TY - GEN
T1 - A Reliability Evaluation and Optimization Policy for Two-phase Industrial Plants Considering Time-varying Stochastic Environmental Effect
AU - Wei, Fanping
AU - Ma, Xiaobing
AU - Chen, Yi
AU - Qiu, Qingan
AU - Yang, Li
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Environmental effects are key factors that affect the degradation accumulation over the lifetime of diverse industrial plants, especially for plants exposed to harsh environment. To evaluate the reliability of system and perform preventive in time, an assessment and decision-making policy considering plants exhibiting two-phase environmental degradation characteristics is proposed. The cumulative impact of environmental effects on degradation behavior is quantified through the establishment of a time-varying stochastic environment model. A two-stage nonlinear stochastic process with environmental covariates is to capture the long-term degradation evolution. Through detection, the accumulation of real-time degradation is found, and the occurrence of hidden defects can be sensed, and then replacement decisions are made according to a pre-determined threshold. Through the joint optimization of inspection intervals and threshold limits, the long-term operational cost is optimized. A numerical experiment is conducted to validate the advantages of the policy, particularly in terms of downtime and cost reduction.
AB - Environmental effects are key factors that affect the degradation accumulation over the lifetime of diverse industrial plants, especially for plants exposed to harsh environment. To evaluate the reliability of system and perform preventive in time, an assessment and decision-making policy considering plants exhibiting two-phase environmental degradation characteristics is proposed. The cumulative impact of environmental effects on degradation behavior is quantified through the establishment of a time-varying stochastic environment model. A two-stage nonlinear stochastic process with environmental covariates is to capture the long-term degradation evolution. Through detection, the accumulation of real-time degradation is found, and the occurrence of hidden defects can be sensed, and then replacement decisions are made according to a pre-determined threshold. Through the joint optimization of inspection intervals and threshold limits, the long-term operational cost is optimized. A numerical experiment is conducted to validate the advantages of the policy, particularly in terms of downtime and cost reduction.
KW - cost management
KW - environmental effect
KW - inspection planning
KW - maintenance management
KW - multi-phase deterioration
KW - reliability evaluation
KW - replacement decision-making
UR - http://www.scopus.com/inward/record.url?scp=85215265195&partnerID=8YFLogxK
U2 - 10.1109/SRSE63568.2024.10772499
DO - 10.1109/SRSE63568.2024.10772499
M3 - Conference contribution
AN - SCOPUS:85215265195
T3 - 2024 6th International Conference on System Reliability and Safety Engineering, SRSE 2024
SP - 414
EP - 419
BT - 2024 6th International Conference on System Reliability and Safety Engineering, SRSE 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on System Reliability and Safety Engineering, SRSE 2024
Y2 - 11 October 2024 through 14 October 2024
ER -