Adaptive automated scheduler in Prognostics Health Management

A mechanism for Automated Scheduler in maintenance management is explored in this paper where the duration of the maintenance tasks are not predefined, rather generated dynamically. An adaptive learning system is employed to determine the duration of a future task based on similar prior tasks. Durations of some maintenance tasks are calculated using statistical regression where nature and specification of the task are well defined. However, for other tasks where the durations are dependent upon the condition of several other parts and subsystems, they are derived through machine learning methodologies like Neural Network and Bayesian rule. Moreover, the duration of the tasks is further refined by Prognostics Health Management assessment that predicts impending failure based on near real time condition of vehicles and its subsystems. Determining the task durations dynamically based on prior knowledge or from prognostic data will make the schedule efficient, saving money and resources required for maintenance.