Open AccessRemaining useful life estimation without needing for prior knowledge of the degradation features
Author(s) -
Benmoussa Samir,
Djeziri Mohand Arab
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0005
Subject(s) - redundancy (engineering) , computer science , euclidean distance , data mining , estimation , reliability engineering , fault (geology) , process (computing) , degradation (telecommunications) , kinematics , artificial intelligence , engineering , telecommunications , systems engineering , physics , classical mechanics , seismology , geology , operating system
This study deals with a hybrid method for the estimation of the remaining useful life (RUL) that does not require prior knowledge on the degradation phenomena, as the lack of information about the process of degradation is one of the main issues in the theory of fault prognosis of dynamic systems. The proposed method uses a dynamic model for the generation of fault indicators based on the principle of analytical redundancy, and the generation of a database by simulating the normal and faulty operations. Then, a data‐driven method is used to identify a cluster for each operating state. Taking into account uncertainties allows the generation of normal operation thresholds, which govern the launch of a kinematic model based on the calculation of the Euclidean distance for RUL estimation. The developed method is applied with three of the most used methods in literature, to a mechanical transmission system for the estimation of the remaining time before the breakage of the drive belt. The obtained results are discussed and evaluated with appropriate metrics in order to demonstrate the effectiveness of the proposed approach.