关键词: 故障诊断, 概率神经网络, 自适应误差修正, 数控机床

Abstract: To meet the failure diagnosis requirements of complex Computer Numerical Control (CNC) machine,an Adaptive Error Correction Probabilistic Neural Network (AECPNN) method by using the advantages of artificial neural network in the field of failure diagnosis was put forward to diagnose real-time failure of CNC machine.Aiming at the problem that the biased judgment interface leaded by Probabilistic Neural Network (PNN) which didnt consider the crisscross influence among different patterns,an adaptive error correction model was proposed based on PNN.Through adaptive clustering and batch of correction for same categorys false classified samples,the judge interface was re-planned.Experimental results of two-spirals-apart problem and IRIS problem showed that AECPNN method was superior to PNN and Radial Basis Probabilistic Neural Network (RBPNN) in aspects of classification accuracy rate and model generalization ability.In addition,the training and testing speeds of AECPNN could meet real-time demand of classification.Applications in the failure diagnosis of CNC machine demonstrated that the proposed method was feasible and effective due to high accuracy rate of failure pattern recognition.

Key words: failure diagnosis, probabilistic neural network, adaptive error correction, computer numerical control machine