基于小波包与神经网络的柴油机故障诊断

针对柴油机缸盖振动信号的非平稳时变特点,提出应用小波包能量法提取故障特征向量,并将提取的特征向量作为BP神经网络的输入向量进行学习训练。训练后的神经网络可以利用测量的振动信号判断柴油机的气阀机构故障状况。实践证明该方法在柴油机振动诊断中是有效可行的,对其他设备的故障诊断也具有借鉴意义。

Diesel Engine Fault Diagnosis based on Wavelet Packet and Neural Network

Aiming at the non-stationary and time-varying characteristics of vibration signals measured on cylinder head in diesel engine,a method of extracting fault characteristic vectors by the energy of wavelet packet over the signals is presented.The neural networks' input vectors are characteristic vectors,then learning and training the BPNN.After that training,the BPNN can be used to classify faults of valve train diesel engine using the input vibration signals.The practice proves that this method can efficiently diagnose and classify faults.In addition,the approach can also be applied to the fault diagnosis of other equipments.