HMM Dynamical Pattern Recognition Theories, Methods And Applications In Faults Diagnosis Of Rotating Machine

Based on the "Application on Faults Diagnosis of Rotating Machine in Hidden Markov Models" (National Nature Science Fund Project, No: 50075079), the Hidden Markov Models (HMMs) dynamic pattern recognition theories and methods are studied, then proposed the applications in faults diagnosis of rotating machine by HMM methods and developed the faults diagnosis software based on HMM. The details are studied as follows:Chapter one briefly introduces the general situation of vibration monitoring and faults diagnosis of the rotating machine. The developing and the current situations of the multivariate dynamic pattern recognition theories are summarized. At last, the background, main contents, general structure scheme and innovation points of this dissertation are present.Chapter two introduces the basic ideas of Markov Chain theories briefly, and then extends it to Hidden Markov Models through a simple example. At last the theories and algorithms of Hidden Markov Models are studied. The modification algorithms of HMM are proposed. Therefore the basic theories of this dissertation are established.Chapter three proposes the basic concept of dynamical pattern recognition and introduces the implementation theories based on probability statistics. Based on the theory of the Discrete Hidden Markov Models (DHMM), the scalar quantization method of vibration spectrum vector of rotating machine is proposed, and then the fault diagnosis method based on DHMM is designed. The experiment of run-up process of rotor machine is made to verify the effect of the new faults diagnosis method.Chapter four introduces the basic theories of Continue Hidden Markov Models (CHMM). the new method of faults diagnosis based mixture density CHMMs directly by the vibration AR coefficients vectors of rotating machine is proposed, and then the dynamic patterns presented in run-up process of rotor machine are successfully recognized. At last compares the two faults diagnosis methods of DHMM and CHMM, and points out the advantages and disadvantages of the two methods.Chapter five studies the new method for faults diagnosis of rotating machine based on SOM and HMM through the integrated information of multi-sensor. First self organization clustering method for high dimension data is proposed, and then DHMM for faults diagnosis is designed by the low dimension features. Experiments verified that the method is effective.Chapter six propose a useful method based on HMM-AR for modelling the dynamictime series of rotating machine running process. It is verified through simulating data and experiments data.Chapter seven builds the HMM faults diagnosis software for rotating machine. The developing environments, tools and implementation methods of interface between Matlab and C++ mixture programming languages on this software are introduced. At last the basic components and functions of the software are illustrated.At last, in the eighth chapter, all of the work in this dissertation is summed up, and the future researches on applications of HMMs are prospected.