Rotating Machine Fault Diagnosis Method Based On Volterra Series Identification

This paper research the rotating machinery fault diagnosis based on the Volterra series model of nonlinear system identification in the funded of the National Natural Science Foundation of China (50775208) and the Natural Science Foundation of Henan Education Department (2008C460003,2006460005) funded, and do some simulation and Experimental studies, made a number of innovative achievements, the main contents of this article include:The first chapter discusses the significance of this issue raised, reviews the research status of non-linear system identification, and presents a nonlinear analysis of fault detection and diagnosis of the development and research status at home and abroad,and presented the main contents of this paper and innovations.The second chapter discusses the theory of nonlinear Volterra series model, describes the time-domain Volterra series representation and frequency domain representation and the nature, then, discusses of non-linear analysis based on fault detection and diagnosis theory, this chapter is the theoretical basis of the whole thesis.The third chapter discusses the least squares method for the general identification of Volterra series in the existence of large computation, data storage space occupied by more than enough of a recursive least squares based on the Volterra series model identification method, and applied to rotating machinery fault diagnosis. The method uses recursive inverse method avoids the direct inverse of the observation matrix, reducing the amount of calculation. Experimental studies the rotor to rub from the normal state of Volterra series of nuclear changes, the results show that the method has fast convergence speed and convergence precision, and achieved a good recognition results.The fourth chapter discusses the ant colony optimization (ACO) of the basic theory and algorithms, ant colony optimization has the powerful search capabilities for solving optimization problems in the performance of the outstanding advantages of this, the introduction of ant colony optimization to the class based on Volterra number of model identification of nonlinear systems is proposed based on the Volterra ACO nuclear identification method in time domain and gives the steps to achieve simulation results show that, whether the interference in the absence of noise, or interference with noise, Volterra kernel parameter identification has good convergence, identification accuracy and robust against noise. Finally, the method is applied to fault diagnosis of rotor system, the experimental results show that when the Volterra field of nuclear (generalized impulse response function) could reflect the changes in the system can effectively distinguish between normal and fault state.The fifth chapter, based on Ant Colony Optimization for the Volterra series model of nonlinear system identification method and its shortcomings, the ant colony in the number of individual movement is random, and when population size is large, to find out a better path requires a longer search time, was proposed based on Adaptive Ant Colony Optimization (AACO) of Volterra nuclear identification method. And this paper gives the specific steps of the algorithm, while the corresponding ant colony optimization (ACO) of the Volterra nuclear identification methods was compared. Simulation results show that both in the absence of noise interference, or whether the interference noise, based on Volterra Adaptive Ant Colony Optimization Algorithm for nuclear identification of Volterra-based ant colony optimization algorithm for nuclear identification can be a very good estimation accuracy and robust against noise, from the convergence curve, even if the noise interference, the two identification methods of the convergence process smooth, but relatively speaking, the proposed ant colony optimization based on adaptive Volterra nuclear identification algorithm fast convergence of the Volterra-based ant colony optimization algorithm for nuclear identification. Finally, the proposed method is applied to the rotor rub-impact fault system, the experimental results confirm the effectiveness of the proposed method.The sixth chapter summarizes the work, and put forward a new vision in the Volterra series theory of rotating machine fault detection and diagnosis in the field of application.