Classifying inventory using an artificial neural network approach
Computers and Industrial Engineering
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Immune model-based fault diagnosis
Mathematics and Computers in Simulation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system
Expert Systems with Applications: An International Journal
Fault diagnosis of an automotive air-conditioner blower using noise emission signal
Expert Systems with Applications: An International Journal
A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease
Computers and Electrical Engineering
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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 high-resolution 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.