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Vibration signatures,wavelets and principal components analysis in diesel engine diagnostics

机译：柴油发动机诊断中的振动信号，小波和主成分分析

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The vibration signatures of a normally aspirated diesel engine contain valuable information on the health of the combustion chamber components.It could be used to detect incipient faults in the engine.Several commonly occurring faults were induced in a 4-stroke diesel engine and the ensuing vibration signals recorded.Three different feature extraction techniques:Do-main expertise,wavelet analysis and principal components analysis(PCA),were compared to evaluate the effectiveness of each method.The extracted features were then used to develop artificial neural nets based fault diagnostic systems.It was found that the best results were obtained with the wavelets based feature extraction technique.However,each of the three systems was shown to exhibit a high degree of accuracy.In a separate study,ensembles of 3 nets were created.The majority of ensembles performed better than any of the constituent nets,thus demonstrating the power of the technique of combining neural nets in majority voting systems.
机译：正常吸气柴油机的振动信号包含有关燃烧室部件健康状况的宝贵信息，可用于检测发动机中的早期故障，在四冲程柴油机中会引起几种常见故障并随之产生振动比较了三种不同的特征提取技术：领域专业知识，小波分析和主成分分析（PCA），以评估每种方法的有效性，然后将提取的特征用于开发基于人工神经网络的故障诊断系统。发现基于小波的特征提取技术可获得最佳结果。但是，三个系统中的每一个都显示出较高的准确性。在单独的研究中，创建了3个网的集合。大多数集合表现优于任何组成的网络，因此证明了在多数情况下结合神经网络的技术的力量ng系统。

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