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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18376

Title: A Self-Adaptive Data Analysis for Fault Diagnosis of An Automotive Air-Conditioner Blower
Authors: Wu, Jian-Da;Liao, Shu-Yi
Contributors: 車輛科技研究所
Keywords: Empirical mode decomposition;Probabilistic neural networks;Feature extraction;Fault diagnosis
Date: 2011-01
Issue Date: 2014-04-29T07:28:35Z
Publisher: Elsevier Ltd
Abstract: This paper presents a fault diagnosis system for an automotive air-conditioner blower based on a noise emission signal using a self-adaptive data analysis technique. The proposed diagnosis system consists of feature extraction using the empirical mode decomposition (EMD) method and fault classification using the artificial neural network technique. The EMD method has been developed quite recently to adaptively decompose the non-stationary and non-linear signals. It sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance classification performance. These energy features of various fault conditions are used as inputs to train the artificial neural network. In the fault classification, the probabilistic neural network (PNN) is used to verify the performance of the proposed system and compare with the traditional technique, back-propagation neural network (BPNN). The experimental results indicated the proposed technique performed well for quickly and accurately estimating fault conditions.
Relation: Expert Systems with Applications, 38(1): 545-662
Appears in Collections:[車輛科技研究所] 期刊論文

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