Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis
Vong C.-M.; Wong P.-K.; Ip W.-F.
2011-10-01
Source PublicationEngineering Applications of Artificial Intelligence
ISSN09521976
Volume24Issue:7Pages:1281-1294
Abstract

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system. © 2011 Elsevier Ltd. All rights reserved.

KeywordCase Based Reasoning (Cbr) Engine Spark Ignition Signal Analysis Kernel K-means (Kkm) Kernel Principal Component Analysis (Kpca) Wavelet Packet Transform (Wpt)
DOI10.1016/j.engappai.2011.07.002
URLView the original
Indexed BySCIE
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS IDWOS:000295151200019
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Citation statistics
Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
First Author AffilicationUniversity of Macau
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GB/T 7714
Vong C.-M.,Wong P.-K.,Ip W.-F.. Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis[J]. Engineering Applications of Artificial Intelligence,2011,24(7):1281-1294.
APA Vong C.-M.,Wong P.-K.,&Ip W.-F..(2011).Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis.Engineering Applications of Artificial Intelligence,24(7),1281-1294.
MLA Vong C.-M.,et al."Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis".Engineering Applications of Artificial Intelligence 24.7(2011):1281-1294.
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