Equipment diagnosis device and equipment diagnosis method

Abstract

To suppress deterioration of manufacturing equipment while keeping KPIs within acceptable limits.SOLUTION: An equipment diagnosis device comprises: a deterioration suppression mode definition storage unit that stores information including an operation control method for suppressing deterioration of manufacturing equipment or a part of the manufacturing equipment for each predetermined deterioration suppression mode; a KPI calculation unit that calculates a predetermined KPI using the degree of deterioration predicted for each deterioration suppression mode for the manufacturing equipment or parts of the manufacturing equipment, and determines whether the KPI satisfies a predetermined condition; a deterioration suppression determination unit for determining the deterioration suppression mode to be executed from the determination result of satisfaction; and a control information output unit that outputs control information for the manufacturing equipment or parts of the manufacturing equipment according to the deterioration suppression mode to be performed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an equipment diagnosis device and an equipment diagnosis method. [Background technology]

[0002] Patent Document 1 describes a technology that "provides a cell control device that predicts component failures and controls manufacturing machinery to prevent failures until the time when the components can be replaced." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-102554 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in the aforementioned Patent Document 1 describes a method for predicting failures and changing the control method for facilities and equipment to suppress the progression of deterioration of the facilities and equipment and continue production. However, if the control method for manufacturing facilities and equipment is changed to suppress the progression of deterioration, there is a risk that manufacturing-related KPIs (Key Performance Indicators), such as manufacturing quality and productivity, may unintentionally decrease too much.

[0005] An object of the present invention is to suppress the progression of deterioration of manufacturing equipment while keeping KPIs within an acceptable range. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, the present invention employs, for example, the means described in the claims. The present invention includes a plurality of means for solving the above-mentioned problems, and one example thereof is an equipment diagnosis device comprising: a degradation control mode definition storage unit that stores, for each predetermined degradation control mode, information including an operation control method for degradation control of manufacturing equipment or a part of the manufacturing equipment; a KPI calculation unit that calculates a predetermined KPI using a degradation degree predicted for each degradation control mode for the manufacturing equipment or the part of the manufacturing equipment and determines whether the KPI satisfies a predetermined condition; a degradation control determination unit that determines a degradation control mode to be executed based on the determination result of the satisfaction; and a control information output unit that outputs control information for the manufacturing equipment or the part of the manufacturing equipment in accordance with the degradation control mode to be executed. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide a technique for suppressing the progression of deterioration of manufacturing equipment while keeping KPIs within an allowable range.

[0008] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a first embodiment. [Figure 2] FIG. 10 is a diagram illustrating an example of a deterioration detection method. [Figure 3] FIG. 10 illustrates an example of a data structure of a degradation control mode definition storage unit; [Figure 4] FIG. 2 is a diagram illustrating an example of a hardware configuration of an equipment diagnosis device. [Figure 5] FIG. 10 is a diagram showing a deterioration prediction curve and an example of a KPI prediction. [Figure 6] FIG. 10 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a second embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of the data structure of a KPI definition table. [Figure 8]FIG. 10 is a diagram illustrating an example of a KPI input screen. [Figure 9] FIG. 10 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a third embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a degradation suppression mode selection screen. [Figure 11] FIG. 10 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a fourth embodiment. [Figure 12] FIG. 10 is a diagram illustrating an example of the data structure of a prioritized KPI definition table. [Figure 13] FIG. 10 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a fifth embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example of the configuration of an equipment diagnosis system according to a sixth embodiment. [Figure 15] FIG. 10 illustrates an example of a data structure of a line deterioration suppression mode definition storage unit; DETAILED DESCRIPTION OF THE INVENTION

[0010] In the following embodiments, for convenience, when necessary, the description will be divided into multiple sections or embodiments, but unless otherwise expressly stated, they are not unrelated to each other, and one is a partial or complete variation, detail, supplementary explanation, etc. of the other.

[0011] Furthermore, in the following embodiments, when referring to the number of elements (including the number, numerical value, amount, range, etc.), unless otherwise specified or when it is clearly limited to a specific number in principle, it is not limited to that specific number and may be more or less than the specific number.

[0012] Furthermore, it goes without saying that in the following embodiments, the components (including element steps, etc.) are not necessarily essential unless otherwise specified or considered to be clearly essential in principle.

[0013] Similarly, in the following embodiments, when referring to the shapes, positional relationships, etc. of components, etc., it is intended to include those that are substantially similar or similar to those shapes, etc., unless otherwise specified or when it is considered that this is clearly not the case in principle. This also applies to the above numerical values and ranges.

[0014] In addition, in all drawings used to explain the embodiments, the same components are generally given the same reference numerals, and repeated explanations will be omitted. However, if there is a high risk of confusion if the same components share the same names as the components before the change due to an environmental change, etc., different reference numerals or names may be given to the same components. Each embodiment of the present invention will be described below using the drawings.

[0015] Generally, manufacturing facilities include various devices, such as robotic devices with movable arms, which are used in the manufacture of industrial products. Also, line equipment for controlling the integrated movement of a plurality of such robotic devices is also considered to be included in the manufacturing equipment. Throughout the embodiments of the present invention, a dual-arm robot 200 with two movable arms (hereinafter, each arm may be referred to as arm A and arm B for convenience) is basically assumed as the manufacturing equipment. Each arm has degrees of freedom in three axes, and a gripper is provided at the tip of the arm for performing the process of grasping and moving an object.

[0016] In the embodiment of the present invention, unless otherwise specified, the movable axis 1 of arm A of the dual-arm robot 200 will be referred to as "arm A axis 1 (211a)," and the movable axis 2 of arm A will be referred to as "arm A axis 2 (211b)." Furthermore, the gripper of arm B will be referred to as "arm B gripper (211c)." The axes and grippers of each arm of the dual-arm robot 200 are equipped with motors, and operate by controlling the rotation of the motors based on control information received by the control input unit 201.

[0017] In the following embodiments, the "input unit" and "output unit" may be one or more interface devices. The one or more interface devices may be at least one of the following: One or more I / O (Input / Output) interface devices. The I / O interface device is an interface device for at least one of the I / O device and a remote display computer. The I / O interface device for the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., an NIC and an HBA (Host Bus Adapter)).

[0018] In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0019] In the following description, a "storage unit" may be one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

[0020] In the following description, the term "storage unit" or "storage device" may refer to either a memory or a persistent storage device, or both.

[0021] Also, in the following description, a "processing unit" or a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a processor device in a broader sense, such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0022] Furthermore, in the following description, functions may be described using the expression "yyy unit." However, the functions may be realized by one or more computer programs executed by a processor, by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination thereof. When a function is realized by a program executed by a processor, the specified processing is performed using a storage device and / or an interface device, etc., as appropriate, and therefore the function may be considered to be at least a part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a computer from which the program is distributed or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is merely an example; multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0023] In the following description, processing may be described using a "program" or a "processing unit" as the subject, but processing described using a program as the subject may also be processing performed by a processor or a device having that processor. Two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0024] In the following description, information that provides an output for an input may be described using expressions such as "xxx table," but this information may be a table of any structure, or may be a neural network that generates an output for an input, or a learning model such as a genetic algorithm or random forest. Therefore, the "xxx table" may be referred to as "xxx information." In the following description, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0025] Furthermore, in the following description, the "equipment diagnosis system" may be a system configured with one or more physical computers, or may be a system (e.g., a cloud computing system) implemented on a group of physical computing resources (e.g., a cloud infrastructure). When the equipment diagnosis system "displays" display information, it may mean that the display information is displayed on a display device possessed by the computer, or that the computer transmits the display information to a display computer (in the latter case, the display information is displayed by the display computer).

[0026] [First embodiment] Figure 1 is a diagram showing an example of the configuration of an equipment diagnosis system according to the first embodiment. The equipment diagnosis system 10 includes, as the equipment to be diagnosed, a dual-arm robot 200 which is a manufacturing device installed in a manufacturing site (area), an equipment diagnosis device 100, and an equipment data acquisition unit 202 which acquires information necessary for diagnosis from the dual-arm robot 200.

[0027] The equipment diagnosis device 100, the dual-arm robot 200, and the equipment data acquisition unit 202 are connected to each other via a network or communication line (not shown) so as to be able to communicate with each other.

[0028] The network may be, for example, a LAN (Local Area Network), a WAN (Wide Area Network), a VPN (Virtual Private Network), a communication network that uses a general public line such as the Internet in part or in whole, a mobile phone communication network, or a combination of these. Note that the network may also be a wireless communication network such as Wi-Fi (registered trademark) or 5G (Generation).

[0029] The device data acquisition unit 202 acquires information used to detect deterioration of the equipment to be diagnosed. The device data acquisition unit 202 is, for example, a current sensor, and when the equipment to be diagnosed is a part controlled by a motor, the device data acquisition unit 202 is attached to a connection line to a power source in order to acquire the drive current of the motor. Alternatively, when the equipment to be diagnosed is one whose drive current can be measured in advance, the device data acquisition unit 202 acquires the measured drive current.

[0030] The equipment diagnosis device 100 acquires measurement information obtained from the equipment to be diagnosed, selects a degradation suppression mode so as to satisfy the required KPI, and outputs control information for the equipment to be diagnosed.

[0031] More specifically, equipment diagnosis device 100 acquires information about the controlled parts measured from the equipment to be diagnosed, i.e., dual-arm robot 200, via equipment data acquisition unit 202. Equipment diagnosis device 100 then measures the degree of deterioration from the information about the controlled parts measured using a predetermined algorithm, identifies the deteriorated parts, and identifies one or more candidate deterioration suppression modes according to the deteriorated parts.

[0032] Furthermore, the equipment diagnosis device 100 predicts a deterioration curve according to a combination of the deterioration level of the deteriorated portion and the deterioration suppression mode, and predicts a change in the KPI for each deterioration curve. If the predicted change in the KPI satisfies a predetermined threshold, the equipment diagnosis device 100 outputs the deterioration suppression mode that satisfies the threshold as the control mode of the equipment to be diagnosed.

[0033] Equipment diagnosis device 100 includes, as processing units, a deterioration detection unit 101, a deterioration level prediction unit 103, a KPI calculation unit 104, a deterioration suppression determination unit 105, and a control information output unit 110. Equipment diagnosis device 100 also includes, as a storage unit, a deterioration suppression mode definition storage unit 102.

[0034] The deterioration detection unit 101 obtains equipment features from the double-arm robot 200 , estimates the degree of deterioration of each part of the double-arm robot 200 , and inputs the deteriorated parts and their degrees of deterioration to the deterioration suppression determination unit 105 .

[0035] 2 is a diagram showing an example of a deterioration detection method. The deterioration detection unit 101 measures the drive current of the motor corresponding to each movable axis of the dual-arm robot 200 as an equipment feature using a current sensor, which is the equipment data acquisition unit 202, and measures the degree of deterioration of the motor from the power in the harmonic band relative to the rotation frequency.

[0036] When acquiring equipment feature quantities of components other than a motor, the deterioration detection unit 101 may acquire feature quantities that are preset in accordance with the feature quantities. For example, in the case of equipment in which deterioration is caused by clogging of a filter, the deterioration detection unit 101 may acquire an image of the filter or flow velocities and pressures before and after the filter as equipment feature quantities. Furthermore, even when acquiring equipment feature quantities of a motor, the deterioration detection unit 101 may acquire, for example, sound using a microphone or vibration quantities using a vibration sensor as feature quantities instead of values from a current sensor.

[0037] The deterioration level prediction unit 103 predicts a deterioration prediction curve for one of the deterioration control mode candidates. Specifically, the deterioration level prediction unit 103 specifies a function that approximates the degree of deterioration that progresses when the deterioration control mode is used. For example, the deterioration level prediction unit 103 specifies the degree of deterioration as a function that approximates a predetermined linear function f_m(t)=K_m·N·t+b (m is the deterioration control mode, t is time, K_m is a coefficient indicating the degree of deterioration per production unit, N is the standard production amount per unit time, and b is the degree of deterioration at t=0).

[0038] The KPI calculation unit 104 performs KPI calculation and sufficiency determination using a deterioration prediction curve, i.e., the deterioration level predicted for each of the deterioration suppression modes for the manufacturing equipment or a portion of the manufacturing equipment, as input. Specifically, upon receiving the function identified by the deterioration prediction unit 103, the KPI calculation unit 104 calculates a predetermined KPI over time using a predetermined algorithm. For example, based on the knowledge that the deterioration level and the shutdown probability are proportional to each other, the KPI calculation unit 104 may calculate the shutdown probability by multiplying the slope of the deterioration prediction curve by a positive constant and normalizing the result. However, the definition and calculation method of the KPI are not limited to this. For example, based on the knowledge that the deterioration level and the manufacturing quality of the target equipment are negatively proportional to each other, the KPI calculation unit 104 may calculate the manufacturing quality as a KPI by multiplying the deterioration prediction curve by a negative constant and normalizing the result.

[0039] In addition, the KPI calculation unit 104 determines whether the KPI satisfies a specified condition by, for example, determining that if the KPI is below a threshold, the degradation suppression mode satisfies the specified condition that is a requirement for the KPI, and if not, determining that it does not satisfy the condition.

[0040] The degradation suppression determination unit 105 determines the degradation suppression mode to be executed based on the satisfaction determination result. Specifically, the degradation suppression determination unit 105 receives as input the KPI calculation result and the satisfaction determination result of each degradation suppression mode candidate from the KPI calculation unit 104, determines the degradation suppression mode candidate whose satisfaction determination result is satisfactory as the degradation suppression mode to be executed, and outputs the determined mode to the control information output unit 110.

[0041] The control information output unit 110 outputs control information for the manufacturing equipment or a part of the manufacturing equipment according to the degradation suppression mode to be executed. Specifically, the control information output unit 110 receives the degradation suppression mode output from the degradation suppression determination unit 105, and outputs control information corresponding to the degradation suppression mode to the control input unit 201 of the dual-arm robot 200. For example, the control information output unit 110 outputs an instruction to control the load for each control part according to the degradation suppression mode.

[0042] FIG. 3 is a diagram illustrating an example of the data structure of a degradation prevention mode definition storage unit. The degradation prevention mode definition storage unit 102 stores, for each predetermined degradation prevention mode, information including an operation control method for suppressing degradation of manufacturing equipment or a portion of the manufacturing equipment. Specifically, the degradation prevention mode definition storage unit 102 includes a degradation prevention mode 102a associated with a degradation portion 102b and a corresponding operation method definition 102c. The operation method definition 102c includes an operation mode for each control portion of the arm of the dual-arm robot 200, and includes, for example, gripper 102d indicating the gripper of arm A, axis 1 (102e) indicating movable axis 1 of arm A, axis 2 (102f) indicating movable axis 2 of arm A, and axis 3 (102g) indicating movable axis 3 of arm A. Note that arm B (102h) indicating the entire arm B may also be included.

[0043] In each deterioration suppression mode, "normal operation" indicates the operating method in normal mode, and one of the following operating methods can be set: "high load" which is a higher load than normal operation, "medium load," "low load," or "ultra-low load" which is a lower load than normal operation. Note that a high load results in a faster rate of deterioration, and a low load results in a slower rate of deterioration. Since the specific operation differs depending on the part of the manufacturing equipment, the difference in the level of load is specifically reflected in the control that is predetermined for each part. For example, for a part that undergoes repetitive motion, the load is determined based on the frequency of that motion, etc.

[0044] In the example of Figure 3, mode 1 is a mode that suppresses deterioration by keeping the load on the deteriorated Arm A axis 1 at a moderate level. Specifically, one possible method is to reduce the frequency of operations that use axis 1. Mode 2 is a mode that sets the operating frequency of Arm A axis 1 at an extremely low frequency, and instead compensates by increasing the load on Arm A axis 2 and Arm A axis 3. Mode 3 corresponds to the case where Arm A axis 3 is also in a deteriorated state in addition to Arm A axis 1, and is a mode that reduces the load on both axes, and compensates for the reduced operating rate of Arm A due to the reduced load by increasing the operating rate of Arm B.

[0045] 4 is a diagram showing an example of the hardware configuration of an equipment diagnosis device. Equipment diagnosis device 100 can be realized as a general computer 900 equipped with a processor (e.g., a CPU or GPU) 901, memory 902 such as RAM (Random Access Memory), external storage device 903 such as a hard disk drive (HDD) or SSD, reading device 905 for reading information from portable storage medium 904 such as a CD or DVD, input device 906 such as a keyboard, mouse, barcode reader, or touch panel, output device 907 such as a display, and communication device 908 for communicating with other computers via a communication network such as a LAN or the Internet, or as a network system equipped with a plurality of such computers 900. It goes without saying that reading device 905 may be capable of not only reading from but also writing to portable storage medium 904.

[0046] For example, the processing units, namely, the deterioration detection unit 101, the deterioration degree prediction unit 103, the KPI calculation unit 104, the deterioration suppression judgment unit 105, and the control information output unit 110, can be realized by loading a predetermined program stored in an external storage device 903 into memory 902 and executing it on the processor 901; the input unit can be realized by the processor 901 using the input device 906; the output unit can be realized by the processor 901 using the output device 907 or the communication device 908; the communication unit can be realized by the processor 901 using the communication device 908; and the memory unit, namely, the deterioration suppression mode definition memory unit 102, can be realized by the processor 901 using the memory 902 or the external storage device 903.

[0047] This predetermined program may be downloaded to the external storage device 903 from the portable storage medium 904 via the reading device 905 or from a network via the communication device 908, and then loaded onto the memory 902 and executed by the processor 901. Alternatively, the predetermined program may be directly loaded onto the memory 902 from the portable storage medium 904 via the reading device 905 or from a network via the communication device 908, and then executed by the processor 901.

[0048] FIG. 5 shows an example of a deterioration prediction curve and a KPI prediction. The deterioration prediction curve 300 is a curve in which the horizontal axis represents date and time and the vertical axis represents the degree of deterioration of the equipment or its parts to be diagnosed. Since the deterioration prediction curve 300 is a curve that is similar to the equipment or its parts to be diagnosed, it can be used to predict the degree of deterioration of each equipment or its parts to be diagnosed by determining the coefficient parameters of the curve. Note that the deterioration curve function may be a linear function such as a straight line, as described above, or a more complex, higher-order function. Alternatively, the deterioration prediction curve function is not limited to a deterioration curve function, and may output an array of numerical values of the degree of deterioration at each point in time.

[0049] The deterioration prediction curve 300 is estimated for each of the deterioration suppression modes 1 to 3, because different degrees of deterioration are predicted for each of them. In other words, the difference in the slope of each curve indicates that the progression of deterioration of the arm A axis 2 differs depending on the mode.

[0050] The KPI prediction example 301 is an example in which the equipment downtime probability is used as the KPI. For example, based on the knowledge that the degree of deterioration and the downtime probability are proportional to each other, it is conceivable to calculate the downtime probability by multiplying the slope of the deterioration prediction curve 300 by a positive constant and normalizing it. The KPI prediction example 301 is an example of the results of performing the above KPI calculation on the example of the deterioration prediction curve 300. Note that the KPI is not limited to the equipment downtime probability, and may be, for example, takt time or defect rate.

[0051] In an example of satisfaction determination, a method can be considered in which if the KPI is equal to or less than a threshold, it is determined to be satisfied, and if not, it is determined to be unsatisfied. In KPI prediction example 301, an example of a threshold for the shutdown probability is shown. In deterioration control mode 1 and deterioration control mode 3, in which the degree of deterioration exceeds the threshold, the shutdown probability, which is the KPI, exceeds the threshold within a predetermined period, so the KPI is not satisfied, and only deterioration control mode 2 is determined to satisfy the KPI. Next, the flow of the diagnosis process performed by equipment diagnosis device 100 is shown.

[0052] Step 1: The deterioration detection unit 101 obtains equipment features from the dual-arm robot 200, estimates the degree of deterioration of each part of the dual-arm robot 200, and inputs the deteriorated parts and the degree of deterioration to the deterioration suppression determination unit 105. For example, as shown in FIG. 2, the deterioration detection unit 101 measures the drive current of the motor corresponding to each axis of the dual-arm robot 200 using a current sensor as an equipment feature, and measures the degree of deterioration from the power in the harmonic band relative to the rotation frequency.

[0053] Step 2: The deterioration suppression determination unit 105 receives as input the deteriorated part and its deterioration degree output by the deterioration detection unit 101 in step 1, and acquires deterioration suppression modes corresponding to the deteriorated part as deterioration suppression mode candidates by referring to the deterioration suppression mode definition storage unit 102. There may be multiple deterioration suppression mode candidates, but in this case, all applicable deterioration suppression modes are acquired.

[0054] Step 3: The deterioration suppression determination unit 105 inputs the deteriorated part and deterioration level input from the deterioration detection unit 101 in step 1 and the deterioration suppression mode candidates acquired from the deterioration suppression mode definition storage unit 102 in step 2 to the deterioration level prediction unit 103. The deterioration level prediction unit 103 calculates a deterioration prediction curve 300 using the input information, and inputs a function or array indicating the deterioration prediction curve 300 to the KPI calculation unit 104.

[0055] Step 4: The KPI calculation unit 104 receives as input the deterioration prediction curve 300 output by the deterioration degree prediction unit 103, performs KPI calculation and satisfaction judgment, and outputs the KPI calculation result and satisfaction judgment result (designation of the deterioration suppression mode that is satisfied) to the deterioration suppression judgment unit 105.

[0056] The method for outputting the KPI calculation results is not limited to a graph like the KPI prediction example 301, but may be an array in which the numerical values of the KPI calculation results at each time point are arranged.

[0057] Step 5: The deterioration suppression judgment unit 105 receives as input the KPI calculation results and satisfaction judgment results for each deterioration suppression mode candidate from the KPI calculation unit 104, decides to adopt the deterioration suppression mode candidate whose satisfaction judgment result is satisfactory as the deterioration suppression mode, and outputs information identifying the adopted deterioration suppression mode to the control information output unit 110.

[0058] Step 6: The control information output unit 110 receives information specifying the deterioration prevention mode from the deterioration prevention determination unit 105, and outputs control information corresponding to the deterioration prevention mode to the control input unit 201 of the dual-arm robot 200. For example, when "mode 2", which is the deterioration prevention mode shown in Figure 3, is received as the deterioration prevention mode, the control information output unit 110 outputs a control program to the control input unit 201 as control information, which reduces the load on arm A axis 1 and increases the loads on arm A axis 2 and arm A axis 3.

[0059] According to the processing of steps 1 to 6 of the equipment diagnosis device 100 described above, the deterioration suppression mode is dynamically determined based on predictions of various KPIs, such as the equipment downtime probability and manufacturing quality, and it becomes possible to carry out manufacturing by suppressing the deterioration of the dual-arm robot 200 while preventing unintended deterioration of the KPIs.

[0060] The above is an example of the equipment diagnosis system 10 according to the first embodiment. According to the equipment diagnosis system 10 according to the first embodiment, it is possible to suppress the progression of deterioration of manufacturing equipment while keeping KPIs within an allowable range.

[0061] [Second embodiment] Fig. 6 is a diagram showing an example of the configuration of an equipment diagnosis system according to a second embodiment. The equipment diagnosis system 10 according to the second embodiment is basically the same as the equipment diagnosis system 10 according to the first embodiment, but there are some differences. The following description will focus on the differences.

[0062] As shown in FIG. 6, the equipment diagnosis device 100 includes a KPI definition table 108 and a KPI input unit 109 in addition to the components of the equipment diagnosis device 100 according to the first embodiment.

[0063] 7 is a diagram showing an example of the data structure of the KPI definition table 108. The KPI definition table 108 stores a list of KPIs that can be calculated by the KPI calculation unit 104. The KPI definition table 108 stores a fulfillment determination method 108b in association with a KPI 108a. The KPI definition table 108 is stored in the memory 902 or the external storage device 903. In other words, the KPI definition table 108 can also be expressed as a KPI definition table storage unit.

[0064] For example, in the KPI definition table 108, the KPI "equipment downtime probability" is associated with "equipment downtime probability <= (less than or equal to) 0.01%" as the fulfillment determination method. Similarly, the KPI "takt time" is associated with "takt time <= (less than or equal to) 1 minute" as the fulfillment determination method, and the KPI "defect rate" is associated with "defect rate <= (less than or equal to) 0.0001%" as the fulfillment determination method. Similarly, fulfillment determination methods are associated and stored for other KPIs as well.

[0065] The KPI input unit 109 receives a selection input of a KPI to be used for determining the degradation suppression mode. Specifically, the KPI input unit 109 receives a selection input of a KPI via the input device 906 and passes it to the KPI calculation unit 104.

[0066] 8 is a diagram showing an example of a KPI input screen. The KPI input screen 109g displays the contents of the KPI definition table 108 as KPI options, and includes an input field 109h for accepting input of the KPI to be used. The user can select and input the KPI to be used for determining the degradation suppression mode in the input field 109h.

[0067] In the equipment diagnosis device 100 according to the second embodiment, with respect to steps 1 to 6 of the diagnosis processing performed by the equipment diagnosis device 100 according to the first embodiment, the KPI input screen 109 is displayed by the KPI input unit 109 and input of the KPI to be used is accepted (step 0) before step 1. Furthermore, in step 4, the KPI calculation unit 104 receives the deterioration prediction curve 300 output by the deterioration degree prediction unit 103 as input, executes KPI calculation and satisfaction judgment using the KPI and satisfaction judgment method accepted in step 0, and outputs the KPI calculation result and satisfaction judgment result (designation of the deterioration suppression mode that is satisfied) to the deterioration suppression judgment unit 105.

[0068] The above is the equipment diagnosis system 10 according to the second embodiment. According to the equipment diagnosis system 10 according to the second embodiment, the degradation suppression mode can be used using a KPI desired by the user.

[0069] [Third embodiment] Fig. 9 is a diagram showing an example of the configuration of an equipment diagnosis system according to a third embodiment. The equipment diagnosis system 10 according to the third embodiment is basically the same as the equipment diagnosis system 10 according to the first embodiment, but there are some differences. The following description will focus on the differences.

[0070] As shown in FIG. 9, equipment diagnosis device 100 includes KPI definition table 108 and degradation control mode confirmation unit 111 in addition to the components of equipment diagnosis device 100 according to the first embodiment.

[0071] The KPI definition table 108 is the same as the KPI definition table 108 according to the second embodiment, and therefore a description thereof will be omitted.

[0072] The degradation control mode confirmation unit 111 outputs either or both of the KPI calculation result and the satisfaction determination result for each degradation control mode, and allows the user to select and input the degradation control mode to be executed.

[0073] Fig. 10 is a diagram showing an example of a degradation prevention mode selection screen. The degradation prevention mode selection screen 111g shows selectable degradation prevention modes and whether each KPI will be satisfied or not when that mode is adopted (in the example of Fig. 10, satisfied is "OK" and not satisfied is "NG"), and includes an input field 111h for receiving input of the degradation prevention mode to be executed. The user can select and input the degradation prevention mode to be executed in the input field 111h.

[0074] In the equipment diagnosis device 100 according to the third embodiment, in steps 1 to 6 of the diagnosis processing performed by the equipment diagnosis device 100 according to the first embodiment, in step 4, the KPI calculation unit 104 receives as input the deterioration prediction curve 300 output by the deterioration degree prediction unit 103 and performs KPI calculation and satisfaction determination for each KPI and satisfaction determination method in the preset KPI definition table 108. The deterioration control mode confirmation unit 111 then displays the KPI calculation results and the satisfaction determination results for each KPI (KPI satisfaction / non-satisfaction) for the deterioration control mode that can satisfy at least any KPI on the deterioration control mode selection screen 111g. When the deterioration control mode confirmation unit 111 receives the deterioration control mode to be executed that has been received in the input area 111h, it passes the deterioration control mode to be executed, the KPI calculation results, and the satisfaction determination results to the deterioration control mode confirmation unit 111.

[0075] Then, in step 5, the degradation prevention judgment unit 105 receives as input the KPI calculation result and satisfaction judgment result of the degradation prevention mode to be executed from the degradation prevention mode confirmation unit 111, and outputs information identifying the degradation prevention mode to the control information output unit 110.

[0076] The above is the equipment diagnosis system 10 according to the third embodiment. According to the equipment diagnosis system 10 according to the third embodiment, it is possible to check the KPI fulfillment results before execution, thereby preventing the user from executing an unintended degradation suppression mode.

[0077] [Fourth embodiment] Fig. 11 is a diagram showing an example of the configuration of an equipment diagnosis system according to a fourth embodiment. The equipment diagnosis system 10 according to the fourth embodiment is capable of selecting the next best or best degradation suppression mode when there is no degradation suppression mode that satisfies the KPI or when there are multiple degradation suppression modes. The equipment diagnosis system 10 according to the fourth embodiment is basically the same as the equipment diagnosis system 10 according to the first embodiment, but there are some differences. The following description will focus on the differences.

[0078] 11, the equipment diagnosis device 100 according to the fourth embodiment includes a priority-referenced KPI calculation unit 116 instead of the KPI calculation unit 104 included in the equipment diagnosis device 100 according to the first embodiment. In addition, the equipment diagnosis device 100 according to the fourth embodiment includes a prioritized KPI definition table 115.

[0079] 12 is a diagram showing an example of the data structure of a prioritized KPI definition table. In the prioritized KPI definition table 115, a fulfillment determination method 115b and a priority 115c are stored in association with each KPI 115a. The prioritized KPI definition table 115 is stored in the memory 902 or the external storage device 903.

[0080] For example, in the prioritized KPI definition table 115, the KPI "equipment downtime probability" is associated with "equipment downtime probability <= (less than or equal to) 0.01%" as the fulfillment determination method and with a priority of "1." Similarly, the KPI "takt time" is associated with "takt time <= (less than or equal to) 1 minute" as the fulfillment determination method and with a priority of "2." The KPI "defect rate" is associated with "defect rate <= (less than or equal to) 0.0001%" as the fulfillment determination method and with a priority of "3." Similarly, the fulfillment determination methods and priorities are associated and stored for the other KPIs.

[0081] In the equipment diagnosis device 100 according to the fourth embodiment, with respect to steps 1 to 6 of the diagnosis processing performed by the equipment diagnosis device 100 according to the first embodiment, in step 4, the priority-referenced KPI calculation unit 116 receives as input the deterioration prediction curve 300 output by the deterioration degree prediction unit 103, and performs KPI calculation and fulfillment determination for each of the KPIs and fulfillment determination methods in descending order of priority in the preset prioritized KPI definition table 115.

[0082] Here, if there is only one deterioration prevention mode candidate whose satisfaction judgment result is ``satisfied,'' the priority reference KPI calculation unit 116 outputs that deterioration prevention mode to the deterioration prevention judgment unit 105 and transfers control to step 5.

[0083] On the other hand, if there are multiple deterioration control mode candidates for which the satisfaction judgment result is satisfied, the following process (a) is performed; if there are no deterioration control mode candidates for which the satisfaction judgment result is satisfied, the following process (b) is performed.

[0084] (a) When there are multiple degradation control mode candidates for which the satisfaction determination result is satisfied, the priority-referenced KPI calculation unit 116 preferentially adopts, among the satisfied degradation control mode candidates, a mode with a higher satisfaction level in different KPIs. Specifically, the priority-referenced KPI calculation unit 116 again refers to the prioritized KPI definition table 115 and uses the KPI with the next highest priority to perform calculations similar to those in step 4 according to the first embodiment, thereby obtaining KPI calculation results and satisfaction determination results, and narrowing down the degradation control modes that are satisfied. The priority-referenced KPI calculation unit 116 repeats this process for different KPIs until no satisfied degradation control mode candidates remain.

[0085] The priority-referenced KPI calculation unit 116 outputs the degradation prevention mode candidate that was the last remaining one to the degradation prevention determination unit 105. Alternatively, if degradation prevention mode candidates that satisfy all KPIs are obtained, the priority-referenced KPI calculation unit 116 outputs them to the degradation prevention determination unit 105.

[0086] (b) If there is no deterioration prevention mode candidate for which the satisfaction judgment result is satisfied, the priority-referenced KPI calculation unit 116 again refers to the prioritized KPI definition table 115 and uses the KPI with the next highest priority to perform calculations similar to step 4 according to the first embodiment to obtain a KPI calculation result and a satisfaction judgment result. The priority-referenced KPI calculation unit 116 repeats this process until it obtains a deterioration prevention mode candidate for which the satisfaction judgment result is satisfied, and outputs the result to the deterioration prevention determination unit 105.

[0087] The above is the equipment diagnosis system 10 according to the fourth embodiment. Note that the processing performed by the priority-reference KPI calculation unit 116 can also be realized by the KPI calculation unit 104 according to the first embodiment calculating the KPI according to the priority and determining whether predetermined conditions are satisfied. According to the equipment diagnosis system 10 according to the fourth embodiment, when there is no degradation suppression mode that satisfies the KPI or when there are multiple degradation suppression modes that satisfy the KPI, it is possible to select the next best or best degradation suppression mode.

[0088] [Fifth Embodiment] Fig. 13 is a diagram showing an example of the configuration of an equipment diagnosis system according to a fifth embodiment. The equipment diagnosis system 10 according to the fifth embodiment is capable of outputting an alert to the outside of the system when there is no degradation suppression mode that satisfies the KPI. The equipment diagnosis system 10 according to the fifth embodiment is basically the same as the equipment diagnosis system 10 according to the first embodiment, but there are some differences. The following description will focus on the differences.

[0089] As shown in FIG. 13, the equipment diagnosis device 100 according to the fifth embodiment includes an alert output unit 107 in addition to the components of the equipment diagnosis device 100 according to the first embodiment.

[0090] The alert output unit 107 outputs an alert including a predetermined error message, error information, etc. to an external monitoring device (not shown) or the like. The deterioration suppression determination unit 105 causes the alert output unit 107 to output an alert when there is no deterioration suppression mode that satisfies a predetermined threshold for a predefined KPI. In other words, the deterioration suppression determination unit 105 outputs a predetermined alert when, as a result of the determination by the KPI calculation unit 104 as to whether a predetermined condition is satisfied, none of the deterioration suppression modes satisfies the predetermined condition.

[0091] The above is the equipment diagnosis system 10 according to the fifth embodiment. According to the equipment diagnosis system 10 according to the fifth embodiment, it is possible to output an alert when there is no degradation suppression mode that satisfies the KPI.

[0092] [Sixth embodiment] Fig. 14 is a diagram showing an example of the configuration of an equipment diagnosis system according to a sixth embodiment. The equipment diagnosis system 10 according to the sixth embodiment is not limited to control of the deterioration suppression mode when the equipment to be diagnosed is a single device, but is extended to control the deterioration suppression mode for the entire line 400 made up of multiple manufacturing devices (multiple dual-arm robots).

[0093] Line 400 includes a dual-arm robot X (310X), a dual-arm robot Y (310Y), and a dual-arm robot Z (310Z). The dual-arm robot X (310X), the dual-arm robot Y (310Y), and the dual-arm robot Z (310Z) are equipped with control input units 311X, 311Y, and 311Z, respectively, that receive control for controlling the respective devices.

[0094] Furthermore, the dual-arm robot X (310X), the dual-arm robot Y (310Y), and the dual-arm robot Z (310Z) each have two arms that are movable along three axes, similar to the dual-arm robot 200 according to the first embodiment. It is assumed that the dual-arm robot X (310X), the dual-arm robot Y (310Y), and the dual-arm robot Z (310Z) work together to assemble a single product.

[0095] The line 400 includes a line control unit 401, which, upon receiving control information from the equipment diagnosis device 100, issues control instructions to the control input units of each of the dual-arm robots. The equipment diagnosis system 10 according to the sixth embodiment is basically the same as the equipment diagnosis system 10 according to the first embodiment, but there are some differences. The following description will focus on the differences.

[0096] 14, the equipment diagnosis device 100 according to the sixth embodiment includes a line degradation suppression mode definition storage unit 122 instead of the degradation suppression mode definition storage unit 102 in the equipment diagnosis device 100 according to the first embodiment. Also, the equipment data acquisition unit 202 acquires information on the control target parts of the double-arm robot X (310X), the double-arm robot Y (310Y), and the double-arm robot Z (310Z).

[0097] 15 is a diagram illustrating an example of the data structure of a line degradation suppression mode definition storage unit. The line degradation suppression mode definition storage unit 122 is basically the same as the degradation suppression mode definition storage unit 102 according to the first embodiment. However, because the equipment to be diagnosed is a line 400, the line degradation suppression mode definition storage unit 122 includes, for each deteriorated portion 122b, a degradation suppression mode 122a and operation method definitions for each of the multiple dual-arm robots included in the line 400 (robot X operation method definition 122c, robot Y operation method definition 122j, and robot Z operation method definition 122k). In other words, the line degradation suppression mode definition storage unit 122 includes, for each predetermined degradation suppression mode, an operation control method for suppressing degradation of the manufacturing equipment constituting the line 400 or parts of the manufacturing equipment.

[0098] The operation method definition of each dual-arm robot includes an operation mode for each control part of the arm of the dual-arm robot 200, and includes, for example, gripper 122d indicating the gripper of arm A of dual-arm robot X, axis 1 (122e) indicating movable axis 1 of arm A of dual-arm robot X, axis 2 (122f) indicating movable axis 2 of arm A of dual-arm robot X, and axis 3 (122g) indicating movable axis 3 of arm A of dual-arm robot X. Note that arm B (122h) indicating the entire arm B of dual-arm robot X may also be included. Alternatively, an operation mode indicating the entire dual-arm robot Y or the entire dual-arm robot Z may also be included.

[0099] Here, the deterioration suppression mode 122a, "mode 3," is a deterioration suppression mode that corresponds to a case where not only the arm A axis 1 of the dual-arm robot X (310X) but also the arm B axis 1 of the dual-arm robot X (310X) is in a deteriorated state. Specifically, "mode 3" is a deterioration suppression mode that stops the use of the dual-arm robot X (310X) itself and compensates for the resulting decrease in the availability rate of the entire line 400 by increasing the availability rates of the dual-arm robot Y (310Y) and the dual-arm robot Z (310Z). Next, the flow of the diagnosis process performed by the equipment diagnosis device 100 will be described, focusing on the processes that differ from those in the first embodiment.

[0100] Step 1: The deterioration detection unit 101 obtains equipment features from the control target parts of the double-arm robot X (310X), the double-arm robot Y (310Y), and the double-arm robot Z (310Z), estimates the deterioration levels of the control target parts of the double-arm robot X (310X), the double-arm robot Y (310Y), and the double-arm robot Z (310Z), and inputs the deteriorated parts and the deterioration levels to the deterioration suppression determination unit 105.

[0101] Step 2: The deterioration suppression determination unit 105 receives as input the deteriorated portion and its degree of deterioration output by the deterioration detection unit 101 in step 1, and acquires deterioration suppression modes corresponding to the deteriorated portion as deterioration suppression mode candidates by referring to the line deterioration suppression mode definition storage unit 122. There may be multiple deterioration suppression mode candidates, but in this case, all applicable deterioration suppression modes are acquired.

[0102] Step 3: The deterioration suppression determination unit 105 inputs the deteriorated part and deterioration level input from the deterioration detection unit 101 in step 1 and the deterioration suppression mode candidates acquired from the line deterioration suppression mode definition storage unit 122 in step 2 to the deterioration level prediction unit 103. The deterioration level prediction unit 103 calculates a deterioration prediction curve 300 using the input information, and inputs a function or array indicating the deterioration prediction curve 300 to the KPI calculation unit 104.

[0103] Step 6: The control information output unit 110 receives information specifying the degradation prevention mode from the degradation prevention determination unit 105 and outputs control information corresponding to the degradation prevention mode to the line control unit 401 of the line 400. For example, when "mode 3," which is the degradation prevention mode shown in FIG. 15, is received as the degradation prevention mode, the control information output unit 110 stops the double-arm robot X (310X) and outputs a control program that increases the load on the double-arm robot Y (310Y) and the double-arm robot Z (310Z) as control information to the line control unit 401. In other words, the control information output unit 110 outputs control information for the manufacturing equipment that constitutes the line or the parts of the manufacturing equipment according to the degradation prevention mode to be executed.

[0104] According to the processing of steps 1 to 6 of the equipment diagnosis device 100 described above, it becomes possible to dynamically determine the deterioration control mode based on predictions of various KPIs, such as the equipment stoppage probability and manufacturing quality of the line 400, and to carry out manufacturing by adopting a deterioration control mode that compensates by substituting other dual-arm robots on the same line for the work of a specific dual-arm robot. When the line stoppage probability is the KPI, adopting such a deterioration control mode makes it possible to continue manufacturing on the entire line 400, even if a single device has to be stopped in the worst case.

[0105] The above is an example of the equipment diagnosis system 10 according to the sixth embodiment. According to the equipment diagnosis system 10 according to the sixth embodiment, it is possible to suppress the progression of deterioration of line manufacturing equipment while keeping KPIs within an allowable range.

[0106] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described examples have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. It is possible to replace part of the configuration of an embodiment with another configuration, and it is also possible to add the configuration of another embodiment to the configuration of an embodiment. It is also possible to delete part of the configuration of an embodiment.

[0107] Furthermore, the above-described units, configurations, functions, processing units, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described units, configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk, or a recording medium such as an IC card, SD card, or DVD.

[0108] It should be noted that the control lines and information lines in the above-described embodiments are those considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be considered that almost all components are interconnected. The present invention has been described above, focusing on the embodiments. [Explanation of symbols]

[0109] 10: Equipment diagnosis system, 100: Equipment diagnosis device, 101: Deterioration detection unit, 102: Deterioration suppression mode definition memory unit, 103: Deterioration degree prediction unit, 104: KPI calculation unit, 105: Deterioration suppression judgment unit, 110: Control information output unit, 200: Dual-arm robot, 201: Control input unit, 202: Device data acquisition unit, 211a: Arm A axis 1, 211b: Arm A axis 2, 211c: Arm B gripper.

Claims

1. a degradation suppression mode definition storage unit that stores information including an operation control method for suppressing degradation of the manufacturing equipment or a portion of the manufacturing equipment for each predetermined degradation suppression mode; a KPI calculation unit that calculates a predetermined KPI using the deterioration degree predicted for each of the deterioration suppression modes for the manufacturing equipment or a part of the manufacturing equipment, and determines whether the KPI satisfies a predetermined condition; a degradation suppression determination unit that determines that the degradation suppression mode in which the KPI satisfies the predetermined condition is the degradation suppression mode to be executed; a control information output unit that outputs control information for the manufacturing equipment or a part of the manufacturing equipment in accordance with the degradation suppression mode to be executed; An equipment diagnosis device comprising:

2. The equipment diagnosis device according to claim 1, a KPI definition table storage unit that stores a list of KPIs that can be calculated by the KPI calculation unit; a KPI input unit that accepts any of the KPIs stored in the KPI definition table storage unit as a KPI to be used in the KPI calculation unit; An equipment diagnosis device comprising:

3. The equipment diagnosis device according to claim 1, a prioritized KPI definition table storage unit that stores a list of KPIs that can be calculated by the KPI calculation unit and the priorities of the KPIs; the KPI calculation unit calculates a KPI and determines whether the predetermined condition is satisfied in accordance with the priority, and identifies the deterioration suppression mode that satisfies the predetermined condition. An equipment diagnosis device characterized by:

4. The equipment diagnosis device according to claim 1, an alert output unit that outputs a predetermined alert when, as a result of the determination by the KPI calculation unit as to whether the predetermined condition is satisfied, none of the deterioration suppression modes satisfies the predetermined condition; An equipment diagnosis device comprising:

5. The equipment diagnosis device according to claim 1, the degradation suppression mode definition storage unit includes, for each of the degradation suppression modes, an operation control method for suppressing degradation of the manufacturing equipment constituting the line or a portion of the manufacturing equipment, the control information output unit outputs control information of the manufacturing equipment constituting the line or a part of the manufacturing equipment in accordance with the degradation suppression mode to be executed. An equipment diagnosis device characterized by:

6. The equipment diagnosis device according to claim 1, a deterioration prediction unit that outputs a deterioration prediction curve that predicts a deterioration degree when the deterioration suppression mode is adopted for the manufacturing equipment or a part of the manufacturing equipment, The KPI calculation unit calculates the KPI using the deterioration prediction curve. An equipment diagnosis device characterized by:

7. An equipment diagnosis method using an information processing device, the information processing device includes a processor and a degradation control mode definition storage unit that stores, for each predetermined degradation control mode, information including an operation control method for degradation control of manufacturing equipment or a portion of the manufacturing equipment; The processor: a KPI calculation step of calculating a predetermined KPI using the deterioration degree predicted for each of the deterioration suppression modes for the manufacturing equipment or a part of the manufacturing equipment, and determining whether the KPI satisfies a predetermined condition; a degradation suppression determination step of determining that the degradation suppression mode in which the KPI satisfies the predetermined condition is the degradation suppression mode to be executed; a control information output step of outputting control information for the manufacturing equipment or a part of the manufacturing equipment according to the degradation suppression mode to be executed; An equipment diagnosis method characterized by carrying out the above.

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