An Intelligent Diagnosis Method of Planetary Gearbox Based on Stacked Autoencoder

Abstract

An intelligent diagnosis method for planetary gearboxes based on stacked autoencoders. First, the data acquisition system is used to obtain the frequency domain signals of planetary gearboxes, and a classification model of stacked autoencoders with a deep structure is established; All hidden layers of the autoencoder are pre-trained layer by layer to help the stacked autoencoder adaptively extract fault information in the spectrum; then use the reverse error propagation method to fine-tune the parameters of the pre-trained stacked autoencoder to optimize the stacked autoencoder The feature extraction process of the machine and the complex nonlinear mapping relationship between the spectrum of the planetary gearbox and the fault type are established to complete the training of the stacked autoencoder; finally, the determined stacked autoencoder model is used to carry out the intelligence of the planetary gearbox under the big data Diagnosis, the present invention realizes self-adaptive extraction of planetary gearbox fault features and intelligent diagnosis of fault states under a large amount of data, which is accurate and reliable.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of planetary gearbox equipment, and in particular relates to an intelligent diagnosis method of a planetary gearbox based on a stacked automatic coding machine. Background technique [0002] With the continuous advancement of science and technology, complex mechanical equipment such as aero-engines, large-scale wind power equipment, and EMU power transmission equipment are developing in the direction of large-scale, complex, high-speed, automation, and high-power. As the key mechanical transmission system of these complex mechanical equipment, the health of the planetary gearbox is directly related to the safe operation of the equipment. Therefore, establishing a reliable fault monitoring system to monitor the fault condition of the planetary gearbox has become a necessary way to ensure the safe and efficient operation of complex mechanical equipment. The establishment of the fault monitoring syste...

AI Technical Summary

Problems solved by technology

Due to the diversity of the planetary gearbox big data, that is, the collection of various working conditions and the characteristics of various planetary gearbox faults, it is difficult for diagnostic experts to have a comprehensive understanding of the planetary gearbox big data. Therefore, through the diagnostic expert design It is unrealistic to extract features that cover all the fault information of planetary gearbox big data; (2) Traditional intelligent diagnosis relies on shallow models for intelligent classification, but these shallow models lack sufficient ability to fit the complex big data of planetary gearboxes. Non-linear mapping relationship, thus limiting the diagnostic and generalization performance of the method

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