Variable-working-condition bearing fault diagnosis method based on fast kurtogram and deep residual learning

Abstract

The invention, which belongs to the technical field of mechanical part fault diagnosis, particularly relates to a variable-working-condition bearing fault diagnosis method based on fast kurtogram anddeep residual learning. According to the method, a vibration acceleration signal is converted into a series of spectral kurtosis graphs through a rapid spectral kurtosis graph method; processing is carried out to obtain a data set comprising a training set test set and a verification set; and a deep residual error network based on a residual error block and a deep convolutional neural network is constructed, the deep residual error network is trained by using the data set to obtain a trained bearing health state classification model, and the bearing health state of a to-be-detected signal is carried out by using the bearing health state classification model. According to the invention, characteristics generated by the method, namely the spectral kurtosis graphs, are basically not interfered by noises, and the spectral kurtosis graphs of the same fault under different operation conditions have considerable similarity; in the aspect of the classifier, a deep residual learning technologyis adopted, and the robustness of the method to noise and different working conditions is further enhanced by using the strong feature self-learning characteristic of the classifier.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of mechanical parts, and in particular relates to a fault diagnosis method for variable working condition bearings based on fast kurtogram and deep residual learning. Background technique [0002] Rolling bearings are one of the key components of rotating machinery and are also the most vulnerable components (more than 40% of failures are caused by bearings). Therefore, monitoring the health status of bearings in real time is of great significance to avoid unexpected failures. In order to achieve this goal, many methods have been proposed around classical signal processing methods (such as spectrum analysis, cepstrum analysis, wavelet packet decomposition), but these methods have a common defect: lack of robustness to changing operating conditions. These methods can realize real-time diagnosis under known working conditions. However, in actual industrial production, the workload and speed...

AI Technical Summary

Problems solved by technology

In order to achieve this goal, many methods have been proposed around classical signal processing methods (such as spectrum analysis, cepstrum analysis, wavelet packet decomposition), but these methods have a common defect: lack of robustness to changing operating conditions

These methods can realize real-time diagnosis under known working conditions. However, in actual industrial production, the workload and speed are often different under different working conditions. When the working conditions change, the defect characteristic frequency will also occur. Therefore, the method has to be adjusted to adapt to the new working conditions, and if the new working condition parameters are unknown, it is difficult to complete the diagnostic task

Since then, some multi-working condition diagnosis methods have been proposed one after another: Feng Zhipeng et al. proposed to use the joint time-varying amplitude-frequency demodulation spectrum to reveal the time-varying fault characteristic frequency, but this method needs to know the experienced working conditions in advance; P.Borghesani proposed a new method of diagnosis using envelope analysis, but it needs to know the defect frequency of the bearing in advance, but in practical applications, the working conditions change frequently, and it is difficult to obtain specific working condition parameters in most cases , so these multi-condition diagnosis methods are also difficult to deal with

In addition, unavoidable noise in the signal can also have an impact on the diagnosis

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