A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower

Jian-Da Wu, Shu Yi Liao

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)545-552
Number of pages8
JournalExpert Systems with Applications
Volume38
Issue number1
DOIs
Publication statusPublished - 2011 Jan 1

Fingerprint

Blowers
Failure analysis
Neural networks
Air
Decomposition
Backpropagation
Feature extraction
Time series

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower. / Wu, Jian-Da; Liao, Shu Yi.

In: Expert Systems with Applications, Vol. 38, No. 1, 01.01.2011, p. 545-552.

Research output: Contribution to journalArticle

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