Model-based Engine Fault Diagnosis Using Vibration Data

摘要：

In this paper, we propose a model-based engine fault diagnosis algorithm using engine vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we design classifier for fault isolation by applying multiclass support vector machine(SVM) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data and showed a good performance. Sensitivity analysis of proposed fault detection method is also presented by comparing proposed model with traditional model.

关键词： Fault detection fault diagnosis parameter estimation least squares estimation,support vector machine.

作者: Ji-Hyuk Yang Oh-Kyu Kwon

作者单位: School of Electrical Engineering, Inha University, Incheon 402-751, Korea

会议类型: 国际会议

会议名称: 2011年亚太航空航天技术学术会议(APISAT 2011)

会议地点: 澳大利亚

会议语种:英文

页码: 1-10

在线出版日期: 2011-02-28（万方平台首次上网日期，不代表论文的发表时间）