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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network
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Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network

机译：基于扩展神经网络的内燃机故障诊断

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摘要

The internal combustion engine (ICE) is a special type of reciprocating and rotating machine which is an essential part of every automobile and industry in our modern life. Various faults frequently encounter this machine and cause significant losses. Thus, in this paper, we propose an effective and automated technique to diagnose the faults. Unlike the existing methods in this field, the emitted sound signal of the “ICE” is exploited as the information carrier of the faults, wavelet packet decomposition is used as the feature extraction tool, and finally, extension artificial neural network is used for the classifications of the extracted features. The extension neural network (ENN) consists of just the input layer and the output layer. This simple structure of the “ENN” enhances the performance compared to the traditional neural networks and enables us to easily insert any new information, like a new fault or new feature. Therefore, “ENN” is adaptive for new information by just adding new nodes without affecting the previously built network. The results of the proposed method show the effectiveness and the high recognition rate in classifying different faults.
机译：内燃机（ICE）是往复运动和旋转机器的一种特殊类型，是现代汽车中每个汽车和工业中必不可少的一部分。机器经常遇到各种故障，并造成重大损失。因此，在本文中，我们提出了一种有效的自动化技术来诊断故障。与该领域中的现有方法不同，利用“ ICE”发出的声音信号作为故障的信息载体，使用小波包分解作为特征提取工具，最后，使用扩展人工神经网络进行分类。提取的特征。扩展神经网络（ENN）仅由输入层和输出层组成。与传统的神经网络相比，“ ENN”的这种简单结构提高了性能，并使我们能够轻松插入任何新信息，例如新故障或新功能。因此，“ ENN”仅通过添加新节点而不影响先前构建的网络即可适应新信息。所提方法的结果表明，该方法对不同故障进行分类是有效的，识别率较高。

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