为了开发一种有效的激光冲击强化工艺故障诊断系统,基于声学诊断原理,对激光冲击强化过程中采集到的声信号进行小波包分析,提取出能够指示故障状态的声学特征信息,并验证了其有效性。最后将提取的特征信息结合人工神经网络,建立了激光冲击强化的故障诊断系统模型。 In order to develop an effective laser shock enhancement process fault diagnosis system, based on the principle of acoustic diagnosis, wavelet packet analysis of the acoustic signals collected during the laser shock enhancement process is carried out to extract the acoustic feature information that can indicate the fault status and verify that Effectiveness. Finally, the extracted feature information is combined with artificial neural network to establish a laser shock-enhanced fault diagnosis system model.