Planetary gear box intelligent diagnosis method based on stacking automatic encoding machine

Abstract

Disclosed is a planetary gear box intelligent diagnosis method based on a stacking automatic encoding machine. The method includes firstly utilizing a data acquisition system to acquire frequency domains of a planetary gear box, and establishing a stacking automatic encoding machine classification module with a deep structure; utilizing weight to connect to the stacking automatic encoding machine to perform sequential p retraining on all hidden layers of the stacking automatic encoding machine, and assisting the stacking automatic encoding machine in extracting fault information of the spectrum in a self adaption manner; performing fine tuning on the parameters of the stacking automatic encoding machine after p retraining by the error back propagation method, optimizing the feature extracting process of the stacking automatic encoding machine, establishing the complex nonlinear mapping relationship between the spectrum of the planetary gear box and the fault types, and completing the training of the stacking automatic encoding machine; finally, utilizing the determined stacking automatic encoding machine model to perform intelligent diagnosis of the planetary gear box in big data. By the aid of the method, the self adaption extraction of the fault features of the planetary gear box in big data and the intelligent diagnosis of the fault types can be implemented accurately and reliably.

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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