Mechanical failure diagnostic method and device

Abstract

The invention discloses a mechanical failure diagnostic method comprising the following steps: obtaining m (which is an positive integer) signals used for carrying out mechanical failure diagnosis in target mechanical equipment, and carrying out qualitative trend analysis and quantitative trend analysis on each signal; respectively calculating the general similarity coefficient between the results of the qualitative trend analysis and the quantitative trend analysis of the m signals and the characteristic trend corresponding to each fault type prestored in a knowledge base; judging whether the calculated maximum general similarity coefficient is more than the preset threshold value, if yes, determining the fault type corresponding to the maximum general similarity coefficient to be the fault type of the target mechanical equipment. The invention also discloses a mechanical failure diagnostic device. The method and the device of the invention can accurately recognize the fault type of the mechanical equipment.

Description

technical field [0001] The invention relates to automation technology, in particular to a mechanical fault diagnosis method and device. Background technique [0002] At present, the automatic mechanical fault diagnosis system based on artificial intelligence is more and more widely used in the mechanical field. Among them, knowledge-based fault reasoning and mathematical model-based fault diagnosis are the most common researches and applications in academic and industrial circles. However, the implementation of these two techniques needs to rely on a basic assumption, that is, there is a clear expression of the knowledge of the fault type obtained, whether by adopting mathematical equations or by constructing rules in the knowledge base (Knowlegde Database) Set (Rule Stes) expression. [0003] However, since different fault types exhibit different signal patterns in different scenarios, and even for the same fault type of similar mechanical equipment, the signal patterns e...

AI Technical Summary

Problems solved by technology

However, if different time granularities are used to divide the signal time series in the interval-dichotomy technique, and the normal or abnormal amplitude fluctuation of the signal is considered, even for the same signal time series, different trend recognition results will be obtained , leading to different fuzzy similarity calculation results and fault type identification results

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