Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a feature selection and probabilistic neural network-based method is presented in this paper. Firstly, feature parameters are extracted and selected after obtaining the raw signal. Then, the selected feature parameters are preprocessed according to the faulted characteristic frequencies of components. Finally, the diagnosis results are outputted with the decision method of PNN. Experimental data is utilized to demonstrate the effectiveness of this methodology.