Study On Intelligent Fault Diagnosis Of Gearbox Based On Particle Swarm Optimization

Condition monitoring and fault diagnosis on complicated machine is a popular subject taken importantly and cared about by people.This thesis has proposed a modified algorithm for PSO based on systematical and deep research on particle swarm optimization (PSO) algorithm and its parameters, performance. It takes gearbox as a researched object, has studied on the theory and method of gearbox intelligent fault diagnosis with PSO. Its contribution as follows:1. It introduces the basic principle of PSO algorithm, and studies on the particle's itself behavior and social behavior of the algorithm by analysis of the particle's velocity evolutionary equation, then analyses the influences on performance of PSO by the main control parameters.2. It puts forward two modified PSO algorithms based on the strategy of parameters change to improve the convergence performance of the basic PSO algorithm, which are the PSO with dynamic accelerating constants (CPSO) and the PSO with adaptive velocity (VPSO). The accelerating constants and maximum limited velocity are set dynamic alternation with iteration in modified PSO. They are carried out simulated research by the test function and artificial neural networks, and the results show that modified PSO speed-ups the convergent velocity of basic PSO algorithm, while the rational parameter range of the modified PSO has been suggested. In addition, it studies on the priority problem of algorithm controlled by accelerating constants, the maximum limited velocity, and their coordinate with inertia weight. The conclusion is that the PSO with dynamic accelerating constant and coordinating with inertia weight (WCPSO) may overcome the disadvantage of the standard PSO which easily converges in local extreme point in the final period of searching process, and it has better performance.3. Aimed at the blind setting of parameter in kernel principal component analysis (KPCA), it proposes and realizes feature extraction based on kernel principal component analysis optimized by PSO algorithm. Firstly, it constructs a fitness function which Fisher discriminate function is optimized object, then WCPSO is used to optimize it by its many random particles to improve the performance of KPCA. Iris data are researched in simulation, which testify KPCA validity in feature extraction. The optimized KPCA is applied to feature extraction of gearbox typical faults.The results indicate that KPCA after parameter optimized can effectively reduce the dimensions of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.4. It presents a method of optimum placement of sensors in gearbox based PSO algorithm to solve the problem of sensors layout and localization. First, it establishes the fitness function based on mode assurance criterion;then according to the results of gearbox finite element mode analysis, WPSO is applied to optimize it to find the optimal strategy of optimum placement of sensors in gearbox. The results of the test modal analysis and frequency response character analysis for gearbox testify the rationality of the method supposed in the thesis.5. It sets up intelligent fault diagnosis based on PSO with dynamic accelerating constants and adaptive velocity. It establishes the neural network (NN) optimized by PSO for fault diagnosis, in which the features in time domain and frequency-domain from gearbox vibration signal are taken as input vector, while its main fault types as output vector of NN. In training and detecting process, PSO algorithm regulates and optimizes the global parameters such as weights and the thresholds of NN, which acts as a roughly optimizing or off-line studying process; while neural network does local ones, which acts as a fine optimizing or on-line studying process. The diagnostic results show that the method of intelligent fault diagnosis has improved the performance of gearbox fault diagnosis, and provided a universal solving programme for the nonlinear and complicate system to improve the efficient and accuracy of diagnosis and its automation.