Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network

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.