abnormal event occurred.

[0061] In addition to the above explanation, in step S1, the analysis device 10 may also be configured such that the abnormal occurrence time detection unit 14d detects the time of the abnormality from the device log. Specifically, the abnormal occurrence time detection unit 14d determines whether or not an abnormal event has occurred based on the device data for each variable of device D described later, which is included in the device log. If the abnormal occurrence time detection unit 14d determines that an abnormal event has occurred, it may then detect the time of the abnormality.

[0062] Next, in the analysis device 10, the device abnormality data detection unit 14a detects device abnormality data from the device log, including the time before and after the occurrence of the abnormality. In other words, in the analysis device 10, the device abnormality data detection unit 14a determines whether the waveform of the device data related to device D is different from the usual waveform (step S2). Specifically, in step S2, the device abnormality data detection unit 14a determines whether the waveform of the device data is different from the usual waveform by comparing it with, for example, the normal device data stored in the storage unit 13. If the device abnormality data detection unit 14a determines that it is not different from the usual waveform (NO in step S2), the process proceeds to step S4.

[0063] On the other hand, if the device abnormality data detection unit 14a determines that the waveform is different from the usual one (YES in step S2), the device abnormality data detection unit 14a determines that it has detected device abnormality data and registers in the storage unit 13 that device abnormality data has occurred (step S3). Specifically, the device abnormality data detection unit 14a causes the storage unit 13 to record the time of occurrence of the abnormality in which the device abnormality data occurred, or the time of occurrence along with the device abnormality data, including the device abnormality data, for a predetermined period before and after the time of occurrence of the abnormality, for example, the device log device data for a predetermined period. In other words, the storage unit 13 records that abnormal data has occurred with respect to the device data for the variable in which the abnormality was detected (for example, various output data of device D).

[0064] Furthermore, in step S3, the device abnormality data detection unit 14a determines an abnormality level indicating the degree of abnormality according to the type and state of the registered device abnormality data, and stores the determined abnormality level in the storage unit 13 along with the occurrence of the device abnormality data. In this step S3, the analysis device 10 completes the abnormality analysis of the device data.

[0065] Next, the worker abnormality data detection unit 14b detects worker abnormality data from the worker log, including the time before and after the occurrence of the abnormality. In other words, the worker abnormality data detection unit 14b determines whether the waveform of the worker data relating to worker S is different from the usual waveform (step S4). Specifically, in step S4, the worker abnormality data detection unit 14b determines whether the waveform of the worker data is different from the usual waveform by, for example, comparing the worker data displayed on the display unit 12 with the normal worker data stored in the storage unit 13. If the worker abnormality data detection unit 14b determines that the waveform is not different from the usual waveform (NO in step S4), the process proceeds to step S6.

[0066] More specifically, the worker abnormality data detection unit 14b extracts, for example, the gaze position information of worker S around the time the abnormality detected in step S1 occurred from the worker log from the logger 8 with recording function. Then, the worker abnormality data detection unit 14b may determine whether the waveform of the worker data is different from the usual waveform by determining whether the variation in gaze position per unit time is greater than a predetermined average value.

[0067] Furthermore, the worker abnormality data detection unit 14b may, for example, extract the location information of worker S around the time of the abnormality detected in step S1 from the worker log from the logger 8 with recording function. The worker abnormality data detection unit 14b may then determine whether the waveform of the worker data is different from the usual waveform by determining whether worker S is in a different position than the normal movement path.

[0068] Furthermore, the worker abnormality data detection unit 14b may, for example, extract the number of blinks of worker S around the time of the abnormality detected in step S1 from the worker log from the logger 8 with recording function. The worker abnormality data detection unit 14b may then determine whether the waveform of the worker data is different from the usual waveform by determining whether the number of blinks per unit time of worker S has increased compared to the average value.

[0069] On the other hand, if the worker abnormality data detection unit 14b determines that the waveform is different from the usual one (YES in step S4), the worker abnormality data detection unit 14b determines that it has detected worker abnormality data and registers in the storage unit 13 that worker abnormality data has occurred (step S5). Specifically, the worker abnormality data detection unit 14b causes the storage unit 13 to record the time of occurrence of the abnormality in which the worker abnormality data occurred, or the time of occurrence along with the worker abnormality data, including the worker abnormality data, for a predetermined period before and after the time of occurrence of the abnormality. In other words, the storage unit 13 records that abnormal data has occurred with respect to the worker data for the variable in which the abnormality was detected (for example, the variation in gaze position per unit time, movement path position, and number of blinks per unit time).

[0070] In addition to the above explanation, the abnormal occurrence time reception unit 14f may also accept the user's specification of the time of the abnormal occurrence and register it in the storage unit 13 for storage.

[0071] Furthermore, in step S5, the worker abnormality data detection unit 14b determines an abnormality level indicating the degree of abnormality according to the type and state of the registered worker abnormality data, and stores the determined abnormality level in the storage unit 13 along with the occurrence of the worker abnormality data. In this step S5, the analysis device 10 completes the abnormality analysis of the worker data.

[0072] Next, the sorting unit 14c refers to the storage unit 13 to determine whether or not an abnormality has been registered (step S6). If the sorting unit 14c determines that no abnormality has been registered (NO in step S6), the sorting unit 14c displays a message on the display unit 12 prompting the user to visually confirm that there are no abnormalities (step S7). Then, the analyzer 10 terminates the analysis process.

[0073] On the other hand, when the sorting unit 14c determines that an abnormality has been registered (YES in step S6), the sorting unit 14c displays the location where the abnormality occurred as a mark on the display unit 12 based on the time of the abnormality registered in the storage unit 13 (step S8).

[0074] Next, the sorting unit 14c refers to the storage unit 13 to determine whether both the occurrence of device abnormality data and the occurrence of worker abnormality data are registered at the same time of occurrence of the abnormality (step S9). If the sorting unit 14c determines that neither the occurrence of device abnormality data nor the occurrence of worker abnormality data is registered (NO in step S9), the process proceeds to step S12.

[0075] On the other hand, if the sorting unit 14c determines that both the occurrence of equipment abnormality data and the occurrence of worker abnormality data have been registered (YES in step S9), the sorting unit 14c estimates the data among the registered abnormalities that is most likely to be the root cause (cause) of the abnormality (abnormal event) that actually occurred in the production process (step S10).

[0076] Specifically, the sorting unit 14c may, for example, estimate data that is highly likely to be the root cause based on the chronological relationship of the occurrence times of the abnormalities, that is, the time-series relationship between the equipment abnormality data and the worker abnormality data, which are registered in the storage unit 13. In other words, the sorting unit 14c may estimate the cause of the abnormal event by comparing the occurrence time of the equipment abnormality data and the occurrence time of the worker abnormality data, and selecting the data that is registered earlier in the storage unit 13 and has an earlier occurrence time from among the equipment abnormality data and the worker abnormality data.

[0077] Furthermore, the sorting unit 14c may, for example, use the abnormality scores registered in the storage unit 13 to estimate data that is likely to be the root cause. That is, the sorting unit 14c may compare the abnormality scores assigned to the equipment abnormality data with the abnormality scores assigned to the worker abnormality data, and estimate the data with a higher abnormality score (abnormal) among the equipment abnormality data and worker abnormality data as the cause of the abnormal event.

[0078] Furthermore, the sorting unit 14c may estimate data that is highly likely to be the root cause by referring to a sorting table that is pre-stored in the storage unit 13 and is patterned by combining variables of abnormal events and their causes. In other words, the sorting unit 14c may estimate the cause of the abnormal event by referring to the sorting table based on the abnormality variables included in the equipment abnormality data and the abnormality variables included in the worker abnormality data.

[0079] Next, the sorting unit 14c registers the estimated data as a root anomaly in the storage unit 13 (step S11). Subsequently, the sorting unit 14c determines whether the root anomaly registered in the storage unit 13 is device anomaly data or not (step S12). If the sorting unit 14c determines that the root anomaly registered in the storage unit 13 is not device anomaly data (NO in step S12), the process proceeds to step S15.

[0080] On the other hand, when the sorting unit 14c determines that the root anomaly registered in the storage unit 13 is device anomaly data (YES in step S12), the sorting unit 14c displays the parameters and time information of the device anomaly data on the display unit 12 (step S13). Furthermore, the sorting unit 14c displays the anomaly information of the device data and a warning message on the display unit 12 (step S14).

[0081] Next, the sorting unit 14c determines whether the root anomaly registered in the storage unit 13 is operator anomaly data (step S15). If the sorting unit 14c determines that the root anomaly registered in the storage unit 13 is not operator anomaly data (NO in step S15), the sorting unit 14c determines that the root anomaly, i.e., the anomaly incident, is an anomaly caused by the device, and the analysis device 10 terminates the analysis process.

[0082] On the other hand, when the sorting unit 14c determines that the root anomaly registered in the storage unit 13 is worker anomaly data (YES in step S15), the sorting unit 14c determines that the root anomaly, i.e., the anomaly incident, is an anomaly caused by a worker. The sorting unit 14c displays the worker S in which the anomaly occurred, the anomaly condition, and the time information on the display unit 12 (step S16). Furthermore, the sorting unit 14c displays the anomaly information of the worker data and a warning message on the display unit 12 (step S17).

[0083] As described above, in step S10, the sorting unit 14c estimates the data that is most likely to be the root cause of the abnormality that occurred in the production process among the registered abnormalities. This allows the sorting unit 14c to determine whether the abnormality is attributable to at least one of the equipment D or the worker S.

[0084] Next, we will explain the specific display operation of the display unit 12 using Figure 4. Figure 4 is a diagram illustrating an example of the specific analysis results displayed on the display unit 12 shown in Figure 2.

[0085] As shown in Figure 4, when an abnormality is detected in the production process, the display unit 12 displays a warning of the abnormality in the display area 12a. In addition, the display unit 12 displays the video record from camera 9a in the display area 12b, and the video record from camera 9b in the display area 12c.

[0086] Furthermore, the display unit 12 displays device data 1 to 4 included in the device log from the logger with recording function 8, and worker data 1 to 4 included in the worker log from the logger with recording function 8, along with the time, in the display area 12d. Then, when the operation of step S8 is performed, the display unit 12 marks the location where the abnormality occurred, for example, as shown by circle K, in the display area 12d.

[0087] Furthermore, in the display unit 12, for example, when the operation in step S16 is performed, the time of the abnormality is displayed in the display area 12e, and the operator X, the details of the abnormal condition, and the time of the abnormality are displayed in the display area 12f.

[0088] <Effects and Actions> As described above, the analysis device 10 of this embodiment includes an equipment abnormality data detection unit 14a for detecting equipment abnormality data and an operator abnormality data detection unit 14b for detecting operator abnormality data. The analysis device 10 also includes a sorting unit 14c that sorts the cause of the abnormality into whether it is equipment-related or operator-related based on the detection results of equipment abnormality data by the equipment abnormality data detection unit 14a and the detection results of operator abnormality data by the operator abnormality data detection unit 14b, and a display unit 12 that displays the sorting results by the sorting unit 14c.

[0089] With the above configuration, the analysis device 10 of this embodiment can determine, when an abnormality occurs in the production process, whether the abnormality is caused by at least one of the device D or the worker S. Furthermore, in the analysis device 10 of this embodiment, the display unit 12 outputs the results of the separation performed by the separation unit 14c, so that the user can analyze the cause of the abnormality in the production process, whether it is caused by the device D or the worker S.

[0090] Furthermore, in the analysis device 10 of this embodiment, the sorting unit 14c sorts the cause of the abnormality that occurred in the production process (root cause) in step S10 based on the time-series relationship between the device abnormality data and the operator abnormality data, so sorting can be performed with high accuracy.

[0091] Furthermore, in the analytical apparatus 10 of this embodiment, the sorting unit 14c sorts the cause of the abnormality (root cause) that occurred in the production process based on the degree of abnormality in step S10, so sorting can be performed with high accuracy.

[0092] Furthermore, in the analysis device 10 of this embodiment, as shown in step S1, the abnormality occurrence time detection unit 14d automatically detects the time of occurrence of an abnormality that occurred in the production process from the device log, making it easier to analyze the cause of the abnormality that occurred in the production process.

[0093] Furthermore, in the analysis device 10 of this embodiment, the synchronization display unit 14e synchronizes and displays at least one of the device log or operator log with the video record or continuous image record, as shown in Figure 4. This allows the user to easily specify the time of occurrence of an anomaly in the production process relative to the device log or operator log. As a result, the analysis device 10 of this embodiment makes it easier to analyze the cause of an anomaly that occurred in the production process.

[0094] Furthermore, in the analysis device 10 of this embodiment, the device abnormality data detection unit 14a detects device abnormality data from the device log, including the time before and after the time the abnormality occurred, in step S3. In addition, the worker abnormality data detection unit 14b detects worker abnormality data from the worker log, including the time before and after the time the abnormality occurred, in step S5. As a result, the analysis device 10 of this embodiment can efficiently detect both device abnormality data and worker abnormality data.

[0095] [Examples of implementation using software] The functional blocks of the analysis device 10 (in particular, the control unit 14) may be implemented by logic circuits (hardware) formed on an integrated circuit (IC chip) or the like, or by software.

[0096] In the latter case, the control unit 14 includes a computer that executes instructions for a program, which is software that implements each function. This computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. The object of this disclosure is achieved when the processor in the computer reads the program from the recording medium and executes it.

[0097] As the processor mentioned above, for example, a CPU (Central Processing Unit) can be used. As the recording medium, a "non-temporary, tangible medium" such as ROM (Read Only Memory), as well as magnetic disks, cards, semiconductor memory, programmable logic circuits, etc., can be used. Furthermore, a RAM (Random Access Memory) for deploying the program may also be provided.

[0098] Furthermore, the above program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast waves). In one aspect of the present invention, the above program can also be realized in the form of a data signal embedded in a carrier wave, which is embodied by electronic transmission.

[0099] This disclosure is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in each embodiment are also included in the technical scope of this disclosure. [Explanation of Symbols]

[0100] 3. Sensors (devices) 4. Robots (devices) 5. Servos (devices) 6. Image processing device (device) 10 Analyzer 12 Display Unit (Output Unit) 14a Device Anomaly Data Detection Unit 14b Operator Anomaly Data Detection Unit 14c Separation section 14d Abnormal Time Detection Unit 14e Synchronization display section 14f Anomaly Occurrence Time Reception Department D equipment S Worker

Claims

1. An analytical device for analyzing the cause of abnormalities that occur in the production process, A device abnormality data detection unit detects device abnormality data included in the device log, which is a log of data relating to at least one of the operation or status of the device in the aforementioned production process. A worker anomaly data detection unit detects worker anomaly data included in the worker log, which is a log of data relating to at least one of the worker's actions or biometric information in the aforementioned production process, A sorting unit that, based on the detection results of the device abnormality data by the device abnormality data detection unit and the detection results of the worker abnormality data by the worker abnormality data detection unit, sorts the cause of the abnormality into whether it is caused by the device or the worker. The system includes an output unit that outputs the results of the sorting performed by the sorting unit, The sorting unit is an analytical device that, when both the device abnormality data and the operator abnormality data are detected, sorts the cause of the abnormality based on the time-series relationship between the device abnormality data and the operator abnormality data.

2. An analytical device for analyzing the cause of abnormalities that occur in the production process, A device abnormality data detection unit detects device abnormality data included in the device log, which is a log of data relating to at least one of the operation or status of the device in the aforementioned production process. A worker anomaly data detection unit detects worker anomaly data included in the worker log, which is a log of data relating to at least one of the worker's actions or biometric information in the aforementioned production process, A sorting unit that, based on the detection results of the device abnormality data by the device abnormality data detection unit and the detection results of the worker abnormality data by the worker abnormality data detection unit, sorts the cause of the abnormality into whether it is caused by the device or the worker. The system includes an output unit that outputs the results of the sorting performed by the sorting unit, The device abnormality data detection unit and the operator abnormality data detection unit are: An abnormality level indicating the degree of abnormality is assigned to the equipment abnormality data and the worker abnormality data, according to the type and state of the equipment abnormality data and the worker abnormality data. The sorting unit is an analytical device that sorts the cause of the abnormality based on the degree of abnormality.

3. The analytical apparatus according to claim 1 or 2, further comprising an abnormality occurrence time detection unit that detects the time of occurrence of the abnormality from the device log.

4. Furthermore, an analytical device capable of analyzing the cause of an abnormality that occurred in the production process using recorded video records or continuous image records, A synchronized display unit that synchronizes and displays at least one of the device log or the operator log with the video record or the continuous image record, The analytical apparatus according to claim 1 or 2, further comprising an abnormality occurrence time reception unit that receives a user's specification of the time of occurrence of the abnormality.

5. The device abnormality data detection unit detects the device abnormality data from the device log, including the time before and after the occurrence of the abnormality. The analysis apparatus according to claim 3, wherein the worker abnormality data detection unit detects the worker abnormality data from the worker log, including the time before and after the occurrence of the abnormality.

6. An analysis program for causing a computer to function as an analysis apparatus according to claim 1 or 2, wherein the computer functions as the apparatus abnormality data detection unit, the operator abnormality data detection unit, the sorting unit, and the output unit.

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