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摘要:
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.
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篇名 Fault Detection of Fuel Injectors Based on One-Class Classifiers
来源期刊 现代机械工程(英文) 学科 医学
关键词 Fuel Injectors FAULT Detection ACOUSTICS NEURAL Networks ONE-CLASS CLASSIFIERS
年,卷(期) 2014,(1) 所属期刊栏目
研究方向 页码范围 19-27
页数 9页 分类号 R73
字数 语种
DOI
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研究主题发展历程
节点文献
Fuel
Injectors
FAULT
Detection
ACOUSTICS
NEURAL
Networks
ONE-CLASS
CLASSIFIERS
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
现代机械工程(英文)
季刊
2164-0165
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
141
总下载数(次)
0
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0
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