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首页> 外文期刊>Expert systems with applications >An Expert System For Fault Diagnosis In Internal Combustion Engines Using Wavelet Packet Transform And Neural Network
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An Expert System For Fault Diagnosis In Internal Combustion Engines Using Wavelet Packet Transform And Neural Network

机译：基于小波包变换和神经网络的内燃机故障诊断专家系统

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摘要

In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition. In the preprocessing of sound emission signals, WPT coefficients are used for evaluating their entropy and treated as the features to distinguish the fault conditions. Obviously, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the wavelets are used as mother wavelets to build and perform the proposed WPT technique. In the classification, to verify the effect of the proposed generalized regression neural network (GRNN) in fault diagnosis, a conventional back-propagation network (BPN) is compared with a GRNN network. The experimental results showed the proposed system achieved an average classification accuracy of over 95% for various engine working conditions.
机译：在本研究中，提出了一种使用小波包变换（WPT）和人工神经网络（ANN）技术的内燃机故障诊断系统。在机械系统的故障诊断中，WPT是一种用于故障检测和识别的众所周知的信号处理技术。本系统的信号处理算法是从用于语音识别的先前工作中获得的。在声音发射信号的预处理中，WPT系数用于评估其熵，并被用作区分故障条件的特征。显然，WPT可以改善在更长的计算时间和巨大的操作数下使用的连续小波变换（CWT）。它也可以通过离散小波变换（DWT）来解决频带不一致问题，而只需分解近似版本即可。在实验工作中，将小波用作母小波以构建和执行所提出的WPT技术。在分类中，为了验证所提出的广义回归神经网络（GRNN）在故障诊断中的效果，将常规的反向传播网络（BPN）与GRNN网络进行了比较。实验结果表明，所提出的系统在各种发动机工况下均达到了95％以上的平均分类精度。

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