摘要
提出一种基于小波神经网络非线性观测器的故障检测方法,它把信号分析和数学模型结合在一起进行分析,并利用小波信号对输入信号的去噪功能和人工神经网络的自学习本领,获得系统输入输出的非线性动力学特性,实时计算残差和进行逻辑判断。这种方法可提高电机故障检测的速度和准确率。通过对电牵引采煤机电机模型结构损伤故障的仿真研究表明,该方法是可行的。
A method of fault detection on nonlinear observer using wavelet neural networks is presented. This approach is a combination of signal analysis and mathematical model . By denoising function of wavelet and the learning itself function of neural network , the input and output nonlinear dynamic characteristic of system are obtained . The output prediction error, generated from the read output and waved neural networks estimated output is used as a residual error to execute logical judge. And this approach can improve the speed and accuracy rate of fault detection. Simulation for structural damage faults of nonlinear synehro motors of the electro - drawing mining machine show that the fault detection approach is feasible.
出处
《煤矿机械》
北大核心
2006年第4期705-707,共3页
Coal Mine Machinery
基金
广东省自然科学基金资助项目(05011905)
关键词
小波神经网络
故障检测
非线性观测器
wavelet neural networks
fault detection
nonlinear observer
