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Open AccessArticle

Mechanical Reliability Assessment by Ensemble Learning

1
LTDS, Ecole Centrale de Lyon, 69134 Écully, France
2
LIRIS, Ecole Centrale de Lyon, 69134 Écully, France
3
ICJ, Ecole Centrale de Lyon, 69134 Écully, France
*
Author to whom correspondence should be addressed.
Vehicles 2020, 2(1), 126-141; https://doi.org/10.3390/vehicles2010007
Received: 31 December 2019 / Revised: 8 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Future Powertrain Technologies)
Reliability assessment plays a significant role in mechanical design and improvement processes. Uncertainties in structural properties as well as those in the stochatic excitations have made reliability analysis more difficult to apply. In fact, reliability evaluations involve estimations of the so-called conditional failure probability (CFP) that can be seen as a regression problem taking the structural uncertainties as input and the CFPs as output. As powerful ensemble learning methods in a machine learning (ML) domain, random forest (RF), and its variants Gradient boosting (GB), Extra-trees (ETs) always show good performance in handling non-parametric regressions. However, no systematic studies of such methods in mechanical reliability are found in the current published research. Another more complex ensemble method, i.e., Stacking (Stacked Generalization), tries to build the regression model hierarchically, resulting in a meta-learner induced from various base learners. This research aims to build a framework that integrates ensemble learning theories in mechanical reliability estimations and explore their performances on different complexities of structures. In numerical simulations, the proposed methods are tested based on different ensemble models and their performances are compared and analyzed from different perspectives. The simulation results show that, with much less analysis of structural samples, the ensemble learning methods achieve highly comparable estimations with those by direct Monte Carlo simulation (MCS). View Full-Text
Keywords: structural reliability; uncertainties; ensemble learning structural reliability; uncertainties; ensemble learning
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MDPI and ACS Style

You, W.; Saidi, A.; Zine, A.-m.; Ichchou, M. Mechanical Reliability Assessment by Ensemble Learning. Vehicles 2020, 2, 126-141. https://doi.org/10.3390/vehicles2010007

AMA Style

You W, Saidi A, Zine A-m, Ichchou M. Mechanical Reliability Assessment by Ensemble Learning. Vehicles. 2020; 2(1):126-141. https://doi.org/10.3390/vehicles2010007

Chicago/Turabian Style

You, Weizhen; Saidi, Alexandre; Zine, Abdel-malek; Ichchou, Mohamed. 2020. "Mechanical Reliability Assessment by Ensemble Learning" Vehicles 2, no. 1: 126-141. https://doi.org/10.3390/vehicles2010007

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