Monitoring device, monitoring method and monitoring program

Abstract

A monitoring device comprises: an abnormality value calculation unit which calculates abnormality values based on machine state data of each of a plurality of machines; a priority order determination unit which determines a priority order for the plurality of machines on the basis of the abnormality values calculated by the abnormality value calculation unit; and a display control unit which displays information relating to the plurality of machines according to the priority order determined by the priority order determination unit.

Description

Monitoring device, monitoring method, and monitoring program

[0001] The present disclosure relates to a monitoring device, a monitoring method, and a monitoring program for monitoring the status of multiple machines.

[0002] Conventionally, in monitoring systems that monitor multiple machines, when analyzing the details of a fault when an alarm occurs, there has been a demand for the system to quickly grasp the details of the failure by using actual machine operation data and historical data.

[0003] In this regard, Japanese Patent Application Laid-Open No. 2018-5833 (Patent Document 1) proposes a method in which data regarding a manufacturer's machines installed in a factory is continuously collected, stored, and monitored centrally on the monitoring device side at predetermined intervals.

[0004] Japanese Patent Application Laid-Open No. 2018-5833

[0005] On the other hand, even if a monitoring device centrally collects data on multiple machines, it is unclear which machine should be given priority.

[0006] The present disclosure is intended to solve the above-mentioned problems, and aims to provide a monitoring device, a monitoring method, and a monitoring program that are capable of more efficiently monitoring the status of multiple machines.

[0007] A monitoring device according to an example of the present disclosure includes an abnormal value calculation unit that calculates an abnormal value based on status data of each of a plurality of machines, a priority determination unit that determines priorities for the plurality of machines based on the abnormal values ​​calculated by the abnormal value calculation unit, and a display control unit that displays information about the plurality of machines in accordance with the priorities determined by the priority determination unit. With this configuration, the priorities for the plurality of machines are determined based on the abnormal values, and information about the plurality of machines is displayed in accordance with the determined priorities, making it possible to efficiently monitor the status of the plurality of machines.

[0008] The status data includes a plurality of variable data. The abnormal value calculation unit calculates an abnormal value based on the Mahalanobis distance of the plurality of variable data. With this configuration, priorities for the plurality of machines are determined based on the abnormal values ​​calculated based on the Mahalanobis distance, and information about the plurality of machines is displayed according to the determined priorities, making it possible to efficiently monitor the status of the plurality of machines.

[0009] The monitoring device further includes a contribution rate calculation unit that calculates the contribution rate of each variable data to the abnormal value, and an abnormality estimation unit that estimates an abnormality based on the calculated contribution rates. With this configuration, the contribution rates to the abnormal value are calculated and an abnormality is estimated based on the calculated contribution rates, making it possible to efficiently monitor the states of multiple machines.

[0010] The monitoring device further includes a maintenance suggestion unit that provides information regarding maintenance according to the estimated abnormality. With this configuration, the contribution rate to the abnormal value is calculated, and the abnormality is estimated according to the calculated contribution rate, making it possible to efficiently monitor the states of multiple machines.

[0011] The priority order determination unit determines priorities for the multiple machines based on the calculated abnormal values ​​and the maintenance-related information corresponding to each of the multiple machines. With this configuration, the priorities for the multiple machines are determined based on the abnormal values ​​and the maintenance-related information, and information about the multiple machines is displayed in accordance with the determined priorities, making it possible to efficiently monitor the status of the multiple machines.

[0012] The maintenance-related information includes at least one of the following information: maintenance history, maintenance personnel, information on availability of maintenance parts, and maintenance area. According to this configuration, by including at least one of the following information as the maintenance-related information: maintenance history, maintenance personnel, information on availability of maintenance parts, and maintenance area, it is possible to efficiently monitor the status of multiple machines.

[0013] The priority determination unit calculates the difference between the abnormal value and a reference threshold value, and determines the priorities of the multiple machines based on the calculated difference value. With this configuration, the priorities of the multiple machines are determined based on the difference between the abnormal value and the reference threshold value, and information about the multiple machines is displayed in accordance with the determined priorities, making it possible to efficiently monitor the status of the multiple machines.

[0014] The reference threshold value is set so that it can be changed for each of the multiple machines. With this configuration, the threshold value is set so that it can be adjusted to suit the characteristics of each individual machine.

[0015] A monitoring method according to an example of the present disclosure includes the steps of calculating an abnormal value based on status data of each of a plurality of machines, determining priorities for the plurality of machines based on the calculated abnormal value, and displaying information about the plurality of machines in accordance with the determined priorities. With this configuration, the priorities for the plurality of machines are determined based on the abnormal value, and information about the plurality of machines is displayed in accordance with the determined priorities, making it possible to efficiently monitor the status of the plurality of machines.

[0016] A monitoring program according to an example of the present disclosure is a monitoring program that causes a computer to execute a monitoring method, and causes the computer to execute the steps of: calculating an abnormal value based on status data of each of a plurality of machines; determining priorities for the plurality of machines based on the calculated abnormal value; and displaying information about the plurality of machines in accordance with the determined priorities. With this configuration, the priorities for the plurality of machines are determined based on the abnormal value, and information about the plurality of machines is displayed in accordance with the determined priorities, making it possible to efficiently monitor the status of the plurality of machines.

[0017] According to the monitoring device, monitoring method, and monitoring program disclosed herein, it is possible to more efficiently monitor the status of multiple machines.

[0018] FIG. 1 is a diagram illustrating a configuration of a monitoring system 1 according to a first embodiment. FIG. 1 is a diagram illustrating a configuration of a data collecting device 10 according to the first embodiment. FIG. 2 is a diagram illustrating a configuration of a monitoring device 100 according to the first embodiment. FIG. 3 is a diagram illustrating functional blocks of the monitoring system according to the first embodiment. FIG. 4 is a diagram illustrating status data acquired by a device data acquiring unit 22 according to the first embodiment. FIG. 5 is a diagram illustrating a calculation process of an abnormal value calculating unit 20 according to the first embodiment. FIG. 6 is another diagram illustrating an abnormal value in the abnormal value calculating unit 20 according to the first embodiment. FIG. 7 is a diagram illustrating processing of the monitoring device 100 according to the first embodiment. FIG. 8 is a diagram illustrating an example of a monitoring screen displayed on a display device 70 according to the first embodiment. FIG. 9 is a diagram illustrating functional blocks of a monitoring system according to a second embodiment. FIG. 10 is a diagram illustrating contribution rates of a plurality of variables calculated by a contribution rate calculating unit 206 according to the second embodiment. FIG. 11 is a diagram illustrating presentation of information related to maintenance in a maintenance suggesting unit 209 according to the second embodiment. FIG. 12 is a diagram illustrating an example of a monitoring screen displayed on a display device 70 according to a third embodiment.

[0019] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail with reference to the accompanying drawings, in which the same or corresponding parts in the drawings are designated by the same reference numerals and the description thereof will not be repeated.

[0020] (Embodiment 1) (Overall Configuration) The configuration of a monitoring system 1 according to embodiment 1 will be described. Fig. 1 is a diagram illustrating the configuration of the monitoring system 1 according to embodiment 1. Referring to Fig. 1, the monitoring system 1 includes a plurality of devices 2A to 2N, data collection devices 10A to 10N (hereinafter also collectively referred to as data collection devices 10) provided corresponding to the devices 2A to 2N (hereinafter also collectively referred to as devices 2), respectively, and a monitoring device 100.

[0021] The devices 2A-2N may be, for example, devices for manufacturing something, assembly devices, etc., and may be composed of one mechanism or multiple mechanisms. Note that, for example, the devices 2A-2N do not need to be the same device and may be devices of different types. The data collection device 10 collects status data for the device 2. The status data may include any type of data related to the status of each mechanism constituting the device 2. Each mechanism may be composed of at least one device, such as a conveyor, a robot arm, a servo motor, a cylinder (such as a molding machine), a suction pad, a cutting device, or a sealing device, or part of such a device. In addition to devices that perform some physical operation as described above, each mechanism may also include devices that perform internal processing, such as devices that detect information using various sensors, devices that acquire data from various sensors, devices that detect information from the acquired data, and devices that process the acquired data.

[0022] As a specific example, if the device 2 is an injection molding machine, the data collection device 10 may collect status data from a temperature sensor that detects the heater temperature, a pressure sensor that detects the injection pressure, a pressure sensor that detects the resin pressure, a pressure sensor that detects the pump pressure, and a temperature sensor that detects the mold temperature. The status data may be, for example, data indicating at least one of torque, speed, acceleration, temperature, current, voltage, air pressure, pressure, flow rate, position, dimensions (height, length, width), and area. Such status data can be obtained using at least one measurement device, such as a known sensor or camera. For example, flow rate can be obtained using a flow sensor. Position, dimensions, and area can be obtained using an image sensor. The status data may also be data input to each mechanism, such as input command values ​​and control values, indicating the status of each mechanism. The status data may be composed of data obtained from one or more measurement devices. The status data may be the data obtained directly from the measurement device, or data that can be obtained by applying some processing to the data obtained from the measurement device, such as position data obtained from image data. In this example, a configuration in which a data collection device 10 is provided corresponding to one device 2 is described, but this configuration is not limited to this, and a method in which multiple devices 2 are connected to the data collection device 10 and status data is collected may also be adopted.

[0023] The data collection devices 10A to 10N collect status data from the devices 2A to 2N and output the collection results to the monitoring device 100. The data collection devices 10A to 10N collect status data from the devices 2A to 2N, calculate abnormal values ​​for each of the devices 2A to 2N, and output the calculation results to the monitoring device 100 as collection results.

[0024] The monitoring device 100 aggregates the data sent from the data collection devices 10A to 10N and displays, as an example, the results based on the status of each of the devices 2A to 2N on a display device.

[0025] FIG. 2 is a diagram illustrating the configuration of a data collection device 10 according to the first embodiment. Referring to FIG. 2, the data collection device 10 typically has a structure conforming to a general-purpose computer architecture. Specifically, the data collection device 10 includes a processor 11, such as a central processing unit (CPU) or a microprocessing unit (MPU), a memory 12, a storage 13, and a communication interface 16. These components are connected to each other via a bus so as to enable data communication. The processor 11 implements various processes according to this embodiment by loading various programs stored in the storage 13 into the memory 12 and executing them. The memory 12 is typically a volatile storage device such as a dynamic random access memory (DRAM) and stores programs read from the storage 13. The storage 13 is typically a nonvolatile magnetic storage device such as a hard disk drive. The storage 13 stores a data collection program 14 and an anomaly calculation program 15, which are executed by the processor 11. The various programs installed in the storage 13 are distributed in a state stored on a memory card or the like. The communication interface 16 mediates data transmission between the processor 11 and external devices (e.g., the device 2A and the monitoring device 100). The communication interface 16 typically includes Ethernet (registered trademark) or USB (Universal Serial Bus). Note that the various programs stored in the storage 13 may be downloaded from a distribution server (not shown) via the communication interface 16.

[0026] When using a computer having a structure conforming to the above-described general-purpose computer architecture, an operating system (OS) for providing basic computer functions may be installed in addition to an application for providing the functions according to this embodiment. In this case, the program according to this embodiment may execute processing by calling necessary modules provided as part of the OS in a predetermined order and timing. In other words, the program according to this embodiment itself may not include such modules and may execute processing in cooperation with the OS. Alternatively, some or all of the functions provided by execution of the data collection program 14 and the anomaly calculation program 15 may be implemented as dedicated hardware circuits.

[0027] FIG. 3 is a diagram illustrating the configuration of a monitoring device 100 according to the first embodiment. Referring to FIG. 3, the monitoring device 100 typically has a structure conforming to a general-purpose computer architecture. Specifically, the monitoring device 100 includes a processor 101, such as a central processing unit (CPU) or a microprocessing unit (MPU), a memory 102, a storage 103, a display controller 104, an input interface 105, and a communication interface 106. These components are connected to each other via a bus so as to enable data communication. The processor 101 implements various processes according to the present embodiment by loading various programs stored in the storage 103 into the memory 102 and executing them. The memory 102 is typically a volatile storage device such as a dynamic random access memory (DRAM) and stores programs read from the storage 103. The storage 103 is typically a nonvolatile magnetic storage device such as a hard disk drive. The storage 103 stores an analysis program 131 and an information collection program 132, which are executed by the processor 101. The various programs installed in the storage 103 are distributed in a state stored on a memory card or the like. The display controller 104 is connected to the display device 70 and outputs signals to the display device 70 to display various information in accordance with internal commands from the processor 101. The input interface 105 mediates data transmission between the processor 101 and an input device 75 such as a keyboard, mouse, touch panel, or dedicated console. That is, the input interface 105 accepts operation commands given by a user operating the input device 75. The communication interface 106 mediates data transmission between the processor 101 and an external device (e.g., the data collection device 10). The communication interface 106 typically includes Ethernet (registered trademark) or USB (Universal Serial Bus). The various programs stored in the storage 103 may be downloaded from a distribution server (not shown) or the like via the communication interface 106.

[0028] When using a computer having a structure conforming to the above-described general-purpose computer architecture, an operating system (OS) for providing basic computer functions may be installed in addition to an application for providing the functions according to this embodiment. In this case, the program according to this embodiment may execute processing by calling necessary modules provided as part of the OS in a predetermined order and timing. In other words, the program according to this embodiment itself may not include such modules and may execute processing in cooperation with the OS. Alternatively, some or all of the functions provided by execution of the analysis program 131 and the information collection program 132 may be implemented as dedicated hardware circuits.

[0029] 4 is a diagram illustrating functional blocks of the monitoring system according to the first embodiment. Referring to FIG. 4, data collection device 10A connected to device 2A includes an abnormal value calculation unit 20 and an apparatus data acquisition unit 22. The apparatus data acquisition unit 22 is realized by processor 11 executing data collection program 14. The abnormal value calculation unit 20 is realized by processor 11 executing abnormal value calculation program 15.

[0030] The device data acquisition unit 22 acquires status data based on the status of the device 2A. The abnormal value calculation unit 20 calculates an abnormal value based on the status data acquired by the device data acquisition unit 22. The calculation of the abnormal value will be described later. The abnormal value calculated by the abnormal value calculation unit 20 is transmitted to the monitoring device 100 via the communication interface 16.

[0031] The monitoring device 100 includes an analysis unit 200 and an information acquisition unit 210. The analysis unit 200 is realized by the processor 11 executing an analysis program 131. The information acquisition unit 210 is realized by the processor 11 executing an information collection program 132.

[0032] The information acquisition unit 210 acquires data transmitted from each of the data collection devices 10A to 10N. The analysis unit 200 executes an analysis process for the data transmitted from the data collection devices 10A to 10N acquired by the information acquisition unit 210. The analysis unit 200 includes an information output unit 202 and a priority determination unit 204. The information output unit 202 outputs various information to the display device 70 via the display controller 104, for example, so that the information is displayed. The priority determination unit 204 determines the priority of the devices 2A to 2N based on the abnormal values ​​calculated by the abnormal value calculation units 20 of the data collection devices 10A to 10N. The data collection devices 10A to 10N also transmit information about the devices 2A to 2N (such as device names and date and time information) to the monitoring device 100 along with the abnormal values. The monitoring system 1 calculates abnormal values ​​of the devices 2A to 2N, and the abnormal values ​​are transmitted to the monitoring device 100. The devices 2A to 2N are then centrally monitored according to the abnormal values, thereby enabling efficient monitoring of the status of multiple devices.

[0033] FIG. 5 is a diagram illustrating status data acquired by the device data acquisition unit 22 according to the first embodiment. Referring to FIG. 5 , in this example, the device data acquisition unit 22 acquires data on multiple variables (parameters) as status data from the device 2A. Specifically, the device data acquisition unit 22 acquires actual values ​​per unit time. It also acquires standard values ​​per unit time. For example, if the device 2A is an injection molding machine, the device data acquisition unit 22 acquires data such as heater temperature, injection pressure, resin pressure, pump pressure, and mold temperature as status data. The abnormal value calculation unit 20 calculates the degree of deviation between the actual values ​​and the standard values ​​as the abnormal values. For example, the standard values ​​per unit time may be determined by using a virtual model of the injection molding machine to simulate the values ​​of multiple variables when the injection molding machine is operated according to a production plan, and acquiring the values ​​of the multiple variables in the injection molding operation obtained by the simulation as the standard values.

[0034] FIG. 6 is a diagram illustrating the calculation process of the abnormal value calculation unit 20 according to the first embodiment. In this example, data for three variables P1 to P3 will be described as an example with reference to FIG. 6. A three-dimensional space with three axes is set for the variables P1 to P3, and a data group indicating a normal range and data indicating an abnormal state are shown as a learned model. The black point cloud data indicates a data group indicating the normal range. The data group indicating the normal range is composed of standard values. On the other hand, hatched data indicates data that is farther from the normal range. The distance from the center point of the data group indicating the normal range is an abnormal value. In other words, the greater the distance, the higher the abnormal value. In this example, the three-dimensional space of the three variables P1 to P3 is described, but it is of course possible to expand it to a multidimensional space depending on the number of variables.

[0035] FIG. 7 is another diagram illustrating an abnormal value in the abnormal value calculation unit 20 according to the first embodiment. Referring to FIG. 7A, data changes from point to point are shown. In this example, a two-dimensional case is shown, with a change of Δx relative to the x-axis and a change of Δy relative to the y-axis. In the two-dimensional case, the change in distance Δx and Δy is calculated as an abnormal value. Referring to FIG. 7B, data changes from a distribution to a point are shown. In the three-dimensional case, the change in distance from the center of the distribution to a point is calculated as an abnormal value. While this example describes a case in which a change in distance is calculated as an abnormal value, this is not limiting. The speed of change in distance, i.e., the magnitude of the rate of change per unit time, may also be calculated as an abnormal value. Examples of algorithms for calculating abnormal values ​​include anomaly detection based on Mahalanobis distance, LOF (Local Outlier Factor), Isolation Forest, and other anomaly detection algorithms.

[0036] FIG. 8 is a diagram illustrating the processing of the monitoring device 100 according to the first embodiment. Referring to FIG. 8, the monitoring device 100 acquires information (step S2). Specifically, the information acquisition unit 210 acquires data related to the devices 2A to 2N collected by the data collection devices 10A to 10N. Next, the monitoring device 100 performs an analysis process on the collected data (step S4). Specifically, the analysis unit 200 performs the analysis process based on the acquired data. In this regard, the priority determination unit 204 determines the priorities of the multiple machines based on the abnormal values ​​calculated by the abnormal value calculation unit 20. Next, the monitoring device 100 displays the data of the devices 2A to 2N in order of priority as the analysis result (step S6). Specifically, the information output unit 202 displays information related to the multiple machines according to the determined priorities. Then, the processing ends (END).

[0037] 9 is a diagram illustrating an example of a monitoring screen displayed on the display device 70 according to the first embodiment. Referring to FIG. 9, data relating to a plurality of devices is displayed as a list. Specifically, data relating to devices assigned with identification numbers (ID) 1 to 14 is displayed. The data includes the date and time when the data was acquired, device information such as the device name, and abnormal values, all of which are displayed in association with each other.

[0038] Corresponding to ID1, date and time information, a device ("ABC Industries Head Office Factory, Electric Injection Molding Machine A"), and an abnormal value ("7.4") are displayed in association with each other. Corresponding to ID2, date and time information, a device ("ABC Industries Motomachi Factory, Electric Injection Molding Machine A"), and an abnormal value ("7.4") are displayed in association with each other. Corresponding to ID3, date and time information, a device ("ABC Industries Motomachi Factory, Electric Injection Molding Machine B"), and an abnormal value ("7.0") are displayed in association with each other. Corresponding to ID4, date and time information, a device ("ABC Industries Head Office Factory, Electric Injection Molding Machine A"), and an abnormal value ("7.0") are displayed in association with each other. Corresponding to ID5, date and time information, a device ("DEF Manufacturing Kyoto Factory, Hydraulic Injection Molding Machine D"), and an abnormal value ("6.8") are displayed in association with each other. Corresponding to ID6, date and time information, a device ("ABC Industries Head Office Factory, Electric Injection Molding Machine A"), and an abnormal value ("6.6") are displayed in association with each other. Corresponding to ID7, date and time information, a device ("DEF Manufacturing Co., Ltd., Kyoto Factory, Hydraulic Injection Molding Machine D"), and an abnormal value ("6.3") are displayed in association with each other. Corresponding to ID8, date and time information, a device ("DEF Manufacturing Co., Ltd., Kyoto Factory, Hybrid Injection Molding Machine C"), and an abnormal value ("6.3") are displayed in association with each other. Corresponding to ID9, date and time information, a device ("DEF Manufacturing Co., Ltd., Kyoto Factory, Hybrid Injection Molding Machine C"), and an abnormal value ("3.8") are displayed in association with each other. Corresponding to ID10, date and time information, a device ("ABC Industries Head Office Factory, Electric Injection Molding Machine A"), and an abnormal value ("3.6") are displayed in association with each other. Corresponding to ID11, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), and an abnormal value ("3.6") are displayed in association with each other. Corresponding to ID12, date and time information, a device ("ABC Industries Motomachi Factory Electric Injection Molding Machine A"), and an abnormal value ("3.6") are displayed in association with each other. Corresponding to ID13, date and time information, a device ("DEF Manufacturing Kyoto Factory Hybrid Injection Molding Machine C"), and an abnormal value ("3.4") are displayed in association with each other. Corresponding to ID14, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), and an abnormal value ("3.2") are displayed in association with each other.

[0039] In this example, a case is shown in which a priority order is determined for each of the various injection molding machines, which are multiple machines, based on the calculated abnormal value, and information about the injection molding machines is displayed according to the determined priority order. Specifically, a case is shown in which information about the machines is displayed in descending order of the abnormal value. This makes it possible to grasp at a glance which machine has the largest abnormal value. This makes it possible to efficiently monitor the status of multiple machines.

[0040] Second Embodiment FIG. 10 is a diagram illustrating functional blocks of a monitoring system according to a second embodiment. Referring to FIG. 10 , the monitoring system according to the second embodiment differs from the monitoring system described in FIG. 4 in that the monitoring device 100 is replaced with a monitoring device 100A. The monitoring device 100A differs from the monitoring device 100 in that the analysis unit 200 is replaced with an analysis unit 200A. The analysis unit 200A further includes a contribution rate calculation unit 206, an abnormality estimation unit 208, and a maintenance proposal unit 209. The contribution rate calculation unit 206 calculates the contribution rates of multiple variables related to the causes of an abnormal value with respect to an abnormal value. For example, the contribution rate calculation unit 206 calculates the contribution rates of multiple variables to the abnormal value based on the orientation from the center of the data group indicating the normal range of the data described in FIG. 6 . The abnormality estimation unit 208 estimates the cause of the abnormality according to the calculated contribution rates of the multiple variables. The maintenance proposal unit 209 presents information regarding maintenance according to the estimated cause of the abnormality.

[0041] For example, the contribution rate calculation unit 206 may calculate the contribution rates of multiple variables related to the causes of abnormal values ​​with respect to abnormal values ​​for information about the injection molding machine with the highest priority. Alternatively, the contribution rate calculation unit 206 may receive a selection input from a user and calculate the contribution rates of multiple variables related to the causes of abnormal values ​​with respect to abnormal values ​​for information about the injection molding machine corresponding to the received selection input.

[0042] 11 is a diagram illustrating the contribution rates of a plurality of variables calculated by the contribution rate calculation unit 206 according to the second embodiment. Referring to FIG. 11 , in this example, a plurality of contribution rates related to the causes of abnormal values ​​are shown for abnormal values. Specifically, as an example, the contribution rates of a plurality of variables, such as variable PA (heater temperature), variable PB (injection pressure), variable PC (resin pressure), variable PD (pump pressure), and variable PE (mold temperature), are shown.

[0043] The abnormality estimation unit 208 estimates the cause of the abnormality according to the calculated contribution rates of the multiple variables. For example, the abnormality estimation unit 208 extracts a variable with a high contribution rate value from among the variables PA to PE. As an example, the abnormality estimation unit 208 extracts the variable PA (heater temperature). The abnormality estimation unit 208 estimates the abnormality according to the extracted variable PA. Specifically, the abnormality estimation unit 208 determines that the variable PA (heater temperature) has a strong causal relationship with the abnormal value. In other words, the abnormality estimation unit 208 estimates that the heater is the cause of the abnormality based on the variable PA. The maintenance suggestion unit 209 presents information related to maintenance according to the estimated cause of the abnormality.

[0044] For example, the abnormality estimation unit 208 may estimate the cause of the abnormality in accordance with the calculated contribution rates of multiple variables for information about the injection molding machine with the highest priority. Alternatively, the abnormality estimation unit 208 may receive a selection input from a user and estimate the cause of the abnormality in accordance with the calculated contribution rates of multiple variables related to the causes of the abnormal value for information about the injection molding machine corresponding to the received selection input.

[0045] 12A and 12B are diagrams illustrating the presentation of information related to maintenance by the maintenance suggestion unit 209 according to the second embodiment. Referring to Fig. 12A, the maintenance suggestion unit 209 presents information related to maintenance along with a guide message stating, "Part XXX needs to be replaced." Referring to Fig. 12B, the maintenance suggestion unit 209 presents information related to maintenance along with a guide message stating, "Part YYY needs maintenance." For example, when the abnormality estimation unit 208 estimates that the heater is the cause of the abnormality, the maintenance suggestion unit 209 may present information related to replacing the heater, or may present information related to maintenance of the heater component.

[0046] As described above, the monitoring system according to the second embodiment is capable of easily grasping the state of a machine corresponding to an abnormal value by calculating the contribution rates of multiple variables with respect to the abnormal value, and is capable of efficiently monitoring the states of multiple machines. Furthermore, the monitoring system according to the second embodiment is capable of inferring the cause of the abnormality by calculating the contribution rates, and is capable of efficiently monitoring the states of multiple machines.

[0047] Third Embodiment FIG. 13 is a diagram illustrating an example of a monitoring screen displayed on a display device 70 according to a third embodiment. Referring to FIG. 13, data related to a plurality of devices is displayed as a list. Specifically, data related to devices assigned with identification numbers (IDs) 1 to 14 is displayed. The data includes the date and time when the data was acquired, device information, abnormal values, and maintenance history, each associated with the other. The maintenance history is historical data on the previous maintenance work. The maintenance history may be information entered by an administrator who manages the monitoring device 100, or may be information transmitted from the data collection device 10A as information registered in the data collection device 10A.

[0048] In this example, the priority determination unit 204 determines the priorities of the multiple machines based on the calculated abnormal values ​​and maintenance-related information corresponding to each of the multiple machines. For example, the maintenance-related information may include at least one of the following information: maintenance history, maintenance personnel, information on availability of maintenance parts, and maintenance area.

[0049] Specifically, corresponding to ID1, date and time information, a device ("ABC Industrial Headquarters Factory Electric Injection Molding Machine A"), an abnormal value ("7.4"), and a maintenance history are displayed in association with each other. Corresponding to ID2, date and time information, a device ("ABC Industrial Motomachi Factory Electric Injection Molding Machine A"), an abnormal value ("7.4"), and a maintenance history are displayed in association with each other. Corresponding to ID3, date and time information, a device ("ABC Industrial Motomachi Factory Electric Injection Molding Machine B"), an abnormal value ("7.0"), and a maintenance history are displayed in association with each other. Corresponding to ID4, date and time information, a device ("ABC Industrial Headquarters Factory Electric Injection Molding Machine A"), an abnormal value ("7.0"), and a maintenance history are displayed in association with each other. Corresponding to ID5, date and time information, a device ("DEF Manufacturing Kyoto Factory Hydraulic Injection Molding Machine D"), an abnormal value ("6.8"), and a maintenance history are displayed in association with each other. Corresponding to ID6, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), an abnormal value ("6.6"), and a maintenance history are displayed in association with each other. Corresponding to ID7, date and time information, a device ("DEF Manufacturing Kyoto Factory Hydraulic Injection Molding Machine D"), an abnormal value ("6.3"), and a maintenance history are displayed in association with each other. Corresponding to ID8, date and time information, a device ("DEF Manufacturing Kyoto Factory Hybrid Injection Molding Machine C"), an abnormal value ("6.3"), and a maintenance history are displayed in association with each other. Corresponding to ID9, date and time information, a device ("DEF Manufacturing Kyoto Factory Hybrid Injection Molding Machine C"), an abnormal value ("3.8"), and a maintenance history are displayed in association with each other. Corresponding to ID10, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), an abnormal value ("3.6"), and a maintenance history are displayed in association with each other. Corresponding to ID11, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), an abnormal value ("3.6"), and a maintenance history are displayed in association with each other. Corresponding to ID12, date and time information, a device ("ABC Industries Motomachi Factory Electric Injection Molding Machine A"), an abnormal value ("3.6"), and a maintenance history are displayed in association with each other.Corresponding to ID 13, date and time information, the device ("DEF Manufacturing Kyoto Factory Hybrid Injection Molding Machine C"), the abnormal value ("3.4"), and the maintenance history are displayed in association with each other. Corresponding to ID 14, date and time information, the device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), the abnormal value ("3.2"), and the maintenance history are displayed in association with each other.

[0050] In this example, the priorities of the injection molding machines are determined based on the calculated abnormal values ​​and maintenance histories of the various injection molding machines, and information about the injection molding machines is displayed according to the determined priorities. Specifically, information about the machines is displayed in order of abnormal values ​​and the priority of maintenance history dates. For example, when two or more machines have the same abnormal value, the data with the most recent maintenance history date is assigned a higher priority. For example, compared to the data in FIG. 9 , the data with ID 4 is assigned a higher priority than the data with ID 3, which has a more recent maintenance history date. Similarly, the data with ID 11, which has a more recent maintenance history date, is assigned a higher priority than the data with ID 10. This allows users to quickly determine which machines have higher abnormal values. This allows users to efficiently monitor the status of multiple machines.

[0051] In this example, the priority determination unit 204 determines the priorities using the calculated abnormal values ​​and maintenance histories as maintenance-related information corresponding to each of the multiple machines. However, this is not limiting. For example, information about maintenance personnel may be used. For example, if priorities are assigned to maintenance personnel, data associated with a maintenance personnel with a higher priority may be prioritized when abnormal values ​​are the same. Alternatively, information about the availability of maintenance parts may be used. For example, if information about the availability of maintenance parts is pre-registered as the availability information for maintenance parts, data associated with the presence of maintenance parts may be prioritized when abnormal values ​​are the same. Alternatively, information about maintenance areas may be used. For example, if priorities are assigned to maintenance areas, data associated with a maintenance area with a higher priority may be prioritized when abnormal values ​​are the same. This makes it possible to quickly determine which machines have higher abnormal values ​​while taking into account maintenance-related information. This enables efficient monitoring of the status of multiple machines.

[0052] Fourth Embodiment FIG. 14 is a diagram illustrating an example of a monitoring screen displayed on a display device 70 according to a fourth embodiment. Referring to FIG. 14, data related to a plurality of devices is displayed in a list. Specifically, data related to devices assigned with identification numbers (IDs) 1 to 14 is displayed. The data includes the date and time when the data was acquired, device information, abnormal values, threshold values, and differences, each associated with the other data. In this example, the priority determination unit 204 calculates the difference between the calculated abnormal value and a reference threshold value, and determines the priority of the plurality of machines based on the calculated difference value. For example, in this example, the priority is determined in descending order of the difference from the threshold value.

[0053] Specifically, corresponding to ID1, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), an abnormal value ("7.4"), a threshold value ("6.0"), and a difference ("1.4") are displayed in association with each other. Corresponding to ID2, date and time information, a device ("ABC Industries Motomachi Factory Electric Injection Molding Machine A"), an abnormal value ("7.4"), a threshold value ("6.0"), and a difference ("1.4") are displayed in association with each other. Corresponding to ID3, date and time information, a device ("ABC Industries Motomachi Factory Electric Injection Molding Machine B"), an abnormal value ("7.0"), a threshold value ("6.0"), and a difference ("1.0") are displayed in association with each other. Corresponding to ID4, date and time information, a device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), an abnormal value ("7.0"), a threshold value ("6.0"), and a difference ("1.0") are displayed in association with each other. Corresponding to ID9, date and time information, a device ("DEF Manufacturing Kyoto Factory Hybrid Injection Molding Machine C"), an abnormal value ("3.8"), a threshold value ("2.9"), and a difference ("0.9") are displayed in association with each other. Corresponding to ID5, date and time information, a device ("DEF Manufacturing Kyoto Factory Hydraulic Injection Molding Machine D"), an abnormal value ("6.8"), a threshold value ("6.0"), and a difference ("0.8") are displayed in association with each other. Corresponding to ID10, date and time information, a device ("ABC Industrial Headquarters Factory Electric Injection Molding Machine A"), an abnormal value ("3.6"), a threshold value ("2.9"), and a difference ("0.7") are displayed in association with each other. Corresponding to ID6, date and time information, a device ("ABC Industrial Headquarters Factory Electric Injection Molding Machine A"), an abnormal value ("6.6"), a threshold value ("6.0"), and a difference ("0.6") are displayed in association with each other. Corresponding to ID11, date and time information, a device ("ABC Industrial Headquarters Factory Electric Injection Molding Machine B"), an abnormal value ("3.6"), a threshold value ("3.1"), and a difference ("0.5") are displayed in association with each other. Corresponding to ID12, the date and time information, the device ("ABC Industries Motomachi Factory Electric Injection Molding Machine A"), the abnormal value ("3.6"), the threshold value ("3.2"), and the difference ("0.4") are displayed in association with each other.Corresponding to ID13, date and time information, a device ("DEF Manufacturing Co., Ltd. Kyoto Factory Hybrid Injection Molding Machine C"), an abnormal value ("3.4"), a threshold value ("3.0"), and a difference ("0.4") are displayed in association with each other. Corresponding to ID7, date and time information, a device ("DEF Manufacturing Co., Ltd. Kyoto Factory Hydraulic Injection Molding Machine D"), an abnormal value ("6.3"), a threshold value ("6.0"), and a difference ("0.3") are displayed in association with each other. Corresponding to ID8, date and time information, a device ("DEF Manufacturing Co., Ltd. Kyoto Factory Hybrid Injection Molding Machine C"), an abnormal value ("6.3"), a threshold value ("6.0"), and a difference ("0.3") are displayed in association with each other. Corresponding to ID14, the date and time information, the device ("ABC Industries Head Office Factory Electric Injection Molding Machine A"), the abnormal value ("3.2"), the threshold value ("3.0"), and the difference ("0.2") are displayed in association with each other.

[0054] In this example, the priority of the injection molding machines is determined based on the difference between the calculated abnormal value and the threshold value, and information about the injection molding machines is displayed according to the determined priority. Specifically, information about the machines is displayed in descending order of the difference between the abnormal value and the threshold value. This makes it possible to quickly determine which machine has a high abnormal value based on the difference between the abnormal value and the threshold value. This allows for efficient monitoring of the status of multiple machines. Note that in this example, the threshold value may be set to be variable by a user or administrator. In other words, the threshold value may be configurable for each machine. This allows for efficient monitoring of the status of multiple machines by setting the threshold value according to the status of each individual machine.

[0055] In this example, the method for determining at a glance which machine has a high abnormal value based on the difference between the abnormal value and the threshold value has been described, but this is not limiting. For example, information on the speed at which the abnormal value is about to reach the threshold value may be used. Specifically, the date information on when the abnormal value will reach the threshold value may be estimated, and the order of priority for the injection molding machines provided as multiple machines may be determined based on the earliest date information, and information about the injection molding machines may be displayed according to the determined order of priority. This makes it possible to efficiently monitor the status of multiple machines.

[0056] In the above-described monitoring system configuration, the configuration in which the data collection devices 10A to 10N and the monitoring device 100 are provided separately has been described, but this is not limiting, and the monitoring device 100 and the data collection devices 10A to 10N can also be integrated into a single device. Also, a configuration in which part of the functions of the data collection device 10, for example the function of the anomaly calculation unit, is provided in the monitoring device 100 may be used.

[0057] Although the embodiments of the present invention have been described, the embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, and it is intended to include all modifications within the meaning and scope of the claims.

[0058] 1 Monitoring system, 2A to 2N devices, 10A to 10N data collection devices, 11, 101 processor, 12, 102 memory, 13, 103 storage, 14 data collection program, 15 abnormal value calculation program, 16, 106 communication interface, 20 abnormal value calculation unit, 22 device data acquisition unit, 70 display device, 75 input device, 100, 100A monitoring device, 104 display controller, 105 input interface, 131 analysis program, 132 information collection program, 200, 200A analysis unit, 202 information output unit, 204 priority determination unit, 206 contribution rate calculation unit, 208 abnormality estimation unit, 209 maintenance proposal unit, 210 information acquisition unit.

Claims

1. A monitoring device comprising: an abnormal value calculation unit that calculates abnormal values ​​based on the status data of each of a plurality of machines; a priority order determination unit that determines priorities for the plurality of machines based on the abnormal values ​​calculated by the abnormal value calculation unit; and a display control unit that displays information regarding the plurality of machines in accordance with the priorities determined by the priority order determination unit.

2. The monitoring device according to claim 1, wherein the status data includes a plurality of variable data, and the abnormal value calculation unit calculates the abnormal value based on a Mahalanobis distance of the plurality of variable data.

3. The monitoring device according to claim 2, further comprising: a contribution rate calculation unit that calculates a contribution rate of each variable data to the abnormal value; and an abnormality estimation unit that estimates an abnormality according to the calculated contribution rates.

4. The monitoring device according to claim 3, further comprising a maintenance suggestion unit that provides guidance on information regarding maintenance in accordance with the estimated abnormality.

5. The monitoring device according to claim 1, wherein the priority determination unit determines priorities for the plurality of machines based on the calculated abnormal values ​​and information related to maintenance corresponding to each of the plurality of machines.

6. The monitoring device according to claim 5, wherein the information relating to maintenance includes at least one of information on a maintenance history, maintenance personnel, information on securing maintenance parts, and a maintenance area.

7. A monitoring device according to claim 1, wherein the priority order determination unit calculates a difference value between the abnormal value and a reference threshold value, and determines priorities for the multiple machines based on the calculated difference value.

8. A monitoring device according to claim 7, wherein the reference threshold value is set so as to be changeable in correspondence with each of the plurality of machines.

9. A monitoring method comprising the steps of: calculating an abnormal value based on status data of each of a plurality of machines; determining priorities for the plurality of machines based on the calculated abnormal values; and displaying information regarding the plurality of machines in accordance with the determined priorities.

10. A monitoring program that causes a computer to execute a monitoring method, the monitoring program executing the steps of: calculating an abnormal value based on the status data of each of a plurality of machines; determining priorities for the plurality of machines based on the calculated abnormal values; and displaying information about the plurality of machines in accordance with the determined priorities.

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