Information processing method, information processing device, display method, display device, program, recording medium, article manufacturing method, and learning data acquisition method

Abstract

To provide an information processing method and an information processing apparatus which facilitate work when a worker performs data analytical work, such as confirmation and comparison, by extracting a plurality of arbitrary partial data out of time-series data collected at a high sampling rate for a long period.SOLUTION: In an information processing method, an information processing apparatus obtains time-series data of physical quantity relating to a state of a machine device and event data relating to an event occurred in the machine device, extracts a plurality of partial time-series data out of the time-series data regarding a predetermined event included in the event data, and generates an image in which information relating to each of the plurality of extracted partial time-series data is disposed so that mutual distances are reduced than when disposed on a linear scale using time as an index.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing method, an information processing device, and the like. [Background technology]

[0002] The operating state of a machine can change from moment to moment due to changes in the state of its components, etc. If an operating state within the permissible range in light of the intended use of the machine is called a normal state, and an operating state outside the permissible range is called a faulty state, then for example, if it is a production machine and it goes into a faulty state, it will produce defective products or cause problems such as stopping the production line.

[0003] In production machinery, etc., maintenance work is generally performed on a regular or irregular basis in order to prevent breakdowns from occurring as much as possible, even when the same work is performed repeatedly and continuously. In order to increase preventive safety, it is effective to shorten the intervals between maintenance work, but since production machinery, etc. are stopped during maintenance work, if the frequency of maintenance work is increased too much, the operating rate of production machinery, etc. will decrease. Therefore, it is desirable to be able to detect when a breakdown is imminent, even if the machinery, etc. is still in a normal state. If it is possible to detect that a breakdown is imminent (to predict the occurrence of a breakdown), maintenance work for the machinery, etc. can be performed at that point, preventing the operating rate from decreasing more than necessary.

[0004] A known method for predicting the occurrence of failures is to create a trained model based on the state of the machinery using machine learning in advance, and then use the trained model to evaluate the state of the machinery at the time of evaluation. To improve prediction accuracy, it is important to build a trained model suitable for predicting failures, but to do so, it is important to prepare training data (teacher data) to be used when generating a failure prediction model for the machinery using machine learning. To determine whether the extracted data is suitable as training data, detailed data analysis such as checking and comparing waveforms is required.

[0005] For example, in the data analysis method described in Patent Document 1, multiple partial time series data are extracted from time series data in which the physical quantities of production equipment are associated with measurement times, and each partial time series data is plotted on a single graph with the axis representing the elapsed time from a predetermined reference time. Then, each plotted partial time series data is shifted in the elapsed time axis direction by a user's operation, and the time series data is compared by aligning the reference points. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2013-8234 A Summary of the Invention [Problem to be solved by the invention]

[0007] In general, in order to manage the operating state of machinery and equipment, various parameters (physical quantities) are measured and a huge amount of time-series data is acquired. In order to build a trained model suitable for predicting failures in machinery and equipment, it is necessary to extract appropriate data from the huge amount of acquired time-series data and perform detailed data analysis such as checking and comparing waveforms to determine whether the data is suitable for learning.

[0008] However, for example, in the case of machinery such as industrial robots installed in production lines, the frequency of failures is generally not high, so it is necessary to collect time series data over a long period of time. Furthermore, the collected time series data is data for managing the operating state of the machinery, so there are many measurement parameters, and in order to analyze the waveforms in detail, the sampling rate needs to be high, and the amount of collected data becomes enormous. Thus, when extracting data related to irregular failures from data collected at a high sampling rate over a long period of time and performing tasks such as comparison, the conventional data display method places a heavy burden on the worker, causing problems in work efficiency and accuracy.

[0009] In the most basic method, when time series data is displayed on a single graph with the horizontal axis representing the measurement time, partial data related to faults are irregularly scattered across the long time axis, and multiple partial data related to faults are not necessarily displayed on the same screen. In addition, when the data is compressed on the time axis to display on the same screen, the graph waveform is crushed even though it was measured at a high sampling rate, making it difficult to confirm and compare the waveforms. For detailed analysis, the operator must enlarge parts of the data, and the data analysis work takes a lot of time.

[0010] In Patent Document 1, first, from the huge amount of acquired time series data, time series data to be added to a comparison graph is selected, and partial time series data is extracted. Then, elapsed time is assigned to each of the extracted partial time series data, and the partial time series data are aligned on the comparison graph so that the phases of the elapsed times of the partial time series data are in phase, and are displayed so as to overlap. Although it is possible to compare partial time series data by aligning the phases of the elapsed times of the partial time series data on the comparison graph so that the phases of the elapsed times of the partial time series data are in phase, the operation of the operator is cumbersome.

[0011] Therefore, there has been a demand for an information processing method and information processing device that makes it easier for workers to extract multiple arbitrary partial data from time-series data collected at a high sampling rate over a long period of time and perform data analysis work such as checking and comparing the data. [Means for solving the problem]

[0012] The first aspect of the present invention relates to the state of a mechanical device. Multiple types Physical quantity Multiple Obtain time series data, and A plurality of physical quantities corresponding to the physical quantities Partial time series data Draw and displaying an image in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data is smaller than the distance between the display areas corresponding to the partial time series data when the partial time series data is displayed based on the time axis of the partial time series data. death , extracting, from the plurality of partial time series data, a first partial time series data corresponding to an event occurring in the machine, extracting a second partial time series data corresponding to the extracted first partial time series data, and displaying the image in which the first partial time series data and the second partial time series data are arranged in correspondence with each other. The information processing method is characterized in that:

[0013] The second aspect of the present invention relates to a state of a mechanical device. Multiple types Physical quantity Multiple Obtain time series data, and from the time series data A plurality of physical quantities corresponding to the physical quantities When partial time series data is extracted and displayed based on the time axis of the time series data, an image is displayed in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data is smaller than the distance between the display areas corresponding to the partial time series data. death , extracting, from the plurality of partial time series data, a first partial time series data corresponding to an event occurring in the machine, extracting a second partial time series data corresponding to the extracted first partial time series data, and displaying the image in which the first partial time series data and the second partial time series data are arranged in correspondence with each other. The present invention is an information processing device characterized by the above. The third aspect of the present invention relates to a state of a mechanical device. Multiple types A method for displaying a physical quantity, comprising: Multiple From time series data Corresponding to the physical quantity When information related to a plurality of extracted partial time series data is displayed based on the time axis of the time series data, an image is displayed in which the partial time series data is arranged so that the distance between the display areas corresponding to the partial time series data is smaller than the distance between the display areas corresponding to the partial time series data. death , acquiring time series data relating to a plurality of types of the physical quantities as the time series data, extracting a plurality of the partial time series data for the plurality of types of the physical quantities from the time series data, and displaying the image in which information relating to the plurality of the partial time series data for the plurality of types of the physical quantities is arranged according to a sampling number or a number of operation cycles; The display method is characterized by the above. The fourth aspect of the present invention relates to a state of a mechanical device. Multiple types A display device for displaying a physical quantity, Multiple From time series data Corresponding to the physical quantity When information related to a plurality of extracted partial time series data is displayed based on the time axis of the time series data, an image is displayed in which the partial time series data is arranged so that the distance between the display areas corresponding to the partial time series data is smaller than the distance between the display areas corresponding to the partial time series data. death , acquiring time series data relating to a plurality of types of the physical quantities as the time series data, extracting a plurality of the partial time series data for the plurality of types of the physical quantities from the time series data, and displaying the image in which information relating to the plurality of the partial time series data for the plurality of types of the physical quantities is arranged according to a sampling number or a number of operation cycles; The display device is characterized in that Effect of the Invention

[0014] The present invention can provide an information processing method and information processing device that facilitates the extraction of arbitrary partial data from collected time-series data and the confirmation, comparison, etc. of the partial data by an operator. [Brief description of the drawings]

[0015] [Figure 1] 1 is a schematic functional block diagram for explaining functional blocks included in a time-series data display device according to an embodiment. [Diagram 2] FIG. 1 is a diagram illustrating an example of a hardware configuration of a time-series data display device according to an embodiment. [Diagram 3] 4 is a flowchart for explaining a control method according to the embodiment. [Figure 4] 1A is a diagram showing an example of time-series data collected by the time-series data display device, and FIG. 1B is a diagram showing an example of event data collected by the time-series data display device. [Diagram 5] 1 is a graph illustrating time-series data for one repeated operation collected from a mechanical device. [Figure 6] (a) A graph showing an example of time series data collected during continuous repetitive motion. (b) A graph showing time series data collected over a long period of time, with the time axis compressed. [Figure 7] An example of the extracted partial time series data displayed on a linear scale (i.e., absolute time axis) using time as the index. [Figure 8] 4 is an example of a display image according to the embodiment. [Figure 9] 1A is an example of a display image according to an embodiment in which a failure stop is treated as an event, and FIG. [Figure 10] 13 is another example of a display image according to the embodiment. [Figure 11] 13 is yet another example of a display image according to the embodiment. [Figure 12] FIG. 1 is a diagram showing an example in which a time-series data display device according to an embodiment is connected to a six-axis articulated robot. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] An information processing method, an information processing device, and the like according to embodiments of the present invention will be described with reference to the drawings. In the drawings referred to in the following description of the embodiments, elements denoted by the same reference numbers have the same functions unless otherwise specified.

[0017] FIG. 1 is a schematic diagram for explaining the configuration of functional blocks included in an information processing device according to an embodiment. In FIG. 1, functional elements necessary for explaining the features of this embodiment are shown as functional blocks, but general functional elements that are not directly related to the problem-solving principle of the present invention are omitted. In addition, each functional element shown in FIG. 1 is a functional concept, and does not necessarily have to be physically configured as shown in the figure. For example, the specific form of distribution and integration of each functional block is not limited to the illustrated example, and all or part of them can be functionally or physically distributed and integrated in any unit depending on the usage situation, etc.

[0018] As shown in FIG. 1, a time-series data display device 100 serving as an information processing device according to the embodiment is communicatively connected to a mechanical device 10 serving as a measurement target. The machine 10 is various industrial equipment such as an industrial robot or a production device installed in a production line. The machine 10 is equipped with various sensors 11 for measuring physical quantities related to the state of the machine. For example, if the machine 10 is a multi-joint robot, a sensor for measuring the current value of a motor that drives a joint, a joint angle sensor, a sensor for measuring speed, vibration, and sound, etc. may be installed. However, this is merely an example, and an appropriate type and number of sensors may be installed as the sensor 11 at an appropriate position depending on the type of the machine 10 and the work purpose. Various sensors such as a force sensor, a torque sensor, a vibration sensor, a sound sensor, an imaging sensor, a distance sensor, a temperature sensor, a humidity sensor, a flow rate sensor, a pH sensor, a pressure sensor, a viscosity sensor, and a gas sensor may be used as the sensor 11. Note that in FIG. 1, the sensor 11 is shown as a single number for convenience of illustration, but usually, multiple sensors are installed so as to be able to communicate with the time-series data display device 100.

[0019] The machine 10 is connected to a time series data display device 100 as an information processing device by wired or wireless communication so as to be able to communicate with the time series data display device 100, and the time series data display device 100 can acquire data measured by the sensor 11 through communication. Below, the functional blocks of the time series data display device 100 will be explained in order. The time series data display device 100 includes a control unit 110, a storage unit 120, a display unit 130, and an input unit 140.

[0020] The control unit 110 includes a plurality of functional blocks, which are configured by, for example, reading and executing a control program stored in a storage device or a non-transitory recording medium by the CPU of the time-series data display device 100. Alternatively, some or all of the functional blocks may be configured by hardware such as an ASIC provided in the time-series data display device 100.

[0021] The storage unit 120 includes a time-series data storage means 121, an event data storage means 122, an extracted data storage means 123, and a combined data storage means 124. These means of the storage unit 120 are configured by being appropriately allocated to storage areas of a storage device such as a hard disk drive, RAM, or ROM. The storage unit 120 is a data storage unit that stores various data required to create an image that displays the time-series data in an easy-to-view manner.

[0022] The display unit 130 and the input unit 140 are user interfaces included in the time-series data display device 100. The display unit 130 uses a display device such as a liquid crystal display or an organic EL display, and the input unit 140 uses an input device such as a keyboard, a jog dial, a mouse, a pointing device, or a voice input device.

[0023] The data collection means 111 included in the control unit 110 acquires time series data and event data related to the mechanical device from the mechanical device 10, and stores them respectively in a time series data storage means 121 and an event data storage means 122. The data collection means 111 can also be called a data acquisition unit.

[0024] The data collection means 111 collects time-series data of physical values ​​related to the state of the mechanical device 10, such as current, speed, pressure, vibration, sound, temperature of each part, etc., measured by the sensor 11 of the mechanical device 10, and stores the data in the time-series data storage means 121. Alternatively, the data collection means 111 may obtain maximum values, minimum values, average values, integral values, integral transformed values ​​into the frequency domain, differential values, second-order differential values, etc. for each predetermined period of the measured values ​​obtained from the sensor 11, and store them in the time-series data storage means.

[0025] Furthermore, the data collection means 111 collects event data relating to events occurring in the machinery and stores it in the event data storage means 122. The entry of the machinery into a specified state is set as an event, and, for example, time information at which the event occurred is collected as event data and stored in the event data storage means 122. For example, if the entry of a machinery that normally operates continuously and repeatedly (cyclically) into a stopped state is set as an event, the date and time at which the stopped state occurred is stored in the event data storage means 122. Events such as breakdowns and maintenance that cause the stopped state generally occur irregularly or at long intervals, but the information processing device of the embodiment is suitable for handling such events that occur discretely or irregularly in time.

[0026] The data extraction means 112 extracts partial time series data relating to an event from the time series data stored in the time series data storage means 121 based on the event data stored in the event data storage means 122, and stores the extracted data in the extracted data storage means 123. The data extraction means 112 can also be called a data extraction unit.

[0027] For example, when the extraction condition is the stop of a machine, the date and time data when the machine stopped is read out as event data from the event data storage means 122. Then, based on this event data, for example, sensor measurement values ​​collected during one operation cycle before the machine stopped are extracted and stored as partial time series data in the extracted data storage means 123. Alternatively, the maximum value, minimum value, average value, integral value, integral transformed value into the frequency domain, differential value, second derivative value, etc. for each predetermined period of the measurement values ​​one operation before the machine stopped are extracted from the time series data storage means 121. Then, they are stored in the extracted data storage means 123 as partial time series data.

[0028] Although the processing has been described for the case where one type of event data is stored in the event data storage means 122, there may be cases where event data relating to multiple types of events is stored in the event data storage means 122. In such cases, the operator selects a specific event from multiple types of events via the input unit 140, and the data extraction means 112 extracts partial time series data relating to the selected specific event and stores it in the extracted data storage means 123. Alternatively, a specific event selected from multiple types of events may be registered in advance, and partial time series data relating to the registered specific event may be automatically extracted and stored in the extracted data storage means 123.

[0029] The data combining means 113 creates a graph in which partial time series data relating to events are arranged based on the partial time series data stored in the extracted data storage means 123. The data combining means 113 can also be called an image creation unit. The data combining means 113 creates a graph in which partial time series data relating to events are combined or arranged closely along the horizontal axis indicating the number of data, for example, and stores the created graph in the combined data storage means 124. The created graph can be displayed on the display unit 130 or printed using a printing device (not shown) according to the needs of the worker (operator).

[0030] Next, Fig. 2 shows a schematic diagram of an example of a hardware configuration of the time-series data display device of the embodiment. As shown in Fig. 2, the time-series data display device can include PC hardware having a CPU 1601 as a main control means, a ROM 1602 as a storage device, and a RAM 1603. The ROM 1602 can store information such as a processing program for implementing an information processing method described later. The RAM 1603 is used as a work area for the CPU 1601 when executing the information processing method. An external storage device 1606 is connected to the PC hardware. The external storage device 1606 is composed of an HDD, an SSD, an external storage device of another system mounted on a network, or the like.

[0031] The processing program of the CPU 1601 for realizing the information processing device or information processing method according to the embodiment can be stored in a storage unit such as an external storage device 1606 consisting of an HDD or SSD, or (for example, an EEPROM area) of the ROM 1602. In this case, the processing program of the CPU 1601 for realizing the information processing method (for example, a time-series data display method) can be supplied to each of the above-mentioned storage units via the network interface 1607 and updated to a new program. Alternatively, the processing program of the CPU 1601 for realizing the information processing method can be supplied to each of the above-mentioned storage units via various storage means such as magnetic disks, optical disks, and flash memories and drive devices therefor and updated. Various storage means, storage units, and storage devices in a state in which a program capable of executing the processing of the CPU 1601 for realizing the information processing method is a computer-readable recording medium related to the information processing method or information processing device of the present invention.

[0032] The sensor 11 shown in Fig. 1 is connected to the CPU 1601. In Fig. 2, for the sake of simplicity, the sensor 11 is shown as being directly connected to the CPU 1601, but may be connected via, for example, IEEE488 (so-called GPIB) or the like. The sensor 11 may also be configured to be communicably connected to the CPU 1601 via a network interface 1607 and a network 1608.

[0033] The network interface 1607 can be configured using a communication standard for wired communication such as IEEE 802.3, or wireless communication such as IEEE 802.11 and 802.15. The CPU 1601 can communicate with the external device 1104 and the external device 1121 via the network interface 1607. For example, if the subject of the time series data display is an industrial robot, the external device 1104 and the external device 1121 may be a supervisory control device such as a PLC or a sequencer arranged for controlling and managing the industrial robot, or a management server.

[0034] In the example shown in Fig. 2, an operation unit 1604 corresponding to the input unit 140 shown in Fig. 1 and a display device 1605 corresponding to the display unit 130 are connected as a UI device (user interface device). The operation unit 1604 can be configured by a terminal such as a handy terminal, or devices such as a keyboard, a jog dial, a mouse, a pointing device, a voice input device, etc. (or a control terminal equipped with these). The display device 1605 may be any device capable of displaying information related to the processing executed by the data extraction means 112, the data combination means 113, etc. on a display screen, and may be, for example, a liquid crystal display device.

[0035] Next, an information processing method (time series data display method) executed by the time series data display device 100 will be described with reference to the flowchart of Fig. 3. Fig. 3 shows an example of a processing procedure executed by the time series data display device. First, in step S101, the time-series data display device 100 collects time-series data and event data from the mechanical device 10. 4(a) shows an example of time series data collected by the time series data display device 100, which is a series of data measured by periodically sampling the drive current of an industrial robot equipped in the machine 10. The data collection means 111 of the time series data display device 100 collects such time series data from the sensor 11 of the machine 10 and stores it in the time series data storage means 121.

[0036] Here, the collected time series data will be explained in more detail. FIG. 5 shows an example of time series data for one cycle operation when the industrial robot equipped in the machine 10 is operating normally, visualized as a current waveform graph. FIG. 6(a) shows an example of time series data collected when the industrial robot is continuously performing cycle operations, visualized as a current waveform graph. FIG. 6(a) shows an example of a case where a waveform SPW with a unique amplitude is included in the graph. FIG. 6(b) shows a graph in which the time axis direction of time series data collected over a long period of time is compressed more than that of FIG. 6(a). Although it can be seen in FIG. 6(b) that two waveforms SPW with unique amplitude are included, the waveforms of each cycle operation are crushed in the time axis direction, so that it is not possible to check or compare the waveforms in detail in the image.

[0037] Next, Fig. 4(b) shows an example of event data collected by the time-series data display device 100. This is data that records the time when an event occurred when the stoppage of an industrial robot equipped in the machine 10 is set as an event. In this example, a stoppage due to maintenance work that is performed regularly or irregularly, or a stoppage due to an irregular malfunction is treated as an event. In parallel with the collection of time-series data, the data collection means 111 receives control information from, for example, a control unit that manages the operation of the machine 10, collects event data, and stores the event data in the event data storage means 122.

[0038] 3, in step S102, the data extraction means 112 extracts partial time-series data relating to a predetermined event from the time-series data stored in the time-series data storage means. Here, the predetermined event refers to an event arbitrarily selected by an operator from the event data stored in the event data storage means 122, but may be configured so that the control unit 110 automatically selects the event.

[0039] For example, partial time series data related to a predetermined event is extracted from the time series data shown in FIG. 4(a) based on the event data shown in FIG. 4(b). Specifically, the time series data related to the operation one cycle before the time when the selected predetermined event occurred (the time when the industrial robot stopped) is extracted as the partial time series data. Note that this is only an example, and for example, time series data separated by a predetermined number of operation cycles from the occurrence of the predetermined event may be extracted, or time series data for a plurality of consecutive operation cycles may be collected and extracted as the partial time series data. Alternatively, the time series data of the operation cycle itself in which the predetermined event occurred may be extracted as the partial time series data. The extracted partial time series data is stored in the extracted data storage means 123 together with time information related to the partial time series data.

[0040] Here, let us first consider a case where the extracted partial time series data is arranged and displayed on a linear scale (i.e., absolute time axis) with time as an index. FIG. 7 shows a schematic diagram of the display screen W, but the graph does not show most of the time series data during continuous operation because it was not extracted, and only the waveforms of the partial time series data related to the event are shown. Therefore, it can be said that the redundancy is significantly reduced compared to the graph in FIG. 6(b). However, when time series data is collected over a long period of time, the waveforms of the partial time series data are compressed and crushed in the time axis direction on the display screen W, so that the details of the waveform cannot be confirmed. In addition, when trying to expand the time axis direction to make it easier to observe the waveform shape, the partial time series data are spaced at unequal intervals from each other, so that even if multiple waveforms are to be comparatively observed, they may go beyond the screen.

[0041] Therefore, in the embodiment, in step S103, the data combining means 113 as a processing unit performs a process of combining the partial time series data stored in the extracted data storage means 123, and stores the combined data in the combined data storage means 124. That is, an image (combined data) is created in which information (e.g., graphs) relating to each of the extracted partial time series data are arranged so that the distance between them is smaller than when they are arranged on a linear scale with time as an index. The data combining means 113 arranges information (e.g., graphs) relating to the partial time series data so that information (e.g., graphs) relating to adjacent partial time series data are linked to each other or arranged at a predetermined short distance. For example, the image is processed so that the distance in the horizontal axis direction separating the waveforms of each partial time series data in FIG. 7 becomes zero or a small predetermined distance, thereby narrowing the interval between the waveforms.

[0042] Then, in step S104, the combined data stored in the combined data storage means 124 is used to display a graph on the display unit 130. At this time, the horizontal axis direction can be expanded as necessary to facilitate observation and comparison of the waveforms. In addition, it is preferable that the index (scale) of the horizontal axis of the graph is not absolute time but the number of samplings of the original measurement data, the number of operation cycles, etc. This is because, since partial time series data that were spaced at unequal intervals are arranged side by side, if the index (scale) of the horizontal axis is absolute time, the value of the index will jump discontinuously at the boundary between the partial time series data, which makes it difficult for the operator to intuitively understand.

[0043] In step S104, instead of displaying the created image using the display unit 130, the image may be transmitted to a display device separate from the time-series data display device 100 for display, or may be transmitted to a printing device for printing. In other words, the output method for the created image may be selected according to the convenience of the worker (operator).

[0044] 8 shows an example of an image displayed on the display screen W of the display unit 130 in step S104. Waveforms of partial time series data relating to an event are displayed connected to each other so that they are adjacent to each other in the horizontal axis direction. That is, the stop of the industrial robot is treated as event data, and a graph is displayed in which partial time series data for each event is extracted and combined from the time series data monitoring the current value of the industrial robot. In this way, only partial time series data when an event occurs is combined and displayed, so that the worker (operator) can very easily check and compare graphs related to the occurrence of an event.

[0045] For example, if the event (stop) is a stop for inspection performed under normal conditions of the machine, the waveform of the partial time series data is similar to the waveform of one normal operation cycle shown in FIG. 5. According to the display image of the embodiment illustrated in FIG. 8, the worker (operator) can easily check the similarity of the waveforms. Also, if the event (stop) is a stop due to a malfunction of the machine, the waveform of the partial time series data will be an abnormal waveform dissimilar to the normal waveform, as shown in ABN1 and ABN2 in FIG. 8. In this way, since it is possible to easily find an abnormal waveform different from the normal waveform and compare it in association with the event, the worker (operator) can easily extract learning data when creating a failure prediction model.

[0046] 8 is an example in which the extraction conditions (predetermined events) in step S102 include both inspection stoppages during normal operation and stoppages due to breakdowns, but the worker can change the extraction conditions (predetermined events) in step S102 depending on the purpose of the work. For example, if it is desired to compare only waveforms related to stoppages due to breakdowns and to verify the correlation between the cause of the breakdown and the waveforms, the predetermined event as the extraction condition in step S102 can be set to a breakdown stoppage.

[0047] For example, FIG. 9(a) shows a display image in which a stop due to a failure is set as a predetermined event, and the waveforms of partial time series data related to the predetermined event are connected so as to be adjacent to each other in the horizontal axis direction. In this example, the indicator of the horizontal axis is the number of operation cycles, and a vertical line is added at the position where the graphs are connected so that the boundaries between the events can be easily seen. FIG. 9(b) shows detailed information related to the event contents stored in the extracted data storage means 123, and FIG. 9(a) uses this information to display detailed information on the event contents in correspondence with the waveforms of the partial time series data. From the waveforms of the graphs and detailed information on the event contents displayed on the screen, the worker (operator) can easily understand that in the case of a stop due to a failure caused by an excessive motor load, the maximum value of the waveform peak tends to be abnormally high as a sign of the stop. In the case of a stop due to a brake failure, the worker can easily understand that the number of peaks observed during one operation cycle increases as a sign of the stop. In this way, by understanding the details of the events from which partial time series data are extracted, it becomes easy to understand the characteristics of the time series data extracted by each event, and it is easy to determine whether or not to use the data as learning data for machine learning. Therefore, the worker (operator) can efficiently and easily extract learning data when creating a failure prediction model.

[0048] Furthermore, in order to improve the work efficiency of the worker (operator), an input area into which the worker (operator) can input information may be provided in the image in addition to detailed information related to the waveforms and events of the linked partial time-series data. For example, a check box, a pull-down menu, a flag, or the like for the worker (operator) to extract waveforms as learning data may be displayed in the image. Alternatively, a column for the worker (operator) to enter comments or memos may be provided and displayed in the image.

[0049] FIG. 10 is another example of a display image according to the embodiment. In this example, adjacent partial time series data (graphs) are arranged so as to be connected to each other with a predetermined short distance between them so that the worker (operator) can easily visually recognize the boundaries of the partial time series data. Furthermore, a mark indicating information related to the event content is added as a label to each graph. In this example, a mark indicating a stop in a normal state (e.g., inspection) and a mark indicating a stop due to an abnormal state (e.g., failure) are set as labels indicating sub-classifications related to the device stop, which is an event, and are displayed as labels in the image in association with each graph. In addition, a check box is displayed above each label for selecting a waveform to be adopted as learning data for creating a failure prediction model from among the waveforms. The labels and check boxes may be displayed by an instruction from the worker (operator) via the input unit 140, or the control program may be configured to display them automatically.

[0050] In the above-described example, partial time-series data of a single type of physical quantity, such as "current value," is extracted and displayed in graphs arranged along the horizontal axis, but what is displayed on one screen is not limited to graphs related to partial time-series data of a single type of physical quantity. If graphs of partial time-series data related to multiple types of physical quantities can be displayed on the same screen, for example, an operator can easily determine the correlation between different types of physical quantities in the event, which is convenient when extracting learning data for creating a failure prediction model.

[0051] FIG. 11 is another example of a display image according to the embodiment. In this example, in step S102 of the flowchart in FIG. 3, partial time series data of "current value" and "pressure" are extracted for a predetermined event of "device stop". Then, in step S103, the extracted partial time series data for each of "current value" and "pressure" are combined. Furthermore, in step S104, the graphs of "current value" and "pressure" are arranged vertically and displayed with the phases of the events aligned in the horizontal axis direction. As a result, it is understood that when an abnormal waveform occurs in which the peak value is excessive in the current value, an abnormal waveform occurs in which the peak value is excessively small in the pressure, and the operator can easily understand that the correlation between the current value and the pressure is large in this event. On the other hand, even if an abnormal waveform occurs in which the number of peaks observed in one operation cycle increases in the current value, the pressure is a normal waveform, so it is understood that the correlation between the current value and the pressure is small in this event. In this way, only partial time series data when a predetermined event occurs is combined and displayed, so that the operator (operator) can very easily check and compare graphs related to the occurrence of the event. When creating a failure prediction model, an operator can efficiently and easily extract learning data.

[0052] [Example of connecting a time series data display device to a robot] FIG. 12 shows an example in which a time-series data display device 100 according to the embodiment is connected to a six-axis articulated robot as an example of a mechanical device 10. In FIG. The six-axis articulated robot has links 200 to 206 connected in series by six rotary joints J1 to J6. The six-axis articulated robot is provided with sensors that measure the rotation speed of the motors of each rotary joint, sensors that measure the rotation angles of the joints, a torque sensor, a sensor that measures the motor current, a pressure sensor that measures the air pressure that drives the actuator, and the like. For example, a robot hand 210 can be attached and detached to the link at the tip as an actuator.

[0053] A time series data display device 100 of the embodiment is communicatively connected to the six-axis articulated robot, and the time series data display device 100 collects time series data of physical quantities related to the state of the robot and event data related to events that occur to the robot. A six-axis articulated robot repeatedly performs operations such as assembling parts to manufacture an item, and an operator can give commands to the time-series data display device 100 via the input unit 140 to create images and display or print the images.

[0054] For example, when a six-axis articulated robot performs an operation to manufacture an article, an image can be created by combining graphs of partial time-series data related to a predetermined event (e.g., a failure) and displayed on the display unit 130. By displaying in this manner, the operator can easily check the robot's past history related to the predetermined event, and can determine whether or not it is possible to have the robot continue the work of manufacturing an article. In other words, if the time-series data display device of the present invention is connected to a manufacturing device such as a robot and displays the partial time-series data, it is possible to manufacture an article while preventing stoppages due to failures.

[0055] Furthermore, the operator can use the time-series data display device 100 to create teacher data (learning data) used to construct a trained model for predicting robot failures. The operator can select a specific event from the event data acquired by the time-series data display device 100, cause the time-series data display device 100 to extract partial time-series data from the time-series data of various physical quantities, and display an image that allows easy comparison of graphs, etc. For example, by using the checkboxes illustrated in FIG. 10, the operator can easily flag data that is determined to be suitable as a teaching material for machine learning, and can easily construct teacher data (learning data).

[0056] [Other embodiments] The present invention is not limited to the above-described embodiment, and many modifications are possible within the technical concept of the present invention. For example, the embodiment of the present invention is not limited to the graphical display of physical quantities related to a single type of event. For example, in step S102 of the flowchart in Fig. 3, multiple types of predetermined events are set as extraction conditions. Then, in step S103, partial time series data of physical quantities for each of the multiple types of predetermined events is extracted, and a graph is created by combining the partial time series data along the horizontal axis, and in step S104, the graphs are displayed side by side on one screen. This is convenient when an operator wants to verify whether there is a correlation between multiple types of events regarding the physical quantities.

[0057] The present invention can also be realized by supplying a program for implementing one or more functions of the embodiments to a system or device via a network or a storage medium, and having one or more processors in a computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) for implementing one or more functions. A control program capable of implementing the information processing method or display method of the embodiments, and a non-transitory computer-readable recording medium storing the control program are also included in the embodiments of the present invention. In the present embodiment, a six-axis articulated robot has been described as an example of the mechanical device 10, but the present invention is not limited to this. For example, the mechanical device 10 can be any machine that can automatically perform operations such as expanding and contracting, bending and stretching, moving up and down, moving left and right, or turning, or a combination of these operations, based on information stored in a storage device provided in the control device. [Explanation of symbols]

[0058] 10...Mechanical device / 11...Sensor / 100...Time series data display device / 110...Control unit / 111...Data collection means / 112...Data extraction means / 113...Data combination means / 120...Memory unit / 121...Time series data storage means / 122...Event data storage means / 123...Extracted data storage means / 124...Combined data storage means / 130... Display unit / 140···Input unit / 200~206···Link / 1601···CPU / 1602···ROM / 1603···RAM / 1604···Operation unit / 1605···Display device / 1606···External storage device / 1607···Network interface / 1608···Network / 1104, 1121···External device / J1~J6···Rotational joint / W···Display screen

Claims

1. Acquire multiple time series data of multiple types of physical quantities related to the state of the machine; extracting a plurality of partial time series data corresponding to the physical quantities from the time series data; displaying an image in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data is smaller than a distance between display areas corresponding to the partial time series data when the partial time series data is displayed based on a time axis of the partial time series data; extracting a first partial time series data corresponding to an event that has occurred in the machine from among the plurality of partial time series data; extracting second partial time series data corresponding to the extracted first partial time series data; displaying the images arranged in correspondence with the first partial time series data and the second partial time series data; 23. An information processing method comprising:

2. A plurality of the partial time series data are extracted based on event data relating to the event.

2. The information processing method according to claim 1,

3. acquiring, as the event data, event data relating to a plurality of types of events occurring in the machine, extracting a plurality of the partial time series data relating to a plurality of types of predetermined events selected from the plurality of types of events, and displaying the image in which information relating to the plurality of the partial time series data for the plurality of types of predetermined events is arranged.

3. The information processing method according to claim 2.

4. The image includes information related to the event.

4. The information processing method according to claim 2 or 3.

5. The event is set based on a peak of the physical quantity.

5. The information processing method according to claim 2, wherein the first and second inputs are input to the first and second inputs.

6. In the event, At least one of the following is set: when the value of the peak is equal to or greater than a predetermined threshold value; or when the number of times of the peak is equal to or greater than a predetermined number.

6. The information processing method according to claim 5,

7. The event data is date and time data on when the event occurred.

7. The information processing method according to claim 2, wherein the first and second inputs are input to the first and second inputs.

8. The time series data before the partial time series data is extracted is arranged on a linear scale using time as an index.

8. The information processing method according to claim 1,

9. The image is displayed on a display unit.

9. The information processing method according to claim 1,

10. The image includes an image in which graphs of the physical quantities related to the extracted plurality of partial time series data are linked together.

10. The information processing method according to claim 1,

11. The image includes an image in which graphs of the physical quantities related to the extracted plurality of partial time series data are arranged at a predetermined distance from each other.

11. The information processing method according to claim 1,

12. The image includes an input area into which an operator can input information.

12. The information processing method according to claim 1,

13. The input area allows a label indicating a classification of the partial time series data to be set.

13. The information processing method according to claim 12.

14. the partial time series data is displayed on the image regardless of the time axis in the time series data before the partial time series data is extracted.

14. The information processing method according to claim 1,

15. The partial time series data is displayed on the image according to a sampling number or a number of operation cycles.

15. The information processing method according to claim 1,

16. arranging the partial time series data in the image so that the distance between display areas corresponding to the partial time series data is smaller than the distance between display areas corresponding to the partial time series data when the partial time series data is arranged in the time series data before the partial time series data is extracted; 16. The information processing method according to claim 1,

17. Acquire multiple time series data of multiple types of physical quantities related to the state of the machine; extracting a plurality of partial time series data corresponding to the physical quantities from the time series data; displaying an image in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data is smaller than a distance between display areas corresponding to the partial time series data when the partial time series data is displayed based on a time axis of the partial time series data; extracting a first partial time series data corresponding to an event that has occurred in the machine from among the plurality of partial time series data; extracting second partial time series data corresponding to the extracted first partial time series data; displaying the images arranged in correspondence with the first partial time series data and the second partial time series data; 23. An information processing apparatus comprising:

18. A display method for displaying a plurality of types of physical quantities related to a state of a mechanical device, comprising: displaying an image in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data, the distance being smaller than a distance between display areas corresponding to the partial time series data when information related to the partial time series data extracted from the plurality of time series data of the physical quantity is displayed based on a time axis of the time series data; acquiring time series data relating to a plurality of types of the physical quantities as the time series data, extracting a plurality of the partial time series data for the plurality of types of the physical quantities from the time series data, and displaying the image in which information relating to the plurality of the partial time series data for the plurality of types of the physical quantities is arranged according to a sampling number or a number of operation cycles; A display method comprising:

19. A display device that displays a plurality of types of physical quantities related to a state of a mechanical device, displaying an image in which the partial time series data is arranged so that the distance between display areas corresponding to the partial time series data, the distance being smaller than a distance between display areas corresponding to the partial time series data when information related to the partial time series data extracted from the plurality of time series data of the physical quantity is displayed based on a time axis of the time series data; acquiring time series data relating to a plurality of types of the physical quantities as the time series data, extracting a plurality of the partial time series data for the plurality of types of the physical quantities from the time series data, and displaying the image in which information relating to the plurality of the partial time series data for the plurality of types of the physical quantities is arranged according to a sampling number or a number of operation cycles; A display device comprising:

20. A program for causing a computer to execute the information processing method according to any one of claims 1 to 16 or the display method according to claim 18.

21. A computer-readable recording medium having the program according to claim 20 recorded thereon.

22. An article is manufactured using the machine whose state information is acquired by the information processing device according to claim 17. A method for producing an article comprising the steps of:

23. The information processing device according to claim 17 creates the image, and displays the image to an operator when acquiring learning data for creating a trained model for predicting a failure of the mechanical device. A method for acquiring learning data comprising the steps of:

Images