Rolling bearing fault diagnosis method based on multi-scale convolutional neural network

Abstract

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale convolutional neural network. The method comprises the following steps: decomposing an original vibration signal into intrinsic mode components through adaptive variational mode decomposition; performing envelope demodulation on the intrinsic mode component through an energy operator to calculate an envelope signal of the intrinsic mode component; calculating an envelope order spectrum of an envelope signal through angular domain resampling and Fourier transform, calculating envelope kurtosis of the envelope order spectrum, selecting a component with the maximum envelope kurtosis as an effective component, and learning a mapping relation between the envelope order spectrum of the effective component and a fault category through a multi-scale convolutional neural network to accurately identify the health state of the fan rolling bearing. The rolling bearing fault diagnosis method method has the advantages of being high in identification precision and small in sample dependence, and can be effectively applied to intelligent fault diagnosis of the fan rolling bearing under the variable working conditions only according to the historical monitoring data training model under the single working condition.

Description

technical field [0001] The invention relates to the technical field of intelligent fault diagnosis, in particular to a rolling bearing fault diagnosis method based on a multi-scale convolutional neural network. Background technique [0002] Fans are one of the most important auxiliary machines in thermal power plants. Accurate monitoring and evaluation of their health status is of great significance for improving the safety and economy of thermal power generation. Affected by factors such as harsh working environment and complex operating conditions, it is difficult to effectively extract and identify the fault characteristics of key components such as rolling bearings, which seriously affects the reliability of power production. It has become an urgent task for the thermal power industry to effectively evaluate the running state of the rolling bearing of the wind turbine, give early warning of failures, and avoid major failures. [0003] With the development of artificial ...

AI Technical Summary

Problems solved by technology

However, the construction of intelligent diagnostic models requires a large number of training samples covering various operating conditions, which is difficult to meet in practical engineering applications

Therefore, traditional diagnostic methods face great challenges in industrial applications

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