自适应遗传小波网络在发动机故障诊断中的应用

Applying adaptive genetic wavelet network to engine fault diagnosis

由于包含丰富故障信息的传感器信号是由多振源和背景噪声的混合,小波变换良好的时频域局部化特性可以有效地滤除噪声,并且通过对重构的小波系数求取均方根值来有效提取故障的特征向量,根据采集数据的故障工况,建立学习样本.通过网络训练建立神经网络的输入和输出良好的非线性映射,进而通过特征向量输入来诊断系统的故障.由于神经网络具有收敛速度慢和容易陷入局部最小点的缺陷,对神经网络进行改进和优化.仿真结果误差分析表明,该网络具有良好的诊断效果.

Owing that the sensor signals from intensive fault information are composed of multiple vibration sources and background noises,wavelet transform possesses good localization property of time and frequency domains for effective noise elimination.By applying RMS calculation for recomposed wavelet coefficients,the fault feature vectors can be extracted.Through data sampling in terms of fault working conditions,the learning samples are obtained.Accordingly,the nonlinear mapping between neural network inputs and outputs is well established via training.Afterwards,the system faults are detected based on input feature vectors.Due to the slow convergence speed and potential local minimum,the specific neural network is adapted.From the simulation results,the improved network is proven effective for fault diagnosis.