Industrial robot fault detection method

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...

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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 operating state of industrial robots. There are only two types. There is no further consideration of the situation of high sampling rate in existing industrial robots, so the detection effect on industrial robots is poor, making it difficult to effectively monitor the system operation process

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