Rotary machine fault intelligent diagnosis method based on fuzzy soft morphology graph recognition

Abstract

The invention relates to a rotary machine fault intelligent diagnosis method based on fuzzy soft morphology pattern recognition. The method comprises the following steps: arranging a vibration sensoron a rotating machine to be diagnosed to acquire a vibration signal, and forming a time sequence vibration signal by acquired data; generating a three-dimensional parameter graph from the acquired time sequence vibration signals; preprocessing difference reconstruction on the three-dimensional parameter graph; performing gray adaptive histogram equalization enhancement preprocessing on the graph after the difference reconstruction preprocessing; adopting a composite fuzzy soft form filter to carry out enhancement processing on the rotary machine vibration parameter graph after enhancement preprocessing; adopting a fuzzy soft morphology composite edge detection operator to perform texture feature extraction on the enhanced rotary machine vibration parameter graph; for the texture features,extracting fault features by adopting a gray primitive gradient co-occurrence matrix; and performing fault diagnosis by adopting an artificial neural network method according to the extracted fault features.

Description

technical field [0001] The invention relates to an intelligent fault diagnosis method for rotating machinery based on fuzzy soft morphological pattern recognition, and belongs to the technical field of fault diagnosis and signal processing of high-speed rotating machinery. Background technique [0002] With the continuous development of modern industrial production and the advancement of science and technology, in order to maximize production efficiency and product quality, some large rotating machinery such as aero-engines, gas turbines, fans, steam turbines, compressors and generators are constantly moving towards large, high-speed , automation, intelligence, continuous operation and complex structure, so that the damage caused by the failure of rotating machinery equipment will become more and more serious. Once a failure occurs, it will cause huge losses to the enterprise. Therefore, the operating status of rotating machinery should be monitored. Monitoring and fault dia...

AI Technical Summary

Problems solved by technology

In the actual fault diagnosis, because the information in the graphics, especially the information in the multi-dimensional graphics, is difficult to be automatically extracted, a large amount of information in the graphics is not fully utilized. To express it with knowledge rules, this invention proposes a method based on fuzzy soft morphology to extract the graphic features of vibration parameters and then diagnose faults, which can effectively improve the accuracy of fault diagnosis of rotating machinery

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