TY - JOUR
T1 - Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics
T2 - The case of turbofan engines
AU - Mitici, Mihaela
AU - de Pater, Ingeborg
AU - Barros, Anne
AU - Zeng, Zhiguo
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Aircraft
KW - C-MAPSS turbofan engines
KW - Maintenance scheduling
KW - Predictive maintenance planning
KW - Probabilistic remaining useful life prognostics
UR - http://www.scopus.com/inward/record.url?scp=85149406585&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2023.109199
DO - 10.1016/j.ress.2023.109199
M3 - Article
AN - SCOPUS:85149406585
SN - 0951-8320
VL - 234
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 109199
ER -