FAULT DIAGNOSIS BASED ON INTELLIGENT INFORMATION PROCESSING TECHNOLOGY

摘要

This paper proposes a fault diagnosis method based on intelligent information processing technology. It first extracts the characteristics of the primary sample signals with wavelet transform, then optimizes the key characteristics to be input parameters of neural network with the genetic algorithm, and finally recognizes the state and classifies the characteristics with neural network. This method not, only effectively decreases the neural training time and neural calculation, but also enhances the correctness and reliability of the characteristic classification and fault diagnosis. The performance of the proposed method is proven by the bearing fault diagnosis experiment.