Fault Prognostics Technologies Research For Key Parts And Components Of Mechanical Transmission Systems

The safety and reliability of the mechanical transmission systems are vital important, which are widely used in the national defense and national economy realms as a type of important technical equipments. The key parts and components of mechanical transmission systems such as gear and bearing are easily suffered damage and fault due to lasting and continuous running under heavy load with high rotate-speed, and hence resulting in transmission system broken. So it is of great significance to study the practical and reliable fault prognostics technologies of the key parts and components of mechanical transmission systems and active fault prognostics realization methods, which are the technical base of the fault prevention and mechanical transmission systems operational readiness keeping.Funded by the advanced project "Equipment Power and Transmission Systems Condition Monitoring and Fault Prognostics Technology", the dissertation systematically analyzes the fault mechanism and fault evolution rules and studies the fault evolution rules modeling of the key parts and components of mechanical transmission systems mainly aiming at the shortage of fault evolution rules analysis modeling and fault prognostics methods. Then, the feature information extraction technology based on wavelet correlation feature scale entropy, as well as HSMM degradation state recognition and fault prognostics technologies are deeply studied. The research findings have great referrence value and guiding meaning on the improvement of fault prognostics ability of the mechanical transmission parts and components.The main contents of the dissertation are as follows.1. Fault mechanism analysis and modeling of the key parts and components of mechanical transmission systemsOn the base of systematical analysis of the main fault mode, invalid mechanism and fault evolvement rules of mechanical transmission systems key parts and components gear and bearings, the fault evolution rules HSMM model is constructed making use of the similarity between the degradation state in the fault evolution process and HMM (both are apperceived by the observations) and the characteristic that HSMM can logically describe degradation state resident time in the fault evolution process, which lay a solid foundation for the research of fault prognostics technology of the key parts and components of mechanical transmission systems.2. Fault prognostics technology studies of the key parts and components of mechanical transmission systems(1) The feature information extraction technology based on wavelet correlation feature scale entropy is studied in order to resolve the feature information extraction problem of the degradation state recognition and fault prognostics. Feature information extraction has direct relation to the accuracy of the degradation state recognition and the reliability of the fault prognostics, yet the noise is the main obstacle. So the dissertation presents a new feature information extraction method, namely the wavelet correlation feature scale entropy-based feature information extraction technology, which is based on the basic idea of the wavelet entropy theory and conjuncts with wavelet correlation filter denoise method and Shannon information entropy principle. The proposed method can denote equipment operation state more efficiently and synthetically compared to the normal wavelet entropy-based feature extraction method, and hence provides a new effective way for the feature information extraction of the degradation state recognition and fault prognostics.(2) HSMM degradation state recognition and fault prognostics technologies are studied in order to confirm the current degradation state and prognosticate the remaining useful life of the equipments. Firstly, the problems, when HSMM is used to the degradation state recognition and fault prognostics domain, such as how to select model initial parameter, underflow and model generalization and so on are studied, and a modularized training algorithm based on HSMM state recognition and fault prognostics is proposed. Then, the application method about how the HSMM is use to the degradation state recognition and fault prognostics of the key components of power and transmission systems is studied and its feasibility and validity were proved by demonstrations. Further more, in order to improve state recognition and prognostics precision and make full use of multi-source sensor information, the KPCA-HSMM degradation state recognition and fault prognostics method is studied, which involves the KPCA so as to fuse multi-source feature information. The study outcomes indicate that the proposed method can fuse the multi-source information in the state recognition and fault prognostics domains and can improve the reliability and accuracy of the state recognition and fault prognostics.3. Experiment verificaitonThe rolling bearing, which is a typical key parts and components of mechanical transmission systems, is selected as the study object. The experiment was carried out on the ABLT-7 bearing testing machine in the Hang Zhou Bearings Testing and Research Center Ltd, and the tested life cycle experimental data verified the feasibility and validity of the proposed method.