Bearing fault diagnosis method and system based on improved BP neural network

Abstract

The invention relates to a bearing fault diagnosis method and system based on an improved BP neural network. The method comprises the steps of obtaining a training sample set containing annotation information, inputting the training sample set to an optimized BP neural network, and outputting a predicted fault type; determining a loss function according to the actual fault type and the predicted fault type, adjusting parameters of the optimized BP neural network according to the value of the loss function until convergence conditions are met, and storing the optimized BP neural network which is completely trained; and obtaining a to-be-detected vibration signal, extracting corresponding wavelet packet energy characteristics, inputting the wavelet packet energy characteristics into the well-trained optimized BP neural network, identifying and predicting a fault type, and performing fault diagnosis. According to the invention, the fault characteristics are extracted from the original data by using wavelet packet transformation, the efficiency and performance of the BP neural network in mechanical equipment fault diagnosis are improved, and efficient monitoring and diagnosis processes are realized.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and system based on an improved BP neural network. Background technique [0002] With the development of Industry 4.0, mechanical equipment has gradually become complex, integrated, and automated. Once a component fails, it will cause the entire equipment to fail to work or even stop the production line, and more seriously, it will endanger the lives of employees. Therefore, we have to attach great importance to the reliability, maintainability and safety of equipment. [0003] In particular, there are various production, manufacturing, and processing equipment in the industry, and the safety maintenance of equipment is a problem faced by enterprises and an important subject of research. Most of the mechanical equipment is rotating machinery, and rolling bearings are one of the most commonly used general-purpose parts in various rotating...

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Problems solved by technology

[0004] In view of this, it is necessary to provide a bearing fault diagnosis method and system based on an improved BP neural network to solve the problem of insufficient accuracy of fault diagnosis in the prior art

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