Reducing Dimensionality of Multi-regime Data for Failure Prognostics
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TECHNICAL ARTICLE—PEER-REVIEWED
Reducing Dimensionality of Multi-regime Data for Failure Prognostics Oguz Bektas . Amjad Alfudail . Jeffrey A. Jones
Submitted: 30 August 2017 / Published online: 23 October 2017 Ó The Author(s) 2017. This article is an open access publication
Abstract Over the last decade, the prognostics and health management literature has introduced many conceptual frameworks for remaining useful life predictions. However, estimating the future behavior of critical machinery systems is a challenging task due to the uncertainties and complexity involved in the multi-dimensional condition monitoring data. Even though many studies have reported promising methods in data processing and dimensionality reduction, the prognostics applications require integration of these methods with remaining useful life estimations. This paper describes a multiple linear regression process that reduces the number of data regimes under consideration by obtaining a set of principal degradation variables. The process also extracts health indicators and useful features. Finally, a state-space model based on frequencydomain data is used to estimate remaining useful life. The presented approach is assessed with a case study on turbofan engine degradation simulation dataset, and the prediction performance is validated by error-based prognostic metrics. Keywords Failure prognostics Multi-dimensional data Dimensionality reduction Remaining useful life estimation O. Bektas (&) J. A. Jones Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK e-mail: [email protected] J. A. Jones e-mail: [email protected] A. Alfudail Mechanical Engineering Department, Umm Al-Qura University, Al Taif Road, Makkah 24382, Saudi Arabia e-mail: [email protected]
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Introduction Maintenance strategies have witnessed substantial changes over the years. The paradigm has shifted from classic breakdown repairs to more complex and sophisticated condition monitoring strategies, which avoid unnecessary tasks by taking actions only when there is evidence of abnormal system behaviors [1]. Due to the increase in the variety and number of assets with more complex designs, the maintenance strategies must respond changing expectations and increasing awareness in an attempt to achieve high plant availability and reliability in operations. Prognostics can make contributions into these changing expectations by providing dynamic maintenance planning strategies for critical engineering systems. They can provide improved reliability and reduced costs for operation and maintenance of complex systems. As a steadily growing subject, prognostics have advanced expertise in various disciplines [2]. Many breakthroughs in remaining useful life estimation can be found in complex engineering systems such as electronics [3, 4], batteries [5, 6], actuators [7], turbofan engines [8, 9] and NASA’s launch vehicles and spacecraft systems [10]. In general, a typical prognostic method modeled for the complex systems depends on measured conditio
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