基于振动信号的柴油机进排气系统故障诊断研究

Fault Diagnosis Study of the Inlet and Exhaust System Based on Vibration Signal

提出了一种根据柴油机气缸盖振动信号诊断进排气系统故障的方法 ,介绍了对柴油机的振动信号进行小波降噪和小波分解 ,提取相应特征向量 ,然后将振动样本的特征向量作为径向基函数神经网络的输入参数 ,以故障类别作为输出参数训练该网络 ,训练后的神经网络可以利用测量的振动信号来判断柴油机的故障状况。

A new fault diagnosis method for diesel engine based on the wavelet analysis and neural network is proposed.The wavelet theory is used to analyze the vibration signal of the diesel engine, mainly the reduced noises and the wavelet decomposition are used to achieve the relevant characteristic vectors.The model of neural networks is built,its input parameters include eight relevant characteristic vectors,and the output parameters represent four kinds of faults.At last using the experiment data to train it.After that treatment,the padial basis function neural network can be used to classify the faults of diesel engine according to the input vibration signal.The simulation and test results demonstrate that this method can efficiently diagnose and classify the faults.