Abnormality diagnosis device, abnormality diagnosis method, and program

Abstract

In order to solve the problem of providing a technology for notifying, when an abnormality in a welded portion is detected, a cause of the abnormality, this abnormality diagnosis device comprises: an acquisition means (21) that acquires time-series data that includes at least one of a current value and a voltage value at the time of first welding; an abnormality determination means (22) that, on the basis of the time-series data acquired by the acquisition means, uses an abnormality determination model, which has learned a relationship between the absence / presence of an abnormality in the welded portion and time-series data that includes at least one of the current value and the voltage value at the time of a second welding, to determine whether there is an abnormality in the welded portion at the time of the first welding; and an output means (23) that outputs, when the abnormality determination means has determined that there is the abnormality, at least one of a determination result, the cause of the abnormality, and a handling method for resolving the abnormality.

Description

[Technical field]

[0001] The present invention relates to an abnormality diagnosis device, an abnormality diagnosis method, and a program that, when an abnormality is detected in a welded portion, can notify the cause of the detected abnormality. [Background technology]

[0002] Welding is known as a method for joining steel plates. For example, non-destructive testing of welded joints is performed to check the strength of the joined steel plates. Radiation (X-ray) transmission testing, ultrasonic testing, etc. are widely used as non-destructive testing.

[0003] Patent Document 1 discloses a technique for generating frequency spectrum data by performing frequency analysis on a time-series waveform of a welding voltage or welding current detected from a welding power source, and for determining a welding condition based on the frequency spectrum data.

[0004] Furthermore, Patent Document 2 proposes a method for accurately determining an abnormality in welding even when the current waveform Xp in the welding process locally deviates from the normal current waveforms W1 to Wn. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application No. 2018-144069 [Patent Document 2] Japanese Patent Application No. 2019-118954 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in Patent Document 1 and Patent Document 1, even if an abnormality could be accurately detected, it was difficult to know the cause of the welding abnormality.

[0007] One aspect of the present invention has been made in consideration of the above-mentioned problems, and one example of a purpose of the present invention is to provide a technology that, when an abnormality is detected in a welded portion, notifies the cause of the detected abnormality. [Means for solving the problem]

[0008] An abnormality diagnosis device according to one aspect of the present invention comprises an acquisition means for acquiring time series data including at least one of a current value or a voltage value during a first welding process, an abnormality judgment means for judging whether or not there is an abnormality in the welded portion during the first welding process based on the time series data acquired by the acquisition means using an abnormality judgment model that has learned the relationship between time series data including at least one of a current value or a voltage value during a second welding process and the presence or absence of an abnormality in the welded portion, and an output means for outputting, when the abnormality judgment means judges that there is an abnormality, a result of the judgment and at least one of a cause of the abnormality and a countermeasure for resolving the abnormality.

[0009] An abnormality diagnosis method according to one aspect of the present invention includes acquiring time series data including at least one of a current value or a voltage value during a first welding process, determining whether or not there is an abnormality in the welded portion during the first welding process based on the acquired time series data using an abnormality determination model that has learned the relationship between time series data including at least one of a current value or a voltage value during a second welding process and the presence or absence of an abnormality in the welded portion, and if it is determined that there is an abnormality, outputting a result of the determination and at least one of a cause of the abnormality and a countermeasure for resolving the abnormality.

[0010] A program according to one aspect of the present invention causes a computer to function as an abnormality diagnosis device comprising: acquisition means for acquiring time series data including at least one of a current value or a voltage value during a first welding; abnormality judgment means for judging whether or not there is an abnormality in the welded portion during the first welding based on the time series data acquired by the acquisition means, using an abnormality judgment model that has learned the relationship between time series data including at least one of a current value or a voltage value during a second welding and the presence or absence of an abnormality in the welded portion; and output means for outputting, when the abnormality judgment means judges that there is an abnormality, a result of the judgment and at least one of a cause of the abnormality and a countermeasure for resolving the abnormality. Effect of the Invention

[0011] According to one aspect of the present invention, it is possible to provide a technique for, when an abnormality is detected in a welded portion, notifying a cause of the detected abnormality. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing an example of the configuration of an abnormality diagnosis device according to a first exemplary embodiment of the present invention. [Diagram 2] 3 is a flowchart showing the flow of an abnormality diagnosis method according to the first exemplary embodiment of the present invention. [Diagram 3] FIG. 4 is a block diagram showing an example of the configuration of a welding condition diagnosis device according to an exemplary embodiment 2 of the present invention. [Figure 4] FIG. 1 is a diagram illustrating an example of welding by a welding machine. [Diagram 5] 2A and 2B are diagrams showing examples of sensors incorporated in the electrode unit 101. [Figure 6] FIG. 13 is a diagram illustrating an example of a shifted time-series value. [Figure 7] 1A to 1C are diagrams illustrating an abnormality in a weld bead formed in a welded portion. [Figure 8] 10 is a flowchart illustrating an example of a welding condition diagnosis process. [Figure 9] 11 is a flowchart illustrating a detailed example of pre-processing. [Figure 10] 10 is a diagram illustrating an example of abnormality information output by the output unit 23. FIG. [Figure 11] FIG. 11 is a diagram showing another example of a sensor incorporated in an electrode unit 101 according to an exemplary embodiment 4 of the present invention. [Figure 12] FIG. 11 is a block diagram showing an example of the configuration of a welding condition diagnosis and learning device 10A according to an exemplary embodiment 5 of the present invention. [Figure 13] 11 is a flowchart illustrating an example of a learning process. [Figure 14] FIG. 2 is a block diagram showing an example of a computer that executes instructions of a program, which is software that realizes each function. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Example Embodiment 1 A first exemplary embodiment of the present invention will be described in detail with reference to the drawings. This exemplary embodiment is a basic form of the exemplary embodiments described later.

[0014] <Outline of the abnormality diagnosis device 20> The abnormality diagnostic device 20 according to this exemplary embodiment is, generally speaking, a device for diagnosing an abnormality in a welded portion of a workpiece.

[0015] More specifically, the abnormality diagnosis device 20, for example, An acquisition means for acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination means for determining whether or not there is an abnormality in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition means; The device further includes an output means for outputting, when the abnormality determination means determines that there is an abnormality, the result of the determination and at least one of the cause of the abnormality and a method of dealing with the abnormality.

[0016] <Configuration of Abnormality Diagnosis Device 20> The configuration of an abnormality diagnosis device 20 according to this exemplary embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the abnormality diagnosis device 20.

[0017] 1, the abnormality diagnosis device 20 includes an acquisition unit 21, an abnormality determination unit 22, and an output unit 23. The acquisition unit 21 is configured to realize an acquisition means in this exemplary embodiment. The output unit 23 is configured to realize an output means in this exemplary embodiment.

[0018] The acquisition unit 21 acquires time-series data including at least one of a current value and a voltage value during the first welding.

[0019] The first welding time is, for example, the welding of the workpiece currently being performed. As an example, the workpiece is two steel plates that are sufficiently larger than the size of the welding machine. The welding machine welds the steel plates while moving the joint portion of the steel plates.

[0020] The welding machine includes a plurality of sensors, such as a position sensor, a speed sensor, a current sensor, a voltage sensor, and the like, and each sensor outputs a sensor value. The acquiring unit 21 acquires, for example, the sensor values ​​output from the plurality of sensors at a predetermined time interval as time-series data.

[0021] The abnormality judgment unit 22 judges whether or not there is an abnormality in the welded part during the first welding based on the time series data acquired by the acquisition unit 21, using an abnormality judgment model that has learned the relationship between the time series data including at least one of the current value or the voltage value during the second welding and the presence or absence of an abnormality in the welded part.

[0022] The anomaly determination model is a model obtained by performing machine learning in advance, by associating time-series data of the second welding, which is the welding of the workpiece performed in the past, with the presence or absence of an anomaly in the welded part. As an example, the anomaly in the welded part is an anomaly in the weld bead formed in the welded part of the steel plate.

[0023] The abnormality determination model determines the presence or absence of an abnormality in the welded portion based on the time-series data obtained during the first welding.

[0024] When the abnormality determination means determines that an abnormality exists, the output unit 23 outputs the result of the determination and at least one of the cause of the abnormality and a countermeasure for eliminating the abnormality.

[0025] As an example, when the abnormality judgment model judges that there is an abnormality, the abnormality judgment unit 22 refers to correspondence information in which the judgment result is associated with at least one of the cause of the abnormality and a countermeasure for resolving the abnormality. Then, the output unit 23 outputs the judgment result and at least one of the cause of the abnormality and the countermeasure for resolving the abnormality associated with the judgment result.

[0026] Here, examples of causes of the abnormality include excessive / insufficient speed of the welding machine, insufficient current applied to the welded portion, etc. Examples of methods for eliminating the abnormality include adjusting the moving speed of the welding machine, adjusting the current applied to the welded portion, etc.

[0027] As an example, the correspondence information is a table in which the determination result based on the anomaly determination model is associated with the cause of the anomaly and / or a countermeasure for eliminating the anomaly.

[0028] <Effects of the abnormality diagnosis device 20> According to the anomaly diagnosis device of this exemplary embodiment, whether or not there is an anomaly in the welded portion during the first welding is determined based on the time series data during the first welding using an anomaly determination model that has learned the relationship between the time series data during the second welding and the presence or absence of an anomaly in the welded portion. If it is determined that there is an anomaly, the determination result and at least one of the cause of the anomaly and a countermeasure method for eliminating the anomaly are output. Therefore, it is possible to diagnose an anomaly in the welded portion without carrying out non-destructive testing, which has been conventionally performed, and it is possible to perform anomaly diagnosis in real time at low cost.

[0029] In addition, since the output information includes at least one of the cause of the abnormality and a countermeasure for resolving the abnormality, the time required to analyze the cause of the abnormality and to consider a countermeasure can be reduced, and as a result, the welding process can be completed quickly.

[0030] <Flow of abnormality diagnosis method by abnormality diagnosis device 20> The flow of the abnormality diagnosis method executed by the abnormality diagnosis device 20 configured as above will be described with reference to Fig. 2. Fig. 2 is a flowchart showing the flow of the abnormality diagnosis method. As shown in the figure, the abnormality diagnosis method includes steps S11 and S12.

[0031] In step S11, the acquisition unit 21 acquires time-series data including at least one of a current value and a voltage value during the first welding.

[0032] In step S12, the abnormality judgment unit 22 judges whether or not there is an abnormality in the welded part during the first welding based on the acquired time series data, using an abnormality judgment model that has learned the relationship between the time series data including at least one of the current value or the voltage value during the second welding and the presence or absence of an abnormality in the welded part.

[0033] In step S13, if it is determined that there is an abnormality, the output unit 23 outputs the result of the determination and at least one of the cause of the abnormality and a countermeasure for eliminating the abnormality.

[0034] In this manner, the abnormality diagnosis process is carried out.

[0035] <Effects of information processing methods> According to the anomaly diagnosis method of this exemplary embodiment, whether or not there is an anomaly in the welded portion during the first welding is determined based on the time series data during the first welding using an anomaly determination model that has learned the relationship between the time series data during the second welding and the presence or absence of an anomaly in the welded portion. If it is determined that there is an anomaly, the result of the determination and at least one of the cause of the anomaly and a method of dealing with the anomaly are output. Therefore, it is possible to diagnose an anomaly in the welded portion without carrying out non-destructive testing, which has been conventionally performed, and it is possible to perform anomaly diagnosis in real time at low cost.

[0036] In order to carry out conventional non-destructive testing, testing equipment is required, resulting in increased costs.

[0037] Furthermore, this type of non-destructive testing only inspects a range of 1 m at each end of the welded joint, regardless of the size or length of the steel plate, so any welding abnormalities in other positions could not be detected.

[0038] Furthermore, non-destructive testing is performed after welding is completed by moving the joined steel plates to an inspection site. If a welding abnormality is discovered during non-destructive testing, the steel plates must be returned to the welding site and re-welded, so there was a need to reduce the time and energy required to move the steel plates.

[0039] In addition, since the output information includes at least one of the cause of the abnormality and a countermeasure for resolving the abnormality, the time required to analyze the cause of the abnormality and to consider a countermeasure can be reduced, and as a result, the welding process can be completed quickly.

[0040] Exemplary embodiment 2 A second exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the first exemplary embodiment are given the same reference numerals, and the description thereof will be omitted as appropriate.

[0041] <Configuration of welding condition diagnosis device 10> The configuration of the welding condition diagnosis device 10 according to this exemplary embodiment will be described with reference to FIG.

[0042] Fig. 3 is a block diagram showing an example of the configuration of welding condition diagnosis device 10. As shown in Fig. 3, welding condition diagnosis device 10 includes a control unit 50, a storage unit 30, a communication unit 41, an input unit 42, and an output unit 23.

[0043] The storage unit 30 is configured, for example, with a semiconductor memory device or the like, and stores data. For example, a feature DB described below is stored in the storage unit 30. In addition, the storage unit 30 stores model parameters of an anomaly determination model 81.

[0044] The communication unit 41 is an interface for connecting the welding condition diagnosis device 10 to a network. Although the specific configuration of the network does not limit the present exemplary embodiment, as an example, a wireless LAN (Local Area Network), a wired LAN, a WAN (Wide Area Network), a public line network, a mobile data communication network, or a combination of these networks can be used.

[0045] The input unit 42 accepts various inputs to the welding condition diagnosis device 10. Although the specific configuration of the input unit 42 is not limited to this exemplary embodiment, as an example, the input unit 42 may be configured to include input devices such as a keyboard and a touch pad. The input unit 42 may also be configured to include a data scanner that reads data via electromagnetic waves such as infrared rays and radio waves, and a sensor that senses the environmental state.

[0046] The output unit 23 has the same functions as those described in the exemplary embodiment 1, and is a functional block that outputs the processing results by the welding condition diagnosis device 10. Although the specific configuration of the output unit 23 is not limited to this exemplary embodiment, as an example, the output unit 23 is configured with a display, a speaker, a printer, etc., and displays various processing results by the welding condition diagnosis device 10 on a screen or outputs them as sounds or figures.

[0047] Furthermore, the output unit 23 supplies information to be output to the communication unit 41 as necessary, and provides the information to other devices via the network.

[0048] The welding condition diagnosis device 10 is connected to a welding machine 100, which will be described later, by wire or wirelessly.

[0049] The control unit 50 includes an acquisition unit 21 and an abnormality determination unit 22. In the example of FIG.

[0050] The pre-processing unit 61 shifts in the time direction the time series data acquired from the sensors provided corresponding to the multiple electrodes provided in the welding machine 100, and generates shifted time series data. The generation of the shifted time series data by the pre-processing unit will be described later in detail.

[0051] 3, the abnormality determination unit 22 has an abnormality determination model 81. The abnormality determination model 81 is a model that learns the relationship between time-series data from a second welding performed in the past and the presence or absence of an abnormality in the welded portion. For example, time-series data from welding performed in the past is accumulated, and a process is executed to convert the accumulated time-series data into feature quantities. Then, the feature quantities are stored in association with labels indicating the presence or absence of an abnormality in the welded portion.

[0052] As an example, the feature is binary data that is obtained by extracting features related to temporal changes in time-series data and features related to the relationships between each sensor value, and synthesizing the extracted features.The similarity between the accumulated feature and the newly obtained feature is then learned by machine learning.

[0053] By using such machine learning, the anomaly determination model 81 can search for a feature quantity similar to the input feature quantity from among the previously stored feature quantities. Furthermore, the anomaly determination model 81 can determine whether the input feature quantity represents an abnormality in the welded part by referring to the label added to the searched feature quantity.

[0054] That is, by inputting the post-shift time-series data during the first welding currently being performed to the abnormality determination model 81, abnormality information regarding whether or not there is an abnormality in the welded portion is generated.

[0055] The anomaly determination model 81 converts the shifted time series data generated by the preprocessing unit 61 into features. At this time, the anomaly determination model 81 extracts, for example, multiple sensor values ​​at the same time from the shifted time series data to generate partial time series data.

[0056] Then, the anomaly determination model 81 extracts features of the temporal changes of the partial time series data and the relationship between the sensor values, synthesizes these features, and converts the synthesized features into binary data. In this way, the anomaly determination model 81 converts the shifted time series data into features.

[0057] Note that the shifted time series data may not be generated. In this case, the anomaly determination model 81 converts the time series data into features.

[0058] Furthermore, abnormality determination model 81 compares the feature obtained by the conversion with the feature stored in a feature DB stored in storage unit 30. The feature DB stores, for example, feature obtained in a welding process previously performed by welding machine 100.

[0059] The features stored in the feature DB are labeled with a label indicating an abnormality in the weld bead formed in the welded portion of the workpiece during the welding process in which the feature was obtained. For example, a label indicating that the weld bead is normally formed and a label indicating that the weld bead is abnormally formed are attached.

[0060] If the formation of the weld bead is abnormal, the type of abnormality is also indicated by a label. The details of the types of abnormality will be described later.

[0061] The abnormality judgment model 81 searches for features stored in the feature DB that are obtained during the first welding and are similar to the features obtained by converting the shifted time series data.

[0062] When the shifted time-series data is not generated, the anomaly determination model 81 searches for a feature quantity similar to the feature quantity obtained by conversion.

[0063] Then, the abnormality determination model 81 refers to the label attached to the searched feature amount and generates information regarding whether or not there is an abnormality in the welded portion during the first welding.

[0064] <Example of welding using a welding machine> 4 is a diagram for explaining an example of welding by the welding machine 100. Here, an example of welding steel plates using the welding machine 100 that performs arc welding will be explained.

[0065] As shown in the figure, a welding machine 100 welds a steel plate 131 and a steel plate 132. Before welding, the steel plate 131 and the steel plate 132 are fixed by a tab plate 133 and a tab plate 134 so as to be in contact with each other.

[0066] Steel plate 131 and steel plate 132 are steel plates with a length L. In this example, steel plate 131 and steel plate 132 are arranged so that their longitudinal end faces are in contact with each other, and the longitudinal directions of the steel plates are welded to form welded parts.

[0067] The welding machine 100 is provided with an electrode unit 101, which is provided with three electrodes: an electrode 101A, an electrode 101B, and an electrode 101C.

[0068] Since the length L of the steel plate is sufficiently long compared to the electrode unit 101, the electrode unit 101 moves from left to right according to the arrow in the figure, welding the steel plate 131 and the steel plate 132. In this example, the electrode unit 101 performs welding while moving along the longitudinal direction of the steel plate 131 and the steel plate 132.

[0069] When a high voltage is applied to each electrode, a high-temperature arc is generated between the welded parts of steel plates 131 and 132 and each electrode, causing the electrodes, steel plates 131 and 132 to melt and form weld beads.

[0070] Electrode unit 101 moving over the steel plate is provided with electrodes 101A, 101B, and 101C, so that electrode 101C reaches a predetermined position on the welded part first, followed by electrode 101A, and finally electrode 101A. In this way, welding machine 100 is configured to generate an arc three times at the same position with the three electrodes to form a weld bead.

[0071] Furthermore, welding by welding machine 100 is performed under the control of a control unit (not shown). For example, the movement of electrode unit 101, the strength of the voltage applied to each electrode, and the like are controlled according to a control procedure previously set in the control unit. In this manner, the welding process is automated by welding machine 100.

[0072] When the control unit controls the welding machine 100, sensors are required to detect the states of the electrode unit 101 and each electrode. For example, sensors such as a position sensor that detects the position of the electrode unit 101 during movement are required. Such sensors are incorporated into the welding machine 100 when it is manufactured.

[0073] Welding condition diagnosis device 10 is connected to welding machine 100 by wire or wirelessly so as to be able to communicate with a control unit, for example, and acquires a sensor value output from a sensor incorporated in welding machine 100.

[0074] In this manner, the welding machine 100 includes an electrode unit 101 that moves over the workpiece and has a plurality of different electrodes that supply welding current to the weld, and the acquisition unit 21 acquires, as time-series data, the sensor values ​​output by each of the plurality of sensors as the electrode unit moves.

[0075] 5 is a diagram showing an example of sensors incorporated in the electrode unit 101. In this example, a sensor unit 111, a sensor unit 121A, a sensor unit 121B, and a sensor unit 121C are provided.

[0076] Sensor unit 111 is provided corresponding to electrode unit 101. Meanwhile, sensor unit 121A is provided corresponding to electrode 101A, sensor unit 121A is provided corresponding to electrode 101B, and sensor unit 121C is provided corresponding to electrode 101C. Note that unless there is a particular need to distinguish between them, sensor unit 121A, sensor unit 121B, and sensor unit 121C will be collectively referred to as sensor unit 121.

[0077] Each sensor unit includes two sensors, and acquisition unit 21 acquires the sensor values ​​output by the sensors of these sensor units as time series data. That is, the time series data during the first welding includes a plurality of time series data including first time series data and second time series data acquired corresponding to each of a plurality of electrodes provided in the welding machine.

[0078] As described above, the preprocessing unit 61 shifts the time series data acquired from the sensor unit provided corresponding to at least one electrode among the plurality of different electrodes in the time direction. That is, the time series data output from at least one sensor unit among the sensor units 121A to 121C is shifted in the time direction with respect to the time series data output from the sensor unit 111.

[0079] In this manner, the pre-processing unit 61 generates shifted time series data for the first welding time by relatively shifting the first time series data and the second time series data in the time series data for the first welding time in the time direction.

[0080] Then, the abnormality determination model 81 receives the shifted time-series data as an input and determines whether or not there is an abnormality in the welded portion during the first welding.

[0081] As described above, welding machine 100 is configured to generate an arc three times at the same location with three electrodes to form a weld bead.

[0082] For example, as the electrode unit 101 moves, the electrode 101C reaches a position (hereinafter referred to as the central position) corresponding to the length L / 2 of the steel plates 131 and 132 at time t, a predetermined time after the electrode unit 101 starts moving. After that, time b passes, and at time t+b, the electrode 101B reaches the central position. Furthermore, time a passes from time t, and at time t+a (a>b), the electrode 101A reaches the central position.

[0083] In this way, the weld bead at the center is formed by the arc generated by electrode 101C at time t, the arc generated by electrode 101B at time t+b, and the arc generated by electrode 101A at time t+a. Therefore, in order to diagnose an abnormality in the weld bead at the center, it is necessary to shift the time series data of the sensor values ​​related to electrodes 101B and 101A in the time direction.

[0084] That is, it is necessary to shift the time series data obtained from the sensor included in sensor unit 121B corresponding to electrode 101B by time b, and to shift the time series data obtained from the sensor included in sensor unit 121A corresponding to electrode 101A by time a. Here, time b and time a are determined according to the distance between the respective electrodes and the speed at which electrode unit 101 moves.

[0085] The speed at which the electrode unit 101 moves can be determined, for example, by a sensor value output by a speed sensor 113, which will be described later.

[0086] Therefore, the pre-processing unit 61 generates shifted time series data by shifting the time series data of the sensor values ​​of the sensor unit 121B and the time series data of the sensor values ​​of the sensor unit 121A out of the time series data of the sensor values ​​of the sensor unit 111 and the sensor unit 121.

[0087] That is, the acquisition unit 21 further acquires speed information indicating the speed during the first welding, and shifts the first time-series data and the second time-series data relatively in the time direction according to the speed indicated by the speed information.

[0088] 6 is a diagram for explaining an example of shifted time series data. In this diagram, the horizontal axis represents time and the vertical axis represents sensor values, which show changes in the sensor values ​​over time, and the time series data of each sensor value is shown as a waveform.

[0089] In this example, waveform TS111 of time series data of sensor values ​​of sensor unit 111, waveform TS121C of time series data of sensor values ​​of sensor unit 121C, waveform TS121B of time series data of sensor values ​​of sensor unit 121B, and waveform TS121A of time series data of sensor values ​​of sensor unit 121A are shown.

[0090] In reality, each of the sensor units 111, 121A, 121B, and 121C may include multiple sensors, and each sensor unit may output time series data of multiple sensor values. However, for simplicity, the following description will be given assuming that each sensor unit outputs one piece of time series data.

[0091] As shown in the figure, the start position of waveform TS121B of the time series data is delayed by time b from the start positions of waveform TS111 of the time series data and waveform TS121C of the time series data, and the start position of waveform TS121A of the time series data is delayed by time a from the start positions of waveform TS111 of the time series data and waveform TS121C of the time series data.

[0092] By doing this, it is possible to obtain time series data as if the three electrodes had reached the central position at time t.

[0093] In this example, the time series data output from sensor unit 121A and the time series data output from sensor unit 121B are delayed with the time series data output from sensor unit 121C as a reference. However, for example, the time series data output from sensor unit 121B may be used as a reference to advance the time series data output from sensor unit 121C in the time direction and delay the time series data output from sensor unit 121A. Alternatively, the time series data output from sensor unit 121A may be used as a reference to advance the time series data output from sensor unit 121B and the time series data output from sensor unit 121C in the time direction.

[0094] Here, an example has been described in which sensor unit 121A, sensor unit 121B, and sensor unit 121C are provided in electrode unit 101. However, for example, only one of sensor unit 121A, sensor unit 121B, and sensor unit 121C may be provided in electrode unit 101. In this case, it is not necessary to shift the time series data.

[0095] Returning to FIG. 5, an example of a sensor incorporated in the electrode unit 101 will be further described.

[0096] The sensor unit 111 is a sensor unit provided in the electrode unit 101, and has a position sensor 112 and a speed sensor 113. The position sensor 112 is a sensor that detects the position of the electrode unit 101. The speed sensor 113 is a sensor that detects the speed at which the electrode unit 101 moves.

[0097] The position sensor 112 detects, for example, how far the electrode unit 101 has moved in the X-axis, Y-axis, and Z-axis directions starting from a default position of the electrode unit 101, and outputs the detected amount as a sensor value. The default position is, for example, the position of the electrode unit 101 before the start of the welding process.

[0098] The speed sensor 113 detects the speed at which the electrode unit 101 moves. As an example, the speed sensor 113 detects the moving speed of the electrode unit from the number of rotations of a motor that drives a moving mechanism that moves the electrode unit 101 along the longitudinal direction of the steel plates 131 and 132. The speed sensor 113 outputs the detected speed as a sensor value.

[0099] The sensor unit 121 is provided corresponding to each electrode. The sensor unit 121A has a current sensor 122A that detects a current value output by the electrode 101A when an arc is generated, and a voltage sensor 123A that detects a voltage value applied to the electrode 101A.

[0100] Similarly, the sensor unit 121B and the sensor unit 121C each include a current sensor 122B and a voltage sensor 123B, and a current sensor 122C and a voltage sensor 123C.

[0101] In addition, when there is no need to distinguish between the current sensors 122A to 122C and the voltage sensors 123A to 123C, they will be simply referred to as the current sensor 122 and the voltage sensor 123.

[0102] Here, an example has been described in which each sensor unit is incorporated in electrode unit 101, but for example, some or all of each sensor unit may be incorporated in a control unit of welding machine 100. In short, it is sufficient that each sensor unit shown in FIG. 5 is incorporated in welding machine 100.

[0103] Note that FIG. 5 shows an example of a sensor included in the sensor unit, and other sensors may be included.

[0104] Fig. 7 is a diagram illustrating abnormalities in a weld bead formed in a welded portion. As described above, a weld bead is formed between steel plate 131 and steel plate 132 as a result of melting of steel plate 131 and steel plate 132. Fig. 7 shows three examples of abnormalities in a weld bead.

[0105] The weld bead shown at the top of the figure has a concave upper end B11. A weld bead with a concave upper end is called an undercut and is often formed when the welding speed is too fast, because if the welding speed is too fast, the amount of deposited metal is likely to be insufficient.

[0106] Moreover, the weld bead shown in the center of the figure has its upper end B12 spilling over from the welded portion between steel plate 131 and steel plate 132. A weld bead whose upper end spills over from the welded portion in this manner is called an overlap, and is often formed when the welding speed is too slow. This is because an excessive amount of weld metal is deposited when the welding speed is too slow.

[0107] Furthermore, the weld bead shown at the bottom of the figure has its bottom end B13 not reaching the bottom, which is the bottom end of the weld between steel plates 131 and 132. A weld bead whose bottom end does not reach the bottom of the weld like this is called poor penetration, and is often formed when the current output by the electrode is too small. This is because if the current output by the electrode is too small, the arc becomes weak and the deposited metal does not melt sufficiently.

[0108] The multiple features stored in advance in the feature database are labeled with labels representing undercuts, where the end of the weld bead is recessed, overlaps, where the end of the weld bead is formed outside the weld, or poor penetration, where the weld bead is not formed at the bottom of the weld.

[0109] Next, an example of a welding condition diagnosis process performed by the welding condition diagnosis device 10 of this exemplary embodiment will be described with reference to the flowchart of FIG.

[0110] In step S31, the preprocessing unit 61 executes preprocessing, thereby generating shifted time series data in which the time series data acquired from the sensor unit is shifted in the time direction according to the speed at which the electrode unit 101 moves, as described above.

[0111] Here, the pre-processing of step S31 will be described in detail with reference to the flowchart of FIG.

[0112] In step S51, the preprocessing unit 61 acquires the sensor value of the speed sensor 113 of the sensor unit 111, and identifies the speed at which the electrode unit moves.

[0113] In step S52, the pre-processing unit 61 calculates the difference in time when each electrode reaches the same position on the weld. At this time, for example, as described above, it is calculated how late each of the electrodes 101B and 101A reaches the central tail position after the electrode 101C. Note that the distance between each electrode is notified to the pre-processing unit 61 in advance.

[0114] In step S53, the pre-processing unit 61 shifts the time-series data of the sensor values ​​output from the current sensor 122 and the voltage sensor 123 in the time direction.

[0115] 6, for example, the time series data of the sensor values ​​output from the current sensor 122B and the voltage sensor 123B included in the sensor unit 121B is shifted by time b, and the time series data of the sensor values ​​output from the current sensor 122A and the voltage sensor 123A included in the sensor unit 121A is shifted by time a.

[0116] In step S54, the pre-processing unit 61 generates shifted time-series data.

[0117] In this manner, pre-processing is performed.

[0118] Returning to Fig. 8, the description will continue. After the process of step S31, in step S32, the anomaly determination model 81 converts the shifted time-series data generated by the pre-processing unit 61 into a feature quantity.

[0119] In step S33, the abnormality determination model 81 searches for feature amounts similar to the feature amount obtained by the process in step S32, from among the feature amounts stored in the feature amount DB.

[0120] In step S34, the abnormality determination model 81 identifies a feature quantity similar to the feature quantity obtained in the process of step S32 from among the feature quantities stored in the feature quantity DB as the search results in the process of step S33.

[0121] In step S35, anomaly determination model 81 generates anomaly information by referring to the label attached to the feature amount identified in the process of step S34. That is, an anomaly of the weld bead formed in the welded portion and the type of the anomaly are detected from the feature amount obtained in the welding process currently being executed.

[0122] As described above, the features stored in the feature DB are labeled with an abnormality in the weld bead formed on the welded portion of the workpiece in the welding process in which the features were obtained. For example, the abnormality information can include information indicating whether the weld bead is abnormal or not, as well as information indicating the cause of the abnormality.

[0123] For example, if a label is attached to a feature identified in step S34 that is similar to a feature of the time-series data of the welding process being performed and indicates an undercut, the cause of the abnormality is determined to be "the welding speed is too fast."

[0124] Also, for example, if the label attached to the feature similar to the feature of the time series data of the welding process being performed indicates an overlap, the cause of the abnormality will be "the welding speed is too slow."

[0125] Alternatively, if the label attached to the feature similar to the feature of the time-series data of the ongoing welding process indicates poor welding, the cause of the abnormality is determined to be "the current value output by the electrode is too small."

[0126] In this way, it is possible to identify the cause of the abnormality based on the label attached to the feature, so the abnormality information can include information indicating the cause of the abnormality as well as information indicating the abnormality in the weld bead.

[0127] Furthermore, the abnormality information may include a countermeasure based on the cause of the abnormality.

[0128] For example, if a label attached to a feature similar to a feature of the time-series data of a welding process in progress indicates an undercut, the countermeasure would be to "slow down the moving speed of the electrode unit 101."

[0129] Also, for example, if the label attached to the feature similar to the feature of the time series data of the welding process being performed indicates an overlap, the countermeasure would be to "increase the moving speed of the electrode unit 101."

[0130] Alternatively, if the label attached to the feature similar to the feature of the time-series data of the ongoing welding process indicates poor penetration, the solution would be to "increase the current applied to the electrode of the electrode unit 101."

[0131] In this manner, the abnormality information can include a countermeasure for eliminating the abnormality in the weld bead.

[0132] The output unit 23 outputs the anomaly information generated in the process of step S35 as information on whether or not there is an abnormality. That is, the welding condition diagnosis device 10 outputs anomaly information including at least one of a cause of the abnormality and a countermeasure for eliminating the abnormality, together with information indicating whether or not the weld bead formed in the welded portion of the steel plate is abnormal.

[0133] As an example, the anomaly information is generated by referring to a table stored in advance in the storage unit 30, which associates the determination result based on the anomaly determination model with the cause of the anomaly and / or a countermeasure method.

[0134] As an example, the storage unit 30 stores a table such as the one shown below. Judgment result 0: Normal 1: Abnormal, cause "Undercut", solution "Reduce the welding speed." 2: Abnormal, cause "Overlap", solution "Increase the welding speed." 2: Abnormal, cause "poor penetration", remedy "Increase the current value." By referring to such a table, abnormality information including information indicating whether the weld bead is abnormal or not, as well as at least one of the cause of the abnormality and a countermeasure for resolving the abnormality, is generated and output.

[0135] 10 is a diagram illustrating an example of anomaly information output by the output unit 23. In this example, the anomaly information is displayed on the display 201 of the output unit 23.

[0136] 10, an abnormality presence / absence display area 211 that displays the presence or absence of an abnormality on display 201 displays "abnormal welded portion," indicating that an abnormality exists in the welded portion. Furthermore, an abnormality cause display area 212 that displays the cause of the abnormality on display 201 displays "undercut: welding speed is too fast" as the cause of the abnormality. Furthermore, a countermeasure display area 213 that displays a countermeasure for eliminating the abnormality on display 201 displays "reduce the welding speed."

[0137] In addition, in abnormality cause display area 212, "welding speed is too fast" or "undercut" may be displayed. As described above with reference to Fig. 7, if the type of abnormality of the weld bead formed in the welded portion is identified, the cause of the abnormality can be identified. For this reason, the type of abnormality of the weld bead may be displayed as the cause of the abnormality.

[0138] That is, the information regarding whether or not there is an abnormality may include information indicating at least one of an undercut, an overlap, and a penetration defect.

[0139] Also, the display 201 may display the abnormality presence / absence display area 211 and the abnormality cause display area 212, but not the countermeasure method display area 213. Alternatively, the display 201 may display the abnormality presence / absence display area 211 and the countermeasure method display area 213, but not the abnormality cause display area 212.

[0140] This reduces the time required to analyze the cause of the abnormality and to consider countermeasures. As a result, the welding process can be completed quickly. In addition, abnormalities in the welded parts can be diagnosed without the need for non-destructive testing, which was previously required, making it possible to diagnose abnormalities in real time at low cost.

[0141] Exemplary embodiment 3 The third exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the second exemplary embodiment are given the same reference numerals, and the description thereof will be omitted as appropriate.

[0142] Output unit 23 may further output control information for causing welding machine 100 to execute a countermeasure based on the cause of the abnormality included in the abnormality information. The control information is supplied to a control unit of welding machine 100 via communication unit 41, for example.

[0143] For example, if the cause of the abnormality is "undercut," control information to reduce the welding speed is output.

[0144] Also, for example, if the cause of the abnormality is "overlap," control information to increase the welding speed is output.

[0145] Also, for example, if the cause of the abnormality is "poor penetration," control information is output to increase the current value output by the electrode.

[0146] In this manner, when welding condition diagnosis device 10 detects an abnormality in a weld bead, control information including a countermeasure for eliminating the abnormality can be automatically supplied to welding machine 100. For example, if a label attached to a feature similar to a feature of time-series data in a welding process being performed indicates an undercut, control information for slowing down the moving speed of electrode unit 101 can be generated and supplied to a control unit of welding machine 100.

[0147] As a result, when welding condition diagnosis device 10 detects an abnormality in a weld bead, a countermeasure for eliminating the detected abnormality can be reflected in the welding process being performed by welding machine 100. Therefore, the time required to deal with the abnormality can be further reduced.

[0148] Exemplary embodiment 4 A fourth exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the second exemplary embodiment are given the same reference numerals, and the description thereof will be omitted as appropriate.

[0149] Fig. 11 is a diagram showing another example of sensors incorporated in the electrode unit 101. In this example, in addition to the example described above with reference to Fig. 5, a sound sensor 115, a vibration sensor 116, and an image sensor 117 are provided. The other sensors are similar to those in the example described above with reference to Fig. 5.

[0150] The sound sensor 115 detects the sound of the welding process being performed and outputs a sensor value. The vibration sensor 116 detects vibrations caused by the welding process being performed and outputs a sensor value. The image sensor 117 captures an X-ray image of the welded part. The image sensor 117 outputs a sensor value corresponding to, for example, a pixel of the captured image.

[0151] It is not necessary to provide all of the sound sensor 115, the vibration sensor 116, and the image sensor 117, and at least one of these sensors may be provided.

[0152] That is, in this exemplary embodiment, at least one of a sound sensor 115 that detects the sound of the welding process being performed, a vibration sensor 116 that detects vibrations caused by the welding process being performed, and an image sensor 117 that captures an X-ray image of the welded portion is further included.

[0153] By including time-series data of the sensor values ​​output from such sensors in the feature quantities, it becomes possible to detect abnormalities in the weld beads more accurately.

[0154] Although an example in which each sensor unit is incorporated in electrode unit 101 has been described here, for example, some or all of each sensor unit may be incorporated in a control unit of welding machine 100. In short, it is sufficient that each sensor unit shown in FIG.

[0155] Exemplary embodiment 5 The fifth exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the second, third, or fourth exemplary embodiments are denoted by the same reference numerals, and the description thereof will be omitted as appropriate.

[0156] 12 is a block diagram showing an example of the configuration of a welding condition diagnosis and learning device 10A in this exemplary embodiment. The welding condition diagnosis and learning device 10A in this exemplary embodiment executes learning of an abnormality determination model 81 in addition to the welding condition diagnosis process.

[0157] The welding condition diagnosis and learning device 10A in this exemplary embodiment differs from the welding condition diagnosis device 10 described above with reference to Fig. 3 in that a learning unit 24 is provided in the abnormality diagnosis device 20. The configuration of the welding condition diagnosis and learning device 10A other than the learning unit 24 is similar to the configuration described above with reference to Fig. 3.

[0158] The learning unit 24 converts the time-series data obtained from the welding process during the second welding into features, and stores the features in the storage unit 30 with labels relating to abnormalities in the weld beads, thereby generating a feature DB.

[0159] For example, when welding machine 100 welds a steel plate, time-series data of sensor values ​​output from each sensor attached to welding machine 100 is acquired by acquisition unit 21. Acquisition unit 21 generates shifted time-series data, for example, as described with reference to FIG.

[0160] That is, the acquisition unit 21 acquires time-series data including at least one of the current value and the voltage value during the second welding.

[0161] The time series data output from the acquisition unit 21 is supplied to the abnormality determination unit 22. As described above, the abnormality determination unit 22 converts the shifted time series data into feature quantities using the abnormality determination model 81. The converted feature quantities are supplied to the learning unit 24.

[0162] For example, after welding of steel plates by welding machine 100 is completed, the operator of the welding machine, an inspector who inspects the weld, or the like inspects the weld for abnormalities in the weld bead formed in the welded portion of the steel plates. If an abnormality in the weld bead is found by the inspection, the operator, inspector, or the like inputs information indicating the abnormality in the weld bead and the type of abnormality (e.g., undercut, overlap, poor penetration, or the like) via input unit 42.

[0163] The welding process of welding steel plates by the welding machine 100 is performed multiple times, and the weld bead is inspected for abnormalities in each welding process that is performed.

[0164] Learning unit 24 assigns a label related to the weld bead abnormality to the feature corresponding to the welding process for which information indicating the weld bead abnormality and the type of abnormality has been input. For example, a label indicating that the weld bead formation is abnormal is assigned, and when the weld bead formation is abnormal, the type of abnormality is also represented by a label.

[0165] Furthermore, learning unit 24 assigns a label indicating that the weld bead is normally formed to the feature amount corresponding to the welding process for which no information indicating an abnormality in the weld bead and the type of abnormality was input.

[0166] In this manner, learning unit 24 generates a feature DB by storing and labeling the feature obtained from the welding process executed by welding machine 100. Note that data including the shifted time-series data, feature, and label obtained from the welding process during the second welding may also be referred to as learning data.

[0167] (Example of learning process by learning unit 24) An example of a learning process performed by the learning unit 24 using the feature DB will be described below.

[0168] First, the learning unit 24 randomly selects one piece of shifted time series data from the learning data. For convenience in the following description, the data selected here may be referred to as shifted time series data D1.

[0169] Next, the learning unit 24 selects other shifted time series data as verification data. The data selected here may be conveniently referred to as shifted time series data D2, D3, D4, etc. The learning unit 24 converts each piece of data into a feature (binary data) by using the abnormality determination model 81 of the abnormality determination unit 22.

[0170] Then, the learning unit 24 refers to the label attached to the shifted time series data D1 and the labels attached to each of the shifted time series data D2, D3, and D4, and updates the parameters of the anomaly determination model 81 so that the feature values ​​of the shifted time series data D2, D3, and D4 that are labeled with the same label as the shifted time series data D1 become closer to the feature values ​​of the shifted time series data D1.

[0171] Here, the similarity of the feature amount can be used as an index of learning by the learning unit 24. More specifically, the ranking of the feature amount may be used as an index of the similarity. When using the ranking, the ranking may be indexed by using, for example, a sigmoid function or the like.

[0172] In this way, the acquisition unit 21 acquires time series data including at least one of the current value or the voltage value during the second welding and learning data including a label, and the learning unit 24 trains the abnormality judgment model 81 using the learning data.

[0173] After a feature DB in which a sufficiently large number of feature values ​​are stored is generated, welding condition diagnosis and learning device 10A can execute welding diagnosis processing using abnormality determination model 81 learned as described above. That is, in the welding diagnosis processing, abnormality determination model 81 searches for a similar feature value from among the multiple feature values ​​stored in the feature DB.

[0174] Next, an example of the learning process executed by the welding condition diagnosis and learning device 10A of FIG. 12 will be described with reference to a flowchart of FIG.

[0175] In step S101, the pre-processing unit 61 of the time-series data acquisition unit 21 executes pre-processing. This process is similar to the process described with reference to Fig. 9, so a detailed description will be omitted. As a result, shifted time-series data is generated.

[0176] In step S102, the abnormality determination unit 22 converts the shifted time-series data generated in the process of step S101 into a feature quantity.

[0177] In step S103, learning unit 24 adds a label to the feature converted in the process of step S102. As described above, the label is added based on the information indicating the abnormality of the weld bead and the type of the abnormality input via input unit 42.

[0178] In step S104, the learning unit 24 stores in the storage unit 30 the feature amounts to which the labels have been added in the process of step S103.

[0179] The welding process is performed multiple times, and the database generation process is performed each time the welding process is performed. As a result, labels are added to the multiple feature amounts and stored, and a feature amount DB is generated.

[0180] In step S105, the learning unit 24 executes machine learning. At this time, as described above, the learning unit 24 updates the parameters of the anomaly determination model 81. That is, the learning unit 24 updates the parameters of the anomaly determination model 81 so that the feature quantities of the post-shift time series data D2, D3, and D4 to which the same label as that of the post-shift time series data D1 is assigned become closer to the feature quantities of the post-shift time series data D1.

[0181] In this manner, the learning process is carried out.

[0182] In this way, according to the welding condition diagnosis and learning device 10A, the abnormality determination model 81 can be suitably trained.

[0183] [Software implementation example] Some or all of the functions of abnormality diagnosis device 20, welding condition diagnosis device 10, and welding condition diagnosis and learning device 10A may be realized by hardware such as an integrated circuit (IC chip), or by software.

[0184] In the latter case, the abnormality diagnosis device 20, the welding condition diagnosis device 10, and the welding condition diagnosis and learning device 10A are realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter, referred to as computer C) is shown in FIG. 14.

[0185] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for operating the computer C as the abnormality diagnosis device 20, the welding condition diagnosis device 10, or the welding condition diagnosis and learning device 10A. In the computer C, the processor C1 reads the program P from the memory C2 and executes it to realize each function of the abnormality diagnosis device 20, the welding condition diagnosis device 10, or the welding condition diagnosis and learning device 10A.

[0186] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a microcontroller, or a combination of these. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination of these.

[0187] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may further include a communication interface for transmitting and receiving data to and from other devices. The computer C may further include an input / output interface for connecting input / output devices such as a keyboard, a mouse, a display, and a printer.

[0188] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can obtain the program P via such a recording medium M. Furthermore, the program P can be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also obtain the program P via such a transmission medium.

[0189] [Additional Note 1] The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technical means disclosed in the above-described embodiment are also included in the technical scope of the present invention.

[0190] [Additional Note 2] Some or all of the above-described embodiments can be described as follows. However, the present invention is not limited to the following described aspects.

[0191] (Appendix 1) An acquisition means for acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination means for determining whether or not an abnormality exists in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition means; an output means for outputting, when the abnormality determination means determines that an abnormality exists, a result of the determination, and at least one of a cause of the abnormality and a method of dealing with the abnormality; An abnormality diagnosis device comprising:

[0192] (Appendix 2) The abnormality determination model used by the abnormality determination means is Convert time series data into features, Searching for a feature quantity similar to the feature quantity obtained by converting the time-series data from among a plurality of feature quantities stored in advance; By referring to the labels attached to the searched features, information on whether or not there is an abnormality in the welded portion during the first welding is generated. 2. An abnormality diagnosis device as described in appendix 1.

[0193] (Appendix 3) The information regarding whether or not the abnormality exists includes: Undercut, where the end of the weld bead is recessed an overlap in which the end of the weld bead is formed outside the welded portion; Poor penetration where the weld bead is not formed at the bottom of the weld Contains information indicating at least one of the following: 3. An abnormality diagnosis device as described in appendix 2.

[0194] (Appendix 4) The acquisition means includes: acquiring time-series data including at least one of a current value and a voltage value during the second welding and learning data including a label; The abnormality diagnosis device includes: A learning means for learning the abnormality determination model using the learning data is provided. 4. The abnormality diagnostic device according to claim 1, wherein the abnormality diagnostic device is a

[0195] (Appendix 5) The output means further outputs control information corresponding to a countermeasure for eliminating the abnormality in the welded portion during the first welding. 5. The abnormality diagnostic device according to claim 1, wherein:

[0196] (Appendix 6) The time series data during the first welding includes a plurality of time series data including a first time series data and a second time series data acquired corresponding to each of a plurality of electrodes included in the welding machine. (Appendix 7) The acquisition means includes: generating shifted time series data for the first welding time by relatively shifting the first time series data and the second time series data in the time series data for the first welding time in a time direction; The abnormality determination means Based on the shifted time series data, it is determined whether or not there is an abnormality in the welded portion during the first welding. 7. An abnormality diagnosis device according to claim 6.

[0197] (Appendix 8) The acquisition means includes: Further acquiring speed information indicating a speed during the first welding; The first time series data and the second time series data are relatively shifted in the time direction according to the speed indicated by the speed information. 8. An abnormality diagnosis device according to claim 7.

[0198] (Appendix 9) Obtaining time series data including at least one of a current value and a voltage value during the first welding; determining whether or not there is an abnormality in the welded portion during the first welding, based on the acquired time-series data, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion; and when it is determined that there is an abnormality, outputting a result of the determination and at least one of a cause of the abnormality and a method of dealing with the abnormality. 13. An abnormality diagnosis method comprising:

[0199] (Appendix 10) Computer, An acquisition means for acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination means for determining whether or not an abnormality exists in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition means; and an output means for outputting, when the abnormality determination means determines that there is an abnormality, a result of the determination, and at least one of a cause of the abnormality and a method of dealing with the abnormality. A program characterized by:

[0200] [Additional Note 3] A part or all of the above-described embodiments can be further expressed as follows.

[0201] At least one processor, the processor comprising: An acquisition process of acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination process for determining whether or not there is an abnormality in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition process; When it is determined that there is an abnormality by the abnormality determination process, an output process is executed to output the result of the determination, and at least one of a cause of the abnormality and a countermeasure method for eliminating the abnormality. An abnormality diagnosis device comprising:

[0202] The abnormality diagnosis device may further include a memory, and the memory may store a program for causing the processor to execute the time-series value acquisition process and the abnormality method output process. The program may be recorded in a computer-readable, non-transitory, tangible recording medium. [Explanation of symbols]

[0203] 10 Welding condition diagnosis device 20 Abnormality diagnosis device 21 Acquisition Department 22 Abnormality determination section 23 Output section 30 Storage section 41 Communications Department 42 Input section 61 Pretreatment section 81 Anomaly Judgment Model

Claims

1. an acquisition means for acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination means for determining whether or not an abnormality exists in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition means; an output means for outputting, when the abnormality determination means determines that an abnormality exists, a result of the determination, and at least one of a cause of the abnormality and a method of dealing with the abnormality; Equipped with the time series data during the first welding includes a plurality of time series data including first time series data and second time series data acquired corresponding to each of a plurality of electrodes included in a welding machine; The acquisition means includes: generating shifted time series data for the first welding time by relatively shifting the first time series data and the second time series data in the time series data for the first welding time in a time direction; The abnormality determination means Based on the shifted time series data, it is determined whether or not there is an abnormality in the welded portion during the first welding. Anomaly diagnosis device.

2. The abnormality determination model used by the abnormality determination means is Convert time series data into features, Searching for a feature quantity similar to the feature quantity obtained by converting the time-series data from among a plurality of feature quantities stored in advance; By referring to the label attached to the retrieved feature amount, information on whether or not there is an abnormality in the welded portion during the first welding is generated. The abnormality diagnosis device according to claim 1 .

3. The information regarding whether or not the abnormality exists includes: Undercut, where the end of the weld bead is recessed an overlap in which the end of the weld bead is formed outside the welded portion; Poor penetration where the weld bead is not formed at the bottom of the weld Contains information indicating at least one of the following: The abnormality diagnosis device according to claim 2 .

4. The acquisition means includes: acquiring time-series data including at least one of a current value and a voltage value during the second welding and learning data including a label; The abnormality diagnosis device includes: A learning means for learning the abnormality determination model using the learning data is provided. The abnormality diagnosis device according to any one of claims 1 to 3.

5. The output means further outputs control information corresponding to a countermeasure for eliminating the abnormality in the welded portion during the first welding.

5. The abnormality diagnosis device according to claim 1,

6. The acquisition means includes: further acquiring speed information indicating a speed during the first welding; The first time series data and the second time series data are relatively shifted in a time direction according to the speed indicated by the speed information. The abnormality diagnosis device according to claim 1 .

7. acquiring time series data including at least one of a current value and a voltage value during the first welding; determining whether or not there is an abnormality in the welded portion during the first welding, based on the acquired time-series data, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion; When it is determined that there is an abnormality, outputting a result of the determination and at least one of a cause of the abnormality and a method of dealing with the abnormality, the time series data during the first welding includes a plurality of time series data including first time series data and second time series data acquired corresponding to each of a plurality of electrodes included in a welding machine; In the obtaining, generating shifted time series data for the first welding time by relatively shifting the first time series data and the second time series data in the time series data for the first welding time in a time direction; In the determining, Based on the shifted time series data, it is determined whether or not there is an abnormality in the welded portion during the first welding.

13. An abnormality diagnosis method comprising:

8. Computer, an acquisition means for acquiring time series data including at least one of a current value and a voltage value during the first welding; an abnormality determination means for determining whether or not an abnormality exists in the welded portion during the first welding, using an abnormality determination model that has learned a relationship between time-series data including at least one of a current value and a voltage value during the second welding and the presence or absence of an abnormality in the welded portion, based on the time-series data acquired by the acquisition means; and an output means for outputting, when the abnormality determination means determines that there is an abnormality, a result of the determination, and at least one of a cause of the abnormality and a method of dealing with the abnormality, the time series data during the first welding includes a plurality of time series data including first time series data and second time series data acquired corresponding to each of a plurality of electrodes included in a welding machine; The acquisition means includes: generating shifted time series data for the first welding time by relatively shifting the first time series data and the second time series data in the time series data for the first welding time in a time direction; The abnormality determination means Based on the shifted time series data, it is determined whether or not there is an abnormality in the welded portion during the first welding. A program characterized by:

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