On-demand Regression to Improve Preciseness of Time to Failure Predictions

Sylvain Letourneau, Chunsheng Yang, Zhenkai Liu

Despite the availability of huge amounts of data and a variety of powerful data analysis methods, prognostic models are still often failing to provide accurate and precise time to failure estimations. This paper addresses this problem through an innovative approach integrating several machine learning algorithms. The approach proposed relies on a classification system to determine the likelihood of component failures and to provide rough indications of remaining life. It then introduces clustering and SVM-based local regression to refine the time to failure estimations provided by the classification system. The paper illustrates the applicability of the proposed approach through a real world aerospace application and discusses data pre-processing requirements. The preliminary results show that the proposed method can reduce uncertainty in time to failure estimates, which in turn helps augment the usefulness of prognostics.

Subjects: 12. Machine Learning and Discovery; 1.5 Diagnosis

Submitted: Sep 17, 2007