TAILIEUCHUNG - A data-driven framework for remaining useful life estimation

The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete run of the system, the RUL can be predicted using these approaches. | Vietnam Journal of Science and Technology 55 (5) (2017) 557-571 DOI: A DATA-DRIVEN FRAMEWORK FOR REMAINING USEFUL LIFE ESTIMATION Nguyen Dinh Hoa Posts and Telecommunications Institute of Technology, 122 Hoang Quoc Viet St., Cau Giay Dist., Ha Noi, Viet Nam Email: hoand@ Received: 2 August 2016; Accepted for publication: 5 April 2017 ABSTRACT Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete run of the system, the RUL can be predicted using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches are effective in performing RUL prediction. Keywords. Remaining useful life, prognosis, structural health management, wavelets denoise, similarity search, principal component analysis. 1. INTRODUCTION Remaining useful life (RUL) estimation is one of the most common studies in the field of prognostics and structural health management. The aim of RUL prognosis is to estimate the RUL of a system, given a short historic measurement, using either a prediction model or a data-driven technique. It helps provide an acknowledgment about the working time of the system so that appropriate maintenance or replacement actions may be scheduled prior to the failure of the system. The prediction accuracy plays an important role in reducing unnecessary maintenance, such as early .

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