Industrial robot fault detection method

An industrial robot, fault detection technology, applied in the direction of instrumentation, electrical testing/monitoring, testing/monitoring control system, etc., can solve the problems of unreserved process variables, difficult monitoring, and poor fault detection effect of industrial robots.

Active Publication Date: 2021-01-29
ZHEJIANG QIANJIANG ROBOT CO LTD +1
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Problems solved by technology

[0006] The single-feature hidden variable in this patent only retains information related to process variables and quality variables, and does not retain information unrelated to quality variables in process variables. It cannot fully extract the process data characteristics of the opera

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  • Industrial robot fault detection method

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Embodiment Construction

[0073] The following are specific embodiments of the present invention, and further describe the technical solution of the present invention in conjunction with the accompanying drawings, but the present invention is not limited to these embodiments.

[0074] Such as figure 1 As shown, an industrial robot fault detection method includes:

[0075] Step A: Obtain the data of various sampling rates of the industrial robot during normal operation, and form a multi-sampling rate training sample set for building the model. The data collected at different sampling rates include but are not limited to, The actual position value of the joint measured by the angle and position feedback device, the actual joint speed value obtained by the speed sensor, the effective value of the current, the instantaneous acceleration of the end point of the mechanical arm in the horizontal x-axis direction, and the instantaneous acceleration of the end point of the mechanical arm in the horizontal y-axi...

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Abstract

The invention provides an industrial robot fault detection method, and belongs to the field of industrial robot process monitoring and fault detection. The problem that the fault detection accuracy ofan industrial robot is low is solved. According to the method, different Markov chains are set for a preprocessed data set to construct a dynamic potential reference model containing two types of hidden variables, and quality-related process variables and quality-independent process variable information can be extracted at the same time, that is, all process information serves as supervision items of model fault detection. The statistics can be calculated according to the distribution of the two hidden variables, the statistical thresholds and SPElim of the statistics T2 and SPE are calculated by chi-square distribution, the data operated in the working process of the industrial robot are collected, the statistics and SPEtest in the working process are obtained according to the trained dynamic potential reference model, and the statistics and SPEtest are compared with the statistical thresholds and SPElim, so whether the industrial robot breaks down in the working process can be judged. According to the method, the dynamic double-hidden-variable model is established, so the double hidden variables and the dynamics can be combined, the dynamic characteristics related and irrelevantto the quality in the process are fully extracted, and the accuracy of fault detection is improved.

Description

technical field [0001] The invention belongs to the field of process monitoring and fault detection of industrial robots, and in particular relates to a fault detection method of industrial robots. Background technique [0002] Industrial robots are multi-joint manipulators or multi-degree-of-freedom machine devices widely used in the industrial field. They have certain automation and can realize various industrial processing and manufacturing functions by relying on their own power sources and control capabilities. Industrial robots are widely used in various industrial fields such as electronics, logistics, and chemical industry. [0003] During the operation of industrial robots, how to effectively monitor the system operation process is very important to help practitioners understand the process status in time, eliminate abnormal behaviors, and prevent catastrophic accidents. Among them, the data-based multivariate statistical process monitoring method provides an effec...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0243
Inventor 应泽何雨辰项剑
Owner ZHEJIANG QIANJIANG ROBOT CO LTD
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