A Fault Diagnosis Technology of Mechanical Bearing Based on Wide Residual Network Learning Model

Abstract

The invention discloses a mechanical bearing fault diagnosis technology based on a wide residual network learning model, which includes the following steps: step 1, collecting vibration data; Using data visualization technology for each layer of data in the neural network structure, the preliminary application scene construction of the combination of vibration data and convolutional neural network model is completed; step 3, the wide residual network model is introduced, and the high frequency of vibration data is achieved by widening the convolution kernel. A compromise between the filtering of noise signals and low-frequency characteristic signals. By using a wide residual network, the core of this technology is to solve the side effects (degeneration problem) caused by increasing the depth, and at the same time, it can achieve a high accuracy of vibration data in the process of deep learning while keeping the accuracy of vibration data fault diagnosis basically unchanged. It is a compromise between filtering high-frequency noise signals and low-frequency characteristic signals.

Description

technical field [0001] The invention belongs to the technical field of mechanical vibration and relates to a fault diagnosis technology, in particular to a mechanical bearing fault diagnosis technology based on a wide residual network learning model. Background technique [0002] Swivel bearings are key components of automobile transmission systems, and their operating status directly affects the comfort and safety of the vehicle. Quantitative data analysis of rotating bearing vibration is a common method for bearing fault identification. Bearing vibration data is a typical time-series data. The traditional analysis method is to use various time-frequency feature engineering to manually select data features, which greatly increases the cost of data preprocessing. [0003] Most of the existing fault diagnosis of mechanical bearings is based on the "rule" method, which requires preprocessing of static data, and the processing process is complicated. Especially when processing...

AI Technical Summary

Problems solved by technology

By using a wide residual network, the core of this technology is to solve the side effects (degeneration problem) caused by increasing the depth, and at the same time, it can achieve a high accuracy of vibration data in the process of deep learning while keeping the accuracy of vibration data fault diagnosis basically unchanged. Filtering trade-off between high-frequency noise signal and low-frequency characteristic signal

Images