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Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines
The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with...
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Published in: | Reliability engineering & system safety 2023-06, Vol.234, p.109199, Article 109199 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability.
•Predictive maintenance integrating probabilistic data-driven RUL prognostics.•Reliable probabilistic RUL prognostics using CNNs with Monte Carlo dropout.•Stochastic reward processes for maintenance planning.•Single and Multi-component predictive maintenance planning.•Maintenance planning using RUL prognostics for aircraft turbofan engines. |
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ISSN: | 0951-8320 1879-0836 |
DOI: | 10.1016/j.ress.2023.109199 |