Research On Fault Diagnosis Methods For Rolling Bearings Based On Time-Frequency Analysis

Rolling bearings are significant components in rotating machinery and their fault diagnosis techniques play an important role in the safe operation of machines. In the past several decades, the vibration signal, acoustic radiation, temperature and other parameters have been used in diagnosing bearing faults, with the vibration signal being the most widely used because of its characteristics of containing rich information, the simplicity of the test and mature theory.Viewing from the domestic and foreign research methods and contents, bearing fault diagnosis technology is still in the groping stage, hardly to form a complete and effective theory for bearing fault diagnosis. The theoretical study of bearing fault was less, more studies were based on experiment data, combining different fault feature extraction methods and pattern recognition theories to diagnose bearing faults.As the view of the research results, studies for specific scale single point was particularly abundant, and they also received great results. However, at the same time, we also noticed that the correlation between these methods was not strong, and among these methods, few of them were able to effectively promote. Bearing fault signal contains a large amount of information, it is possible to get good results from different angles to extract signal analysis, but the lack of theoretical analysis makes them hard to take into practice.After studying the bearing mechanics theory and signal processing theory, the core content of this article was the pattern recognition of bearing faults based on time-frequency analysis. The details are as follows:1. The research of different pre-processing methods for rolling bearings. Aiming at the problem of vibration signal pretreatment, we researched the noise elimination mechanism of different pretreatment methods, tested their effect for rolling bearing vibration signal de-noising, and investigated the optimization method of existing pretreatment technology;2. The research of feature extraction method for vibration signal. In view of the characteristic data selection, the calculation and analysis data with different characteristics and their combination were taken. And time-frequency domain analysis were used to extract vibration signal features in both time domain and frequency domain; Meanwhile, I brought blind source separation technology into bearing fault diagnosis to identify bearing characteristic frequencies and I also took the signal separation experiment for impact signals, reached beneficially phasic achievements;3. The research of fault diagnosis for bearing vibration signals. Bearing fault diagnosis is an important application of pattern recognition theory. Based on the study and understanding of pattern recognition theory, we analyzed the existing pattern recognition method, and took cross check by experimental data to determine the effect of a variety of methods.Through these studies in this paper, an effective theory and technology basis have been made for bearing fault diagnosis. At the same time, with the help of signal de-noising theory and blind source separation for rolling bearing vibration signal processing, some meaningful explorations has been made on the new direction of the bearing impact signal separation.