Bearing Remaining Life Prediction Method Based on Wavelet Transform Denoising and Feature Preservation CycleGAN under Unbalanced Samples

Abstract

With the rapid development of industrialization today, mechanical equipment has undergone significant development and progress in terms of type and quality. Rolling bearings are the key components of mechanical transmission and the most vulnerable mechanical components. Only 10% of rolling bearings can operate as designed. Exploring more scientific and reasonable test methods to predict the remaining service life of rolling bearings can not only reduce operating costs, but also prevent accidents. Life prediction models can be divided into three categories: statistical models, dynamic models, and data-driven prediction models. The existing life prediction models based on statistics, dynamics, etc. continue to increase the workload and difficulty of life prediction in the case of increasingly complex, time-consuming and labor-intensive mechanical equipment. With the rapid development of information technology, industrial process data collection has become efficient and convenient, providing sufficient data resources for data-driven intelligent life prediction technology, and gradually showing great development potential. The remaining life prediction mainly includes two key parts: the feature extraction of bearing vibration data and the construction of life prediction model. Therefore, this paper takes the rolling bearing as the research object, and carries out the remaining life prediction research from the two directions of feature extraction and prediction model construction. In the bearing operation monitoring data, the proportion of the fault data relative to the normal operation data is very small, forming a typical data imbalance phenomenon, and the traditional life prediction is not ideal when dealing with such data. In this paper, a secondary filtering method combining wavelet transform and parameter adaptive variational modal decomposition is used to preprocess the data and enhance the features; and fully consider the influence of unbalanced cluster data, a life prediction based on CycleGAN is proposed. The high amplitude information of the bearing fault signal is retained, and a large number of fault data samples are generated, which improves the accuracy of the bearing remaining life prediction. prediction model.