基于DWT和PNN的印刷过程实时监测和故障诊断

Real-Time Vibration Monitoring and Diagnosis of Printing Machine Using Discrete Wavelet Transform and Probabilistic Neural Network

提出了一种新的基于离散小波变换和概率神经网络的印刷过程振动信号的实时监测和故障诊断系统。利用小波包分解技术对印刷过程振动信号进行降噪处理,并选择特殊频段进行小波包重构,有效捕捉和分离了处于信号不同频段的印刷过程振动信号故障特征分量。对提取的故障特征参数应用概率神经网络映射,实现对印刷过程振动信号运行状态的实时监测和故障诊断。仿真结果表明,该诊断方法快速、准确且易于工程实现。

The paper puts forward a novel real-time monitoring and fault diagnosis system for a printing machine＇s vibration based on discrete wavelet transform （DWT） and probabilistic neural networks（PNN）.With DWT,the printing process signal can be diagnosed,and some component in special frequency band is selected to reconstruct.With the decomposition and reconstruction of wavelet packet,the mono-components with fault feature in different frequency bands are captured and separated out.For these symptom parameters,the neural network mapping is employed to diagnose the printing machine faults further.Simulation results show that the proposed method is highly efficient,accurate and easy in practical application.