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Fault Detection of Fuel injectors Based on One-Class Classifiers

Modern Mechanical Engineering 2014年第1期4卷 19-27页

作者： Dimitrios Moshou Athanasios Natsis Dimitrios Kateris Xanthoula-Eirini Pantazi Ioannis Kalimanis Ioannis Gravalos Agricultural Engineering Laboratory Aristotle University of Thessaloniki Thessaloniki Greece Department of Biosystems Engineering Technological Educational Institute of Thessaly Larissa Greece Department of Exploitation of Natural Resources and Agricultural Mechanics Agricultural University of Athens Athens Greece

Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors.

关键词： Fuel injectors Fault Detection Acoustics Neural Networks One-Class Classifiers

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