Condition-based maintenance for intelligent monitored series system with independent machine failure modes

Maintenance scheduling, as a decision-making process, plays an important role for today's manufacturing systems. This paper proposes a condition-based maintenance policy for intelligent monitored multi-unit series systems. A novel method of multi-level scheduling is developed to predict maintenance requirements according to machine degradation and maintenance opportunity. The hazard rates of each machine are assumed to be available either from historical reliability information or online state prediction. A global-objective model is used to make the machine-level decision for availability-effective and cost-effective maintenance intervals. Furthermore, a maintenance time window method is developed to decrease system total maintenance cost. This system-level schedule is determined by utilising opportunities of maintenance combination throughout the mission lifetime. The case study shows that the application of the proposed policy results in a noticeable cost saving.