C-MAPPS datasets; Data-driven prognostics; Neural networks; Similarity-based RUL calculation; Algorithm performance; Operating condition; Pre-processing method; Prediction performance; Prognostics and health managements; Remaining useful life predictions; Remaining useful lives; Control and Systems Engineering; Software; Mechanical Engineering; Computer Science Applications; Industrial and Manufacturing Engineering
Abstract :
[en] The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
BEKTAS, Oguz ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal ; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
Jones, Jeffrey A.; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
Sankararaman, Shankar; Data Science and Analytics Manager, Pricewaterhouse Cooper, San Jose, United States
Roychoudhury, Indranil; Stinger Ghaffarian Technologies, Inc., NASA Ames Research Center, Moffett Field, United States
Goebel, Kai; NASA Ames Research Center, Moffett Field, United States ; Division of Operation and Maintenance Engineering, Luleå Technical University, Luleå, Sweden
External co-authors :
no
Language :
English
Title :
A neural network filtering approach for similarity-based remaining useful life estimation
Publication date :
17 March 2019
Journal title :
International Journal of Advanced Manufacturing Technology
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