Remaining useful life prediction method for Degradation–Shock dependence: Case of a subsea hydraulic control system

• Published in: Ocean engineering 2024-05, Vol.299, p.117339, Article 117339
• Main Authors: Shao, Xiaoyan, Cai, Baoping, Li, Jinghao, Liu, Yiliu, Gao, Lei, Zou, Zhexian, Liu, Guijie
• Format: Article
• Language: English
• Subjects: Degradation stage; Dependency; Remaining useful life; Shocks; Uncertainty analysis
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.117339

Predicting the remaining useful life (RUL) is crucial for ensuring reliable equipment operation. This study addresses the challenges of RUL prediction in modeling the effects of external shocks and internal degradation, with emphasis on the dependency relationship between the two factors. A novel hybrid method for RUL prediction is proposed. The proposed method combines degradation models and field data to realize RUL assessment under the influence of system degradation and external shocks. A dependency factor is introduced to effectively incorporate the influence of degradation stage and shock intensity on system failure. A comprehensive uncertainty analysis is performed, in consideration of random degradation, unpredictable shock events, and the dynamic evolution of degradation stages and performance degradation processes. Numerical simulations are conducted on a hydraulic control system to demonstrate the effectiveness and accuracy of the proposed method. The robustness and reliability of the proposed approach in predicting the RUL of the hydraulic control system are demonstrated, with particular emphasis on the method's capability to handle the complexities of dependency modeling and uncertainty analysis. Results show that the proposed method can realize dependency modeling of the hydraulic control system, accurately predict its RUL, and analyze the vulnerability of its subsystems. •A novel degradation–shock dependency modeling method is proposed.•A more comprehensive uncertainty analysis model is developed.•A hybrid RUL prediction method that combines degradation data and models is constructed.