Application of Wavelet Transform and Fuzzy Theory for Turbo-Generator Fault Mode Classification

A novel combined method based on wavelet transform and fuzzy neural network for concurrent vibrant faults of turbo-generator sets is presented. The fault feature is distinct and the high frequency components in the process can be employed to reveal fault characteristics. Fuzzy neural networks show good ability of self-adaption and self-learning, wavelet transformation or analysis shows the time frequency location characteristic and multi-scale ability. The fault diagnosis model of turbo-generator set is established and the improved least squares algorithm is used to fulfill the network parameters initialization. By means of choosing enough samples to train the fault diagnosis network and the information representing the faults is input into the trained diagnosis network, and according to the output result the type of fault can be determined. The computation of wavelet fuzzy network is dynamic and global optimized, therefore the convergence speed and the error precision are improved. The test results of demonstrates that the proposed method has its advantage in dealing with concurrent fault situations and is featured by a high probability of accuracy, proving the method to be effective.