Research on Water Jet Fault Diagnosis Model Based on PNN Network

Abstract

The structure of water jet cutting machine is complex and the components are closely related. Its fault characteristics often have the characteristics of nonlinearity, coupling, uncertainty and complex causality. Traditional fault diagnosis methods have been difficult to solve the problem of water jet cutting machine fault detection quickly and effectively. As a new talent in the field of intelligent fault diagnosis, machine learning can independently mine the representative diagnostic information hidden in the original data and directly establish the accurate mapping relationship between the original data and the operating state, which has been increasingly applied in industrial diagnosis. In this paper, the application of intelligent fault diagnosis method for water jet cutting machine is explored. Data acquisition system of water jet cutting machine is built. Aiming at several common faults of water jet cutting machine, a fault diagnosis model is established based on PNN network, and the network is trained and tested with actual collected data. The results show that the probabilistic neural network model can better realize the fault diagnosis of common faults of water jet cutting machine.