Novel deep feature learning method for planet gear fault diagnosis

Abstract

The invention discloses a novel deep feature learning method for planetary gear fault diagnosis. The method comprises the following steps: a, detecting an original vibration signal generated in the operation process of a planetary gear box of electromechanical equipment by using a vibration sensor; b, introducing a sparsity penalty term and a contractibility limit term on the basis of the loss function of the automatic coding machine; c, optimizing specific positions and key parameters of each sparse automatic coding machine and each contraction automatic coding machine in the deep learning architecture by using a quantum ant colony optimization algorithm; d, determining the initial depth of the deep learning architecture and the initial width of each layer by taking the acquired originalvibration signal of the planetary gear box as the input of the novel deep learning architecture. According to the novel deep feature learning method for planet gear fault diagnosis provided by the invention, the data learning capability and the feature extraction robustness can be exerted to the optimal at the same time, and the positions of a sparse automatic coding machine and a contraction automatic coding machine in a deep learning architecture can be actively adjusted.

Description

technical field [0001] The invention specifically relates to a novel deep feature learning method for planetary gear fault diagnosis, and belongs to the technical field of planetary gear fault diagnosis in electromechanical equipment. Background technique [0002] With the rapid development of science and industrial technology, electromechanical equipment is developing towards high efficiency, safety, reliability and intelligence. Due to the advantages of planetary gears, it has become a key component of the transmission system of electromechanical equipment. However, planetary gears usually work under harsh conditions and often break down, directly affecting the transmission efficiency of electromechanical equipment, and even leading to catastrophic accidents. Therefore, it is necessary to carry out condition monitoring and diagnostic analysis on planetary gears. [0003] A planetary gear is a nonlinear system consisting of a sun gear, planet gears and ring gear. At the ...

AI Technical Summary

Problems solved by technology

[0005] The deep learning architecture can be constructed based on the autoencoder or its improved models, but different improved models have their own characteristics and concerns, and cannot fully utilize the advantages of deep learning in feature extraction

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