You can learn optimal maintenance schedules from operational failure data using multi-armed bandit algorithms, achieving regret bounds that match theoretical limits and requiring only a small number of suboptimal decisions.
This paper develops data-driven algorithms to learn the optimal maintenance schedule for machines under block replacement policies, where machines are replaced individually upon failure and all machines are jointly replaced at fixed intervals.