TY - GEN
T1 - Predictive Aircraft Maintenance
T2 - 31st European Safety and Reliability Conference
AU - Lee, J.
AU - Mitici, M.A.
N1 - Accepted Author Manuscript
PY - 2021
Y1 - 2021
N2 - Predictive aircraft maintenance is a complex process, which requires the modeling of the stochastic degradation of aircraft systems, as well as the dynamic interactions between the stakeholders involved. In this paper, we show that the stochastically and dynamically colored Petri nets (SDCPNs) are able to formalize the predictive aircraft maintenance process. We model the aircraft maintenance stakeholders and their interactions using local SDCPNs. The degradation of the aircraft systems is also modeled using local SDCPNs where tokens change their colors according to a stochastic process. These SDCPN models are integrated into a unifying SDCPN model of the entire aircraft maintenance process. We illustrate our approach for the maintenance of multi-component systems with k-out-of-n redundancy. Using SDCPNs and Monte Carlo simulation, we analyze the number of maintenance tasks and potential degradation incidents that the system is expected to undergo when using a remaining useful life(RUL)-based predictive maintenance strategy. We compare the performance of this predictive maintenance strategy against other maintenance strategies that rely on fixed-interval inspection tasks to schedule component replacements. The results show that by conducting RUL-based predictive maintenance, the number of unscheduled maintenance tasks and degradation incidents is significantly reduced.
AB - Predictive aircraft maintenance is a complex process, which requires the modeling of the stochastic degradation of aircraft systems, as well as the dynamic interactions between the stakeholders involved. In this paper, we show that the stochastically and dynamically colored Petri nets (SDCPNs) are able to formalize the predictive aircraft maintenance process. We model the aircraft maintenance stakeholders and their interactions using local SDCPNs. The degradation of the aircraft systems is also modeled using local SDCPNs where tokens change their colors according to a stochastic process. These SDCPN models are integrated into a unifying SDCPN model of the entire aircraft maintenance process. We illustrate our approach for the maintenance of multi-component systems with k-out-of-n redundancy. Using SDCPNs and Monte Carlo simulation, we analyze the number of maintenance tasks and potential degradation incidents that the system is expected to undergo when using a remaining useful life(RUL)-based predictive maintenance strategy. We compare the performance of this predictive maintenance strategy against other maintenance strategies that rely on fixed-interval inspection tasks to schedule component replacements. The results show that by conducting RUL-based predictive maintenance, the number of unscheduled maintenance tasks and degradation incidents is significantly reduced.
KW - Aircraft maintenance
KW - Predictive maintenance
KW - Stochastic Petri nets
KW - Reliability
KW - Modeling
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85135438105&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-2016-8_050-cd
DO - 10.3850/978-981-18-2016-8_050-cd
M3 - Conference contribution
SN - 9789811820168
T3 - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
SP - 146
EP - 153
BT - Proceedings of the 31st European Safety and Reliability Conference
A2 - Castanier, Bruno
A2 - Cepin, Marko
A2 - Bigaud, David
A2 - Berenguer, Christophe
PB - ESREL
Y2 - 19 September 2021 through 23 September 2021
ER -