A Prototype Similarity-based System for Remaining Useful Life Estimation for Future Industry by Singular Spectrum Analysis-Long Short Term Memory Neural Networks Algorithm

Authors

  • Prakit Intachai The Electrical Engineering Graduate Program, Faculty of Engineering, Mahanakorn University of Technology,140 Cheumsamphan Rd., Nong-chok Bangkok 10530, Thailand https://orcid.org/0000-0002-4425-3507
  • Peerapol Yuvapoositanon Mahanakorn Institute of Innovation, Faculty of Engineering, Mahanakorn University of Technology,140 Cheumsamphan Rd., Nong-chok Bangkok 10530, Thailand https://orcid.org/0000-0002-3140-6471

DOI:

https://doi.org/10.13052/jmm1550-4646.16129

Keywords:

Remaining useful life, Singular spectrum analysis, Long-short term memory, Optimal window length

Abstract

In this paper, we propose a prototype similarity-based approach of estimating the remaining useful life (RUL) of turbofan engine data using the singular spectrum analysis and the long-short term memory (SSA-LSTM) neural networks algorithm. The algorithm consists of two steps. First, the optimal window length of the trajectory matrix of the dataset is empirically determined from a prototype dataset. Second, the estimation of the RUL of the target datasets is performed using the window length parameter obtained from the first step. The validity of the proposed algorithm is verified by testing with 200 turbofan engine datasets. The results are shown to have a significant improvement in the performance of the RUL estimation over the existing LSTM algorithm.

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Author Biographies

Prakit Intachai, The Electrical Engineering Graduate Program, Faculty of Engineering, Mahanakorn University of Technology,140 Cheumsamphan Rd., Nong-chok Bangkok 10530, Thailand

Prakit Intachai received his bachelor’s and master’s degrees in telecommunication engineering in 2010 and 2012, respectively. Currently, he is pursuing a doctor of engineering degree in electronic engineering with signal processing as the subject of study at the Mahanakorn University of Technology, Bangkok, Thailand. His main research interests include technical analysis and synthesis of time series data and estimation of remaining useful life in industrial systems.

Peerapol Yuvapoositanon, Mahanakorn Institute of Innovation, Faculty of Engineering, Mahanakorn University of Technology,140 Cheumsamphan Rd., Nong-chok Bangkok 10530, Thailand

Peerapol Yuvapoositanon received his B.Eng degree in electronics from the King Mongkut’s Institute of Technology, Ladkrabang, Thailand in 1991 and the M.Sc., degree and DIC in physical science and engineering in medicine in 1995 and Ph.D. degree and DIC in electrical engineering (digital signal processing for wireless communications) in 2002, both from the Imperial College, London, United Kingdom. Since 1992, he has been with the Mahanakorn University of Technology, Bangkok, Thailand where he is now serving as an associate professor in electronic engineering. Throughout the years, he has served as a reviewer and a technical program committee member for multiple international conferences and journals. He has also been involved in the automation industry as a consultant for image and signal processing tasks. His current research interests include signal processing theory for time series analysis in predictive maintenance and machine learning algorithms for industrial applications.

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Published

2020-08-26

Issue

Section

Smart Innovative Technology for Future Industry and Multimedia Applications