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Title: An Expert System for Fault Diagnosis in Internal Combustion Engines Using Probability Neural Network
Authors: Wu, Jian-Da;Chiang, Peng-Hsin;Chang, Yo-Wei;Shiao, Yao-Jung
Contributors: 車輛科技研究所
Keywords: Faults diagnosis system;Feature extraction;Adaptive order tracking;Artificial neural network;Probability neural network
Date: 2008-05
Issue Date: 2014-04-29T07:27:55Z
Publisher: Elsevier Ltd
Abstract: An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks
is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals
are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a highresolution
adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order
figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the
artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability
neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and
radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed
PNN network achieved the best performance in the present fault diagnosis system.
Relation: Expert Systems with Applications, 34(4): 2704-2713
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