Research On Fault Diagnosis Method Of Equipment Based OnWavelet Neural Network

Neural network offers a new method for fault diagnosis owing to its memory ability,self-learning ability and strongly fault tolerance. This paper makes research on the faultdiagnosis method of neural network deeply based on the fault characteristics of pump whichis widely used in experiment.Wavelet packet analysis is used to do the signal processing. Wavelet db3 is chosen, andall signals are de-noised by hard threshold de-noising method. Then wavelet packetdecomposes and constructs the energy eigenvectors which are regarded as the inputeigenvectors of the neural network.A three-layer BPNN is applied to do the fault diagnosis. The results of simulation showthat the network traps in local minimum easily, and both the number of hidden neurons andthe learning rate are difficult to decide either.In order to solve these questions above, this paper designs GA+BP algorithm. In thisalgorithm, genetic algorithm is used to optimize the number of hidden neurons, the initialweights and thresholds, and the learning rate of BPNN first, and then fault diagnosis is doneby this neural network which has the optimum structure and parameters. In GA+BP neuralnetwork, each chromosome is divided into the connection genes and the parameter genes, anddifferent genetic operations are carried on two parts. Connection genes are binary type andparameter genes are real-valued. Mixed crossover and mutation operations are operated on theconnection genes and parameter genes separately. It means the connection genes adoptsingle-point crossover and simple mutation, and the parameter genes adopt arithmeticcrossover and non-uniform mutation. Both the crossover and mutation operators adoptself-adaptive method. Comparing the simulation results of GA+BP neural network with BPNN, we know thatGA+BP neural network has less work but high training performance, and the local minimumis inexistent. In addition, the GA+BP neural network can diagnose the failure more correctlythan BPNN. In conclusion, GA+BP neural network can accomplish the pump fault diagnosismuch better.