Fault diagnosis method of rotating machinery based on optimized structure convolutional neural network

Abstract

The invention discloses a rotating machinery fault diagnosis method based on an optimized structure convolutional neural network. Firstly, the working signal is collected in the normal state and the fault state of the rotating machinery, and then converted into a grayscale image, and the grayscale image and the corresponding fault Labels are used as training samples to train the constructed convolutional neural network; during the working process of the rotating machinery, the working signal is collected and converted into a grayscale image, and input to the trained convolutional neural network for fault diagnosis. The invention converts the acquired working signal of the rotating machinery into a grayscale image, and completes the multi-classification task of the rotating machinery failure through a convolutional neural network.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of construction machinery systems, and more specifically relates to a fault diagnosis method for rotating machinery based on an optimized structure convolutional neural network. Background technique [0002] Rotating machinery is the most widely used machinery in industry. With the development of modern industry and the improvement of mechanical automation, its reliability, maintainability and safety have attracted more and more attention. Rolling bearings are one of the core components of rotating machinery. According to statistics, about 30% of mechanical failures in rotating machinery using rolling bearings are related to bearing damage. Moreover, compared with other mechanical parts and components, rolling bearings have the characteristics of large dispersion of service life. Therefore, in actual work, some bearings have exceeded their design life but can still work normally, and some ...

AI Technical Summary

Problems solved by technology

These methods can effectively deal with non-stationary time series, but because the original time series is hidden and the method of relying on feature classification is used, it is difficult to fully express the information of the original time series, and it is impossible to achieve high-precision classification.

The final recognition accuracy of traditional machine learning models depends heavily on the extracted features, and there are two problems in feature extraction: one is whether the processed data has a good ability to express signal features, and the other is that the preprocessing process is extremely time-consuming

Therefore, in the actual use of engineering, the traditional machine learning model has the problems of slow speed and low accuracy, which need to be solved by research

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