Machine learning-based methods for TTF estimation with application to APU prognostics

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 46; no. 1; pp. 227 - 239
• Main Authors: Yang, Chunsheng, Letourneau, Sylvain, Liu, Jie, Cheng, Qiangqiang, Yang, Yubin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2017; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Clustering; Computer Science; Data mining; Estimates; Estimating; Failure; Intelligence; Machine learning; Machines; Maintenance costs; Manufacturing; Mechanical Engineering; Methods; Predictive maintenance; Processes; Prognostics and health management; Regression; Regression analysis; Sensors; Support vector machines (SVMs); Regression; Support vector machines (SVMs); Clustering; Prognostics and health management; Machine learning; Classification
• ISSN: 1573-7497; 0924-669X; 1573-7497
• DOI: 10.1007/s10489-016-0829-4

NRC publication: Yes
Machine learning-based predictive modeling is to develop machine learning-based or data-driven models to predict failures before they occur and estimate the remaining useful life or time to failure (TTF) accurately. Accurate TTF estimation plays a vital role in predictive maintenance or PHM (Prognostic and Health Management). Despite the availability of large amounts of data and a variety of powerful data analysis methods, predictive models developed for PHM still fail to provide accurate and precise TTF estimations. This paper addresses this problem by integrating machine learning algorithms such as classification, regression and clustering. A classification system is used to determine the likelihood of component failures such that rough indications of TTF are provided. Clustering and SVM-based local regression are then introduced 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 with details on data pre-processing requirements. The results demonstrate that the proposed method can reduce uncertainty in estimating time to failure, which in turn helps augment the usefulness of predictive maintenance.