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Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-0051-0954
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3272-4145
2020 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 10, no 1, article id 69Article in journal (Refereed) Published
Abstract [en]

Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 

Place, publisher, year, edition, pages
Basel: MDPI, 2020. Vol. 10, no 1, article id 69
Keywords [en]
predictive maintenance, remaining useful life, recurrent neural networks, LSTM, Stacked Ensemble
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-41314DOI: 10.3390/app10010069OAI: oai:DiVA.org:hh-41314DiVA, id: diva2:1381884
Projects
HEALTH-VINNOVA
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VinnovaAvailable from: 2019-12-29 Created: 2019-12-29 Last updated: 2020-01-21Bibliographically approved

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Sheikholharam Mashhadi, PeymanNowaczyk, SławomirPashami, Sepideh
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