Pile-up noise reduction own coding network bearing fault diagnosis method based on particle swarm optimization

Abstract

The invention discloses a pile-up noise reduction own coding network bearing fault diagnosis method based on particle swarm optimization. The bearing fault diagnosis method provides an improved pile-up noise reduction own coding network SADE bearing fault diagnosis method, SDAE network hyper-parameters, such as cyber hidden layer nodes, sparse parameters, input data random zero setting ratio, are selected adaptively by particle swarm optimization PSO, a SADE network structure is determined, top character representation of malfunction inputting a soft-max classifier is obtained and a classification of defects is discerned. The bearing fault diagnosis method has better feature in learning capacity and more robustness than feature of learning of ordinary sparse own coding device, and builds a SDAE diagnostic model having multi-hidden layer by optimizing the hyper-parameters of noise reduction own coding network deepness network structure with the particle swarm optimization, accuracy of the classification of defects is improved at last.

Description

technical field [0001] The invention belongs to the technical field of mechanical manufacturing and relates to a mechanical fault diagnosis technology, in particular to a particle swarm optimization-based stacking noise reduction self-encoding network bearing fault diagnosis method. Background technique [0002] As a common component of rotating machinery, rolling bearings may cause major economic losses once they fail during work. Therefore, effective diagnosis and treatment of rolling bearing failures is of great significance to ensure the normal operation of the machine. [0003] The fault diagnosis method based on artificial intelligence has been widely used in the fault diagnosis of rotating machinery and achieved good results. At this stage, the fault diagnosis of rolling bearings mostly judges its running state through the detection and analysis of various state parameters, and determines the fault location and wear degree. Generally, bearing fault diagnosis can be c...

AI Technical Summary

Problems solved by technology

At present, most of its hyperparameters are determined by empirically enumerating a variety of hyperparameter combinations to obtain a better set of hyperparameters. For fault diagnosis problems, especially for fault classification problems in different fields, the generalization performance is weak.

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