Noussis Alexandros, O'Neil Ryan, Saif Ahmed, Khatab Abdelhakim, Diallo Claver
Department of Industrial Engineering, Dalhousie University, Halifax, NS Canada.
Laboratory of Computer Engineering (LGIPM), Lorraine University, Metz, France.
Auton Intell Syst. 2025;5(1):13. doi: 10.1007/s43684-025-00099-9. Epub 2025 Jun 6.
Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency. However, classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity. The advent of Industry 4.0 has increased the use of sensors for monitoring systems, while deep learning (DL) models have allowed for accurate system health predictions, enabling data-driven maintenance planning. Most intelligent maintenance literature has used DL models solely for remaining useful life (RUL) point predictions, and a substantial gap exists in further using predictions to inform maintenance plan optimization. The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models. Hence, this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem (SMP). The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system. Numerical experiments compare the framework's performance against prior SMP methods and highlight its strengths. When minimizing cost, maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs. The proposed method is usable in large-scale, complex scenarios and various industrial contexts. The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.
依赖于在资源受限情况下可靠资产运行的现代工业采用智能维护方法来实现效率最大化。然而，传统维护方法依赖于假定的寿命分布，存在估计误差和计算复杂性问题。工业4.0的出现增加了用于监测系统的传感器的使用，而深度学习（DL）模型则能够进行准确的系统健康预测，从而实现数据驱动的维护计划。大多数智能维护文献仅将DL模型用于剩余使用寿命（RUL）点预测，在进一步利用预测来优化维护计划方面存在很大差距。少数试图弥合这一差距的现有研究存在使用简单系统配置和不可扩展模型的问题。因此，本文开发了一种使用蒙特卡洛随机失活的混合DL模型来生成RUL预测，这些预测用于构建用于优化选择性维护问题（SMP）的经验系统可靠性函数。所提出的框架用于为面向任务的串联k/n:G系统规划维护。数值实验将该框架的性能与先前的SMP方法进行了比较，并突出了其优势。在最小化成本时，经常会制定出能确保任务成功完成同时避免不必要维修的维护计划。所提出的方法可用于大规模、复杂场景和各种工业环境。该方法能够找到精确解，同时避免了对计算密集型参数可靠性函数的需求。
