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Article

A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

Dynamics and Mechatronics, Faculty of Mechanical Engineering, Paderborn University, 33098 Paderborn, Germany
Academic Editor: Antonio J. Marques Cardoso
Machines 2021, 9(10), 210; https://doi.org/10.3390/machines9100210
Received: 31 August 2021 / Revised: 13 September 2021 / Accepted: 18 September 2021 / Published: 24 September 2021
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case. View Full-Text
Keywords: prognostics; RUL predictions; particle filter; uncertainty consideration; Multi-Model-Particle Filter; model-based approach; rubber-metal-elements; predictive maintenance prognostics; RUL predictions; particle filter; uncertainty consideration; Multi-Model-Particle Filter; model-based approach; rubber-metal-elements; predictive maintenance
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MDPI and ACS Style

Bender, A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. Machines 2021, 9, 210. https://doi.org/10.3390/machines9100210

AMA Style

Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. Machines. 2021; 9(10):210. https://doi.org/10.3390/machines9100210

Chicago/Turabian Style

Bender, Amelie. 2021. "A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions" Machines 9, no. 10: 210. https://doi.org/10.3390/machines9100210

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