Method and system for calculating residual service life of industrial robot under multiple working conditions

Abstract

The invention relates to the technical field of industrial robots, in particular to a method and system for calculating the remaining service life of an industrial robot under multiple working conditions. According to the technical scheme, deviation values between multi-mode full-life-cycle data, including feedback current, feedback speed and tracking errors, of the industrial robot and reference data are calculated, and the deviation values are fitted through Weibull distribution; and a maximum likelihood estimation method is adopted to calculate shape parameters and scale parameters of the Weibull distribution, and finally the remaining service life of the industrial robot is calculated according to a cumulative failure probability formula of the Weibull distribution. The method has the advantages that deviation calculation and Weibull distribution fitting are carried out on the full-life-cycle data of the industrial robot under multiple modes, and the accuracy of calculation of the remaining service life of the industrial robot under multiple working conditions can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and in particular to a method and system for calculating the remaining service life of an industrial robot under multiple working conditions. Background Art

[0002] Each step in time series data contains rich historical information. Prediction models generally fail to fully consider the correlation between different time steps in the time series, resulting in the loss of useful historical information, which in turn affects long-term prediction capabilities.

[0003] In the prediction method based on the health index (HI) curve, when the HI value exceeds the predetermined failure threshold, it is considered a failure, and the remaining useful life (RUL) is calculated. However, the determination of the failure threshold is a very difficult problem. It not only requires the participation of a large amount of expert experience, but also has no universality. The failure threshold may be different under different equipment and working conditions, which limits the accuracy and applicability of this type of model in the actual calculation of the remaining useful life of industrial robots. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for calculating the remaining service life of an industrial robot under multiple working conditions, so as to solve the problems of low accuracy and limited applicability in predicting and calculating the remaining service life of an industrial robot under different equipment and working conditions.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for calculating the remaining service life of an industrial robot under multiple working conditions, comprising the following steps: S01. Collect the feedback current, feedback speed and tracking error of the industrial robot within a certain operating time period to obtain a feedback current data set, a feedback speed data set and a tracking error data set, and calculate the deviation values ​​of the feedback current data set, the feedback speed data set and the tracking error data set with the benchmark current data set, speed data set and tracking error data set respectively to obtain a feedback current deviation data set, a feedback speed deviation data set and a tracking error deviation data set.

[0006] S02, fitting the feedback current deviation data set, the feedback speed deviation data set and the tracking error deviation data set obtained in step S01 using Weibull distribution, and calculating the shape parameter and scale parameter of the Weibull distribution using maximum likelihood estimation method.

[0007] S03. According to the shape parameters and scale parameters of the Weibull distribution calculated in step S02, the cumulative failure probability formula of the Weibull distribution is obtained, and the cumulative working time of the industrial robot when the set failure probability threshold is reached is calculated using the cumulative failure probability formula, and the existing cumulative working time of the industrial robot is subtracted to obtain the remaining service life of the industrial robot.

[0008] Specifically, in step S02, the function expression of Weibull distribution is: ,in, For time, is the shape parameter, is the scale parameter.

[0009] Specifically, in step S03, the cumulative failure probability formula of the Weibull distribution is: , where T is the cumulative working time.

[0010] A system for calculating the remaining service life of an industrial robot under multiple working conditions includes a reference database, a data acquisition module, a deviation value calculation module, a Weibull distribution fitting calculation module, and a remaining service life calculation output module. Among them: The reference database is used to store the reference current data set, speed data set and tracking error data set in step S01 of the above method.

[0011] The data acquisition module is used to execute step S01 of the above method to collect feedback current, feedback speed and tracking error of the industrial robot within a certain operating time period to obtain a feedback current data set, a feedback speed data set and a tracking error data set.

[0012] The deviation value calculation module is used to perform the deviation value calculation in step S01 of the above method.

[0013] The Weibull distribution fitting calculation module is used to execute step S02 of the above method.

[0014] The remaining service life calculation output module is used to set the failure probability threshold and execute step S03 of the above method, and output the remaining service life of the industrial robot.

[0015] The beneficial effects of the present invention are as follows: the deviation values ​​between the multimodal full life cycle data of the industrial robot including feedback current, feedback speed and tracking error and the benchmark data are calculated respectively, the deviation values ​​are fitted using Weibull distribution, and the shape parameters and scale parameters of the Weibull distribution are calculated using the maximum likelihood estimation method, and finally the remaining service life of the industrial robot is calculated according to the cumulative failure probability formula of the Weibull distribution, thereby effectively improving the accuracy of the calculation of the remaining service life of the industrial robot under multiple working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Attached Figure 1 1 is a system connection schematic diagram of a system for calculating the remaining service life of an industrial robot under multiple working conditions in an embodiment. DETAILED DESCRIPTION

[0017] Embodiment 1, a method for calculating the remaining service life of an industrial robot under multiple working conditions, comprises the following steps: S01. Collect the feedback current, feedback speed and tracking error of the industrial robot within a certain operating time period to obtain a feedback current data set, a feedback speed data set and a tracking error data set, and calculate the deviation values ​​of the feedback current data set, the feedback speed data set and the tracking error data set with the benchmark current data set, speed data set and tracking error data set respectively to obtain a feedback current deviation data set, a feedback speed deviation data set and a tracking error deviation data set.

[0018] S02, fitting the feedback current deviation data set, the feedback speed deviation data set and the tracking error deviation data set obtained in step S01 using Weibull distribution, and calculating the shape parameter and scale parameter of the Weibull distribution using maximum likelihood estimation method.

[0019] S03. According to the shape parameters and scale parameters of the Weibull distribution calculated in step S02, the cumulative failure probability formula of the Weibull distribution is obtained, and the cumulative working time of the industrial robot when the set failure probability threshold is reached is calculated using the cumulative failure probability formula, and the existing cumulative working time of the industrial robot is subtracted to obtain the remaining service life of the industrial robot.

[0020] Specifically, in step S02, the function expression of Weibull distribution is: ,in, For time, is the shape parameter, is the scale parameter.

[0021] Specifically, in step S03, the cumulative failure probability formula of the Weibull distribution is: , where T is the cumulative working time.

[0022] At the same time, in view of the above method, this embodiment also provides a calculation system for the remaining service life of an industrial robot under multiple working conditions, referring to Figure 1 , which includes a benchmark database, a data acquisition module, a deviation value calculation module, a Weibull distribution fitting calculation module and a remaining service life calculation output module. Among them: The reference database is used to store the reference current data set, speed data set and tracking error data set in step S01 of the above method.

[0023] The data acquisition module is used to execute step S01 of the above method to collect feedback current, feedback speed and tracking error of the industrial robot within a certain operating time period to obtain a feedback current data set, a feedback speed data set and a tracking error data set.

[0024] The deviation value calculation module is used to perform the deviation value calculation in step S01 of the above method.

[0025] The Weibull distribution fitting calculation module is used to execute step S02 of the above method.

[0026] The remaining service life calculation output module is used to set the failure probability threshold and execute step S03 of the above method, and output the remaining service life of the industrial robot.

[0027] Of course, the above are only preferred embodiments of the present invention, and are not intended to limit the scope of use of the present invention. Therefore, any equivalent changes made to the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the remaining service life of an industrial robot under multiple working conditions, characterized in that: The steps include: S01, collecting feedback current, feedback speed and tracking error of the industrial robot in a certain operation period, obtaining feedback current data set, feedback speed data set and tracking error data set, and respectively calculating deviation values ​​of the feedback current data set, feedback speed data set and tracking error data set with the reference current data set, speed data set and tracking error data set, to obtain feedback current deviation data set, feedback speed deviation data set and tracking error deviation data set; S02, fitting the feedback current deviation data set, the feedback speed deviation data set and the tracking error deviation data set obtained in step S01 using Weibull distribution, and calculating the shape parameter and scale parameter of the Weibull distribution using maximum likelihood estimation method; S03. According to the shape parameters and scale parameters of the Weibull distribution calculated in step S02, the cumulative failure probability formula of the Weibull distribution is obtained, and the cumulative working time of the industrial robot when the set failure probability threshold is reached is calculated using the cumulative failure probability formula, and the existing cumulative working time of the industrial robot is subtracted to obtain the remaining service life of the industrial robot.

2. The method for calculating the remaining service life of an industrial robot under multiple working conditions according to claim 1, characterized in that: In step S02, the function expression of Weibull distribution is: ,in, For time, is the shape parameter, is the scale parameter.

3. The method for calculating the remaining service life of an industrial robot under multiple working conditions according to claim 2, characterized in that: In step S03, the cumulative failure probability formula of the Weibull distribution is: , where T is the cumulative working time.

4. A system for calculating the remaining service life of an industrial robot under multiple working conditions, characterized in that: It includes a benchmark database, a data acquisition module, a deviation value calculation module, a Weibull distribution fitting calculation module and a remaining service life calculation output module; The reference database is used to store the reference current data set, speed data set and tracking error data set in step S01 of the method according to any one of claims 1 to 3; The data acquisition module is used to perform step S01 of the method according to any one of claims 1 to 3 to collect feedback current, feedback speed and tracking error of the industrial robot within a certain operating time period to obtain a feedback current data set, a feedback speed data set and a tracking error data set; The deviation value calculation module is used to perform the deviation value calculation in step S01 of the method according to any one of claims 1 to 3; The Weibull distribution fitting calculation module is used to perform step S02 of the method according to any one of claims 1 to 3; The remaining service life calculation output module is used to set a failure probability threshold and execute step S03 of the method according to any one of claims 1 to 3, and output the remaining service life of the industrial robot.

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