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Machines 2018, 6(3), 35; https://doi.org/10.3390/machines6030035

An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

1
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Department of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Korea
*
Author to whom correspondence should be addressed.
Received: 20 June 2018 / Revised: 21 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
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Abstract

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems. View Full-Text
Keywords: prognostics; remaining useful life; feature construction; multiple filters; data-driven prediction; machine learning; overfitting; principal component analysis prognostics; remaining useful life; feature construction; multiple filters; data-driven prediction; machine learning; overfitting; principal component analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Trinh, H.-C.; Kwon, Y.-K. An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction. Machines 2018, 6, 35.

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