阅读说明：本技术 一种伺服焊接系统运动的波形识别方法及设备 (Waveform identification method and equipment for servo welding system motion ) 是由 郭云 徐骏 黄毅 何琪 于 2019-11-18 设计创作，主要内容包括：本申请的目的是提供一种用于伺服焊接系统的波形识别方法及设备,本申请通过根据伺服焊接系统的运动控制和程序启动的信息确定基准程序；根据所述基准程序建立所述伺服焊接系统的波形识别模型；利用所述波形识别模型识别所述基准程序运行时的所述伺服焊接系统的运动控制的当前波形信息；根据所述当前波形信息判断所述伺服焊接系统是否存在驱动异常。从而实现与工业逻辑一致性,精准通过位置的偏离识别反应伺服焊接系统的异常状态,且需要的输入参数比较少,可以节约通信资源。(The application aims to provide a waveform identification method and equipment for a servo welding system, wherein a benchmark program is determined according to motion control and program starting information of the servo welding system; establishing a waveform identification model of the servo welding system according to the benchmark program; identifying current waveform information of motion control of the servo welding system while the benchmark program is running using the waveform identification model; and judging whether the servo welding system has abnormal driving according to the current waveform information. Therefore, consistency with industrial logic is achieved, the abnormal state of the servo welding system is accurately identified and reflected through position deviation, required input parameters are few, and communication resources can be saved.)

1. A method of waveform identification for a servo welding system, the method comprising:

determining a benchmark program according to motion control and program starting information of the servo welding system;

establishing a waveform identification model of the servo welding system according to the benchmark program;

identifying current waveform information of motion control of the servo welding system while the benchmark program is running using the waveform identification model;

and judging whether the servo welding system has abnormal driving according to the current waveform information.

2. The method of claim 1, wherein the benchmark program includes position change information and flag bits for motion control of the servo welding system.

3. The method of claim 2, wherein establishing a waveform recognition model of the servo welding system based on the benchmark program comprises:

acquiring position change information of motion control of the servo welding system when the benchmark program runs;

calculating beat time according to the flag bit of the reference program;

screening the position change information by using quantiles and the beat time to obtain a screened training set;

processing the absolute time in the screened training set into relative time to obtain a target training set;

and establishing a waveform recognition model of the servo welding system according to the target training set.

4. The method of claim 3, wherein calculating the beat time based on the flag bits of the benchmark program comprises:

acquiring the starting time and the ending time of the benchmark program according to the flag bit of the benchmark program;

and calculating the beat time according to the starting time and the ending time.

5. The method of claim 3, wherein the filtering the location change information using quantiles and the beat time comprises:

and screening the outlier beat time in the beat time by using the quantile, and determining the position change information corresponding to the beat time without the outlier beat time.

6. The method of claim 3, wherein identifying current waveform information for motion control of the servo welding system while the benchmark program is running using the waveform identification model comprises:

acquiring position change information of motion control of the servo welding system when the benchmark program runs;

determining a target control variable of the motion control according to the position change information;

clustering the target control variables according to the relative time by using the waveform identification model to determine the current waveform;

boundary information of a waveform is extracted from the current waveform.

7. The method of claim 6, wherein determining whether a drive anomaly exists for the servo welding system based on the current waveform information comprises:

and judging whether the servo welding system has abnormal driving according to the extracted boundary information of the waveform, wherein the boundary information of the waveform comprises quantiles, maximum values, minimum values and mean values.

8. The method of claim 6, wherein the target control variable comprises an axial arc of a motor of a servo welding system.

9. The method of claim 3, wherein after building a waveform recognition model of the servo welding system from the target training set, comprising:

and verifying the waveform recognition model according to a preset cross verification mode, wherein the preset cross verification mode comprises verifying by using a plurality of groups of training sets in different time periods and samples formed by the obtained test sets according to a preset ratio.

10. An apparatus for waveform identification for a servo welding system, wherein the apparatus comprises:

one or more processors; and

a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the method of any of claims 1 to 9.

11. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 9.

Technical Field

The present disclosure relates to the field of data analysis of mechanical devices, and more particularly, to a waveform identification method and apparatus for a servo welding system.

Background

Resistance spot welding is widely used in manufacturing processes as a joining process. The resistance spot welding process usually needs a servo welding system to realize, and a servo driving system is a core component of the servo welding system, so that the stability and reliability of the process are greatly influenced. Therefore, the abnormity diagnosis method for the servo drive system is developed, the welding quality defect of the product can be effectively avoided, the unplanned shutdown is reduced, the cost is reduced, and the economic benefit is improved.

Some existing technologies for adding additional sensors, such as vibration and temperature sensors, can realize an abnormality diagnosis function to a certain extent, but new problems and hidden dangers possibly brought by the cost for installing the sensors, space limitation on the site and later reconstruction result in adding new hardware. In actual production of a factory, it is difficult to manually mark the data as normal or abnormal, and generally, data records with abnormal states are less, so that unsupervised learning is required to obtain a model. In addition, communication resources of controllers such as frequency converters and PLCs are generally limited, and channels should be occupied as little as possible.

Disclosure of Invention

An object of the present application is to provide a waveform identification method and apparatus for a servo welding system, which solve the problems of the prior art that additional sensor deployment and modification costs are high and communication resources are occupied when data identification is performed.

According to one aspect of the present application, there is provided a waveform identification method for a servo welding system, the method comprising:

determining a benchmark program according to motion control and program starting information of the servo welding system;

establishing a waveform identification model of the servo welding system according to the benchmark program;

identifying current waveform information of motion control of the servo welding system while the benchmark program is running using the waveform identification model;

and judging whether the servo welding system has abnormal driving according to the current waveform information.

Further, the benchmark program includes position change information of the motion control of the servo welding system and a flag bit.

Further, establishing a waveform identification model of the servo welding system according to the benchmark program includes:

acquiring position change information of motion control of the servo welding system when the benchmark program runs;

calculating beat time according to the flag bit of the reference program;

screening the position change information by using quantiles and the beat time to obtain a screened training set;

processing the absolute time in the screened training set into relative time to obtain a target training set;

and establishing a waveform recognition model of the servo welding system according to the target training set.

Further, calculating the beat time according to the flag bit of the benchmark program, including:

acquiring the starting time and the ending time of the benchmark program according to the flag bit of the benchmark program;

and calculating the beat time according to the starting time and the ending time.

Further, the step of screening the position change information by using quantiles and the beat time comprises the following steps:

and screening the outlier beat time in the beat time by using the quantile, and determining the position change information corresponding to the beat time without the outlier beat time.

Further, identifying current waveform information for motion control of the servo welding system while the benchmark program is running using the waveform identification model, including:

acquiring position change information of motion control of the servo welding system when the benchmark program runs;

determining a target control variable of the motion control according to the position change information;

clustering the target control variables according to the relative time by using the waveform identification model to determine the current waveform;

boundary information of a waveform is extracted from the current waveform.

Further, judging whether the servo welding system has abnormal driving according to the current waveform information comprises:

and judging whether the servo welding system has abnormal driving according to the extracted boundary information of the waveform, wherein the boundary information of the waveform comprises quantiles, maximum values, minimum values and mean values.

Further, the target control variable includes an axis arc of a motor of the servo welding system.

Further, after the waveform recognition model of the servo welding system is established according to the target training set, the method includes:

and verifying the waveform recognition model according to a preset cross verification mode, wherein the preset cross verification mode comprises verifying by using a plurality of groups of training sets in different time periods and samples formed by the obtained test sets according to a preset ratio.

In accordance with another aspect of the present application, there is also provided an apparatus for waveform identification for a servo welding system, the apparatus comprising:

one or more processors; and a memory storing computer readable instructions that, when executed, cause the processor to perform operations of the method as previously described.

According to yet another aspect of the present application, there is also provided a computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to implement the method as described above.

Compared with the prior art, the method and the device determine the benchmark program according to the motion control and program starting information of the servo welding system; establishing a waveform identification model of the servo welding system according to the benchmark program; identifying current waveform information of motion control of the servo welding system while the benchmark program is running using the waveform identification model; and judging whether the servo welding system has abnormal driving according to the current waveform information. Therefore, consistency with industrial logic is achieved, the abnormal state of the servo welding system is accurately identified and reflected through position deviation, required input parameters are few, and communication resources can be saved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 illustrates a flow diagram of a waveform identification method for a servo welding system provided in accordance with an aspect of the present application;

FIG. 2 is a schematic diagram illustrating position data obtained by a fiducial procedure in an embodiment of the present application;

FIG. 3 is a schematic diagram of a flag indicating time information according to an embodiment of the present application;

FIG. 4 illustrates a waveform of an axial arc in an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating data usage during cross-checking in an embodiment of the present application;

FIG. 6 is a diagram illustrating normal waveforms in an embodiment of the present application;

FIG. 7 is a diagram illustrating the cross-check result in one embodiment of the present application;

fig. 8(1) - (4) are schematic diagrams respectively showing the performances of the embodiment of the present application when the number of cycles of the shaft arc is 1, 12, 14 and 16.

The same or similar reference numbers in the drawings identify the same or similar elements.

Detailed Description

The present application is described in further detail below with reference to the attached figures.

In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (e.g., Central Processing Units (CPUs)), input/output interfaces, network interfaces, and memory.

The Memory may include volatile Memory in a computer readable medium, Random Access Memory (RAM), and/or nonvolatile Memory such as Read Only Memory (ROM) or flash Memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, Phase-Change RAM (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other Memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassette tape, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transmyedia), such as modulated data signals and carrier waves.

FIG. 1 illustrates a flow diagram of a waveform identification method for a servo welding system provided in accordance with an aspect of the present application, the method comprising: step S11 to step S14,

in step S11, a reference program is determined based on the motion control of the servo welding system and the information of program start; the servo welding system comprises equipment which needs to be driven by a motor to operate, the motion control of the servo welding system is the motion control condition of a motor driving unit of the servo welding system, a benchmark program is implanted into a control program of the servo welding system, the benchmark program is a section of standard program, and the servo welding system completes the action of a standard working condition according to the standard program, so that the response of relevant parameters of the servo welding system after the standard program is completed is obtained.

In step S12, a waveform identification model of the servo welding system is established according to the benchmark program; and then, the response of the relevant parameters of the servo welding system is obtained according to the operation benchmark program, and the response data is used as a data base and a training set to establish a waveform recognition model of the servo welding system so as to facilitate the subsequent detection of whether the driving system of the servo welding system works normally.

Identifying current waveform information for motion control of the servo welding system while the benchmark program is running using the waveform identification model in step S13; the established waveform identification model is used for identifying the current motion control information of the servo welding system and feeding back the motion control information through waveform information, and the data information on the current waveform represents the running state information of the current driving system of the servo welding system. Subsequently, in step S14, it is determined whether there is a driving abnormality in the servo welding system based on the current waveform information. Here, the current waveform information is analyzed, for example, upper and lower boundary values of the amplitude of the current waveform, and whether the servo welding system has driving abnormality is determined according to the analysis result, and if some deviation values exist, deviation from a normal state caused by intrusion of foreign matters and poor lubrication may occur.

According to the method, a section of standard program is implanted into a control program, equipment completes the work of a standard working condition according to the standard program, the consistency with industrial logic is achieved, a consistency result is obtained, when the standard program is completed through comparison, the response of other parameters of the equipment identifies the response waveform of the equipment in a normal state, the boundary of the response waveform deviating from the normal state is received, and therefore subsequent abnormal warning can be conducted. In addition, additional sensor deployment is not needed, and the transformation cost is low.

In an embodiment of the present application, the benchmark program includes position change information and flag bits for motion control of the servo welding system. The method comprises the steps of establishing a benchmark program according to equipment characteristics and scenes, wherein the benchmark program comprises information about motion control of the equipment and whether the program is started or not, providing a same working condition for a servo welding system, constructing a same comparison benchmark, placing the benchmark program into a controller of the servo welding system, and running the benchmark program when the servo welding system is idle. As shown in fig. 2, the benchmark program at least comprises a set of motion control of the equipment, i.e. position change (line a), and a flag, i.e. whether the benchmark program is running (line B). By using the benchmark program, the required input parameters are less, only one flag bit and one control variable are required, and the communication resources can be saved.

In an embodiment of the present application, in step S12, position change information of the motion control of the servo welding system when the benchmark program is running is acquired; calculating beat time according to the flag bit of the reference program; screening the position change information by using quantiles and the beat time to obtain a screened training set; processing the absolute time in the screened training set into relative time to obtain a target training set; and establishing a waveform recognition model of the servo welding system according to the target training set. The method includes collecting relevant data of motion control of the servo welding system when a benchmark program runs, specifically position data (such as a line A in fig. 2) of a motor driving unit of the servo welding system, calculating beat time through a flag bit of the benchmark program, marking an outlier beat by using a quantile, extracting data marked as 'normal' as a screened training set, processing absolute time in the screened training set into relative time, using the processed training set as a target training set, and establishing a waveform recognition model according to the target training set, wherein the waveform recognition model is a waveform recognition model based on an outlier filtering method of the quantile.

In the above embodiment, when the position change information is screened by using the quantile and the beat time, the outlier beat time in the beat time may be screened by using the quantile, and the position change information corresponding to the beat time from which the outlier beat time is removed is determined. For example, the beat time in the sample is removed from the training sample as an outlier, the quantile below 5% and the quantile above 95% are removed from the training sample, and the non-outlier data is labeled as "normal" data, where the training sample is position change information, and when the beat time is outlier according to the beat time, the position change information corresponding to the non-outlier beat time is obtained and used as the training set after the screening. The quantile is a point in the continuous distribution function that corresponds to a probability p, e.g., a 5% quantile indicates that 5% of the data in the sample is less than the quantile. By using the calculation of the outlier filtering based on the quantile, the calculation amount is smaller and the calculation resources are saved compared with the method of smoothing processing.

In an embodiment of the present application, when calculating the beat time according to the flag bit of the reference program, the start time and the end time of the reference program may be obtained according to the flag bit of the reference program; and calculating the beat time according to the starting time and the ending time. Here, as shown in fig. 3, the beat time is the time taken for each completion of the reference program, the beat time is the beat end time-beat start time, and the relative time is the absolute time-beat start time, and the absolute time in the selected training set is processed into the relative time, so that the obtained position data of the exercise control is compared and aggregated in different cycles, thereby obtaining the waveform information.

In an embodiment of the present application, in step S13, position change information of the motion control of the servo welding system when the benchmark program is running is acquired; determining a target control variable of the motion control according to the position change information; clustering the target control variables according to the relative time by using the waveform identification model to determine the current waveform; boundary information of a waveform is extracted from the current waveform. Wherein the target control variable comprises an axis arc of a motor of the servo welding system. Here, when determining whether the motor driving unit of the current servo welding system is abnormal, the current position change information is collected according to a reference program, a target control variable is determined according to the position change information, for example, in the present application, an axis radian is determined according to the position change information, the axis radian is used as the target control variable, the target control variable is clustered according to relative time by using an established waveform recognition model to form waveform information, upper and lower boundary information of the waveform information is extracted, wherein the waveform information is information of an axis amplitude, and as shown in fig. 4, the waveform information is obtained by clustering the axis radian according to relative time. The process of establishing the waveform identification model is the process of utilizing quantiles to screen the position data of the servo welding system when the mobile phone benchmark program runs, processing absolute time in the screened training set into relative time, and clustering the target control variable according to the relative time to form waveform information. By the waveform information identification method, consistency with industrial logic can be achieved, and position accuracy reflects control accuracy, so that abnormal states of equipment can be reflected through position deviation.

Specifically, whether the servo welding system has driving abnormity is judged according to the extracted boundary information of the waveform, wherein the boundary information of the waveform comprises quantiles, maximum values, minimum values and mean values. Here, the boundary information of the waveform includes, but is not limited to, a quantile, a maximum value, a minimum value, and a mean value, and it is determined whether the waveform of the response of the servo welding system deviates from the boundary of the normal state by the boundary information, and an abnormality warning is triggered. And judging whether the current waveform is a normal waveform or not according to the boundary information.

In an embodiment of the application, after the waveform recognition model of the servo welding system is established according to the target training set, the waveform recognition model may be verified according to a preset cross-checking manner, where the preset cross-checking manner includes checking with a plurality of sets of samples composed of training sets at different time periods and obtained test sets according to a preset ratio. Here, the accuracy of the waveform recognition model is verified through cross-checking, a training set and a test set corresponding to different time periods are adopted, for example, the training set obtained by using data acquired on day XX and the test set obtained by using data acquired on day XX (1) are tested according to the proportion of the training set and the test set, as shown in fig. 5, multiple sets of cross data are used for verification, wherein TP is a true positive example, FP is a false positive example, TN is a true negative example, and FN is a false negative example. For example, the training set data used are data of 10 days and 11 days, the test set data are data of 9 days, the normal waveform is shown in fig. 6, a1 represents the upper limit of the axis radian, a2 represents the lower limit of the axis radian, A3 represents the mean value of the axis radian, and the results of the cross-over test are shown in fig. 7, wherein the results of the test set, the prediction of "abnormal" and the prediction of "normal" are shown in table 1:

test set	Prediction of- -Exception	prediction-Normal
			Actual- -exception	TP＝4	FN＝0
Actual- -Normal	FP＝0	TP＝1034

TABLE 1

This process is exemplified by the number of cycles of axial arc (Cycle) being 1, 12 and 16, as shown in fig. 8(1) - (4) for Cycle 1, 12, 14 and 16, respectively, and the M line indicates the performance of the axial arc Cycle. Thereby obtaining the cross-checking result, and using the waveform identification model or reconstructing the waveform identification model according to the checking result.

Furthermore, the present application also provides a computer readable medium, on which computer readable instructions are stored, the computer readable instructions being executable by a processor to implement the aforementioned waveform identification method for a servo welding system.

In an embodiment of the present application, there is also provided an apparatus for waveform identification for a servo welding system, the apparatus comprising:

one or more processors; and

a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the method as previously described.

For example, the computer readable instructions, when executed, cause the one or more processors to:

determining a benchmark program according to motion control and program starting information of the servo welding system;

establishing a waveform identification model of the servo welding system according to the benchmark program;

identifying current waveform information of motion control of the servo welding system while the benchmark program is running using the waveform identification model;

and judging whether the servo welding system has abnormal driving according to the current waveform information.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.

In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The terms first, second, etc. are used to denote names, but not any particular order.