Fault Prognostic Technology Of Rolling Element Bearings Based On HSMM

Rolling element bearings are of paramount importance to almost all rotating machinery. As a consequence of their importance and wide spread use, Rolling element bearing failure is one of the foremost causes of breakdowns in rotating machinery. There are several reasons that a bearing fails, such as improper lubrication and mounting, inverse environment, overload, fatigue, etc. So the fault prognostics of rolling element bearings is important to detect the incipient faults, to optimize maintenance scheduling, to avoid catastrophic failure, to extend machinery life and to reduce costs.The purpose of the research is aiming to solve the problem of fault prognostics of rolling element bearings. The mechanisms of the faults and the trends of failure process are analyzed systematically. Hence a fault prognostic method of rolling element bearings based on the Hidden Semi-Markov Models(HSMM) is studied in this thesis. The main contents of the research are as follows.1. The mechanism of the faults of rolling element bearings is analyzed and the model of failure process trend is built.Firstly, the mechanism of the faults of rolling element bearings is analyzed systematically. Secondly, a Hidden Semi-Markov Model which describles the failure process trend of rolling element bearings is built. Finally, the description of the state duration and the state transition all the life time are obtained.2. A prognostic feature information extraction scheme based on Wavelet Energy Entropy of rolling element bearings is proposed.A prognostic feature information extraction scheme is proposed based on Wavelet Energy Entropy in order to solve the problem of the difficulty to extract a prognostic feature information of rolling element bearing. According to the test result, the failure process of rolling element bearings all the life can be describled through the feature extraction method.3. A fault prognostic method of rolling element bearings based on HSMM is studied.In order to solve the problem such as the uncetainty of parameter setting, avoiding underflow due to the multi-samples training, a modified algorithm of HSMM is studied in detail. Based on the above researchs, a fault prognostic method of rolling element bearings based on HSMM is studied through taking the Wavelet Energy Entropy of viberation as the prognostic feature information.4. Experiment and validation.An experimental plan of rolling element bearings is designed, and the fault prognostic technology of rolling element bearings based on HSMM is validated. Experimental results show that this proposed method is effective and practicable to prognosticate fault and the remaining life of rolling element bearings.