Abstract
Research on federated learning method for fault diagnosis in multiple working conditions
Highlights
As an indispensable core component in the connection and transmission chain of mechanical equipment, rolling bearings play an important role in aerospace, electric power, metallurgy, and other industrial fields[1]
During the working process of mechanical equipment, the change of load is inevitable, and the change of load will cause the change of the motor speed, which can be seen in the collected data containing vibration data of different loads
To solve the above-mentioned problems, this section proposes a modular federated learning method (MFLM) for multi-working condition fault diagnosis by designing a federation mechanism using dynamic routing technology
Summary
As an indispensable core component in the connection and transmission chain of mechanical equipment, rolling bearings play an important role in aerospace, electric power, metallurgy, and other industrial fields[1]. Existing fault diagnosis methods based on deep learning cannot effectively extract features from multiworking condition data. Designed a deep one-dimensional convolutional neural network based on the idea of modularization, which can perform fault diagnosis under multiple working conditions in a noisy environment. Geng et al.[27] first used wavelet analysis to process the original data, and extreme learning machine was used as a classifier to identify rolling bearing faults Rules such as maximum pooling used in the previous work discard some features and cannot make full use of the extracted features. To overcome the above shortcomings, this paper designs a deep learning fault diagnosis network based on modular federation for bearings under multi-working conditions. (3) Modular federated learning increases the feature expression capabilities of the network to realize online fault diagnosis of bearing operated in any working condition.
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