Based on the techniques of sound intensity analysis, incomplete wavelet packet analysis (WPA) and artificial neural network (ANN), a WPA pre-processing method for noise-based engine fault diagnosis (EFD), so-called WPA–ANN model, is presented in this paper. The noises of an EFI gasoline engine under normal and fault states are measured and their contours of sound intensity level (SIL) are calculated by interpolation approach to initially investigate the possibility of a SIL-based EFD. Furthermore, an incomplete WPA model, which consists of a five-level discrete wavelet transform (DWT) and a four-level WPA, is developed and applied to the measured noise signals for extracting fault features of the engine, as is a multi-layered ANN model for engine failure classification by using the extracted features of the noises. To verify the proposed approach, the WPA–ANN model is extended to recognize other noise-related faults of the engine. The results suggest that the noise-based WPA–ANN models are effective for engine fault diagnosis. Due to its time–frequency characteristics and pattern recognition capacity, the WPA–ANN can be used to process both the stationary and nonstationary signals. In view of the applications, the proposed WPA–ANN model can be directly used in vehicle EFDs, and may be extended to other sound-related fields for failure diagnosis in engineering.

基于声音强度分析，不完整小波包分析（WPA）和人工神经网络（ANN）的技术，一种用于基于噪声的发动机故障诊断（EFD）的WPA预处理方法，即所谓的WPA–ANN模型，在本文中提出。测量EFI汽油机在正常和故障状态下的噪声，并通过插值法计算其声强级（SIL）的轮廓，从而初步研究基于SIL的EFD的可能性。此外，还开发了由五级离散小波变换（DWT）和四级WPA组成的不完全WPA模型，并将其应用于测量的噪声信号以提取发动机的故障特征，这也是一个多层模型。通过提取噪声特征，对发动机故障进行分类的神经网络模型。为了验证提议的方法，WPA-ANN模型得到扩展，可以识别发动机的其他与噪声有关的故障。结果表明，基于噪声的WPA-ANN模型对于发动机故障诊断是有效的。由于其时频特性和模式识别能力，WPA-ANN可用于处理固定信号和非固定信号。考虑到这些应用，建议的WPA-ANN模型可以直接用于车辆EFD中，并且可以扩展到其他声音相关领域，以进行工程故障诊断。WPA-ANN可用于处理固定信号和非固定信号。考虑到这些应用，建议的WPA-ANN模型可以直接用于车辆EFD中，并且可以扩展到其他声音相关领域，以进行工程故障诊断。WPA-ANN可用于处理固定信号和非固定信号。考虑到这些应用，建议的WPA-ANN模型可以直接用于车辆EFD中，并且可以扩展到其他声音相关领域，以进行工程故障诊断。