An Offline Deep Reinforcement Learning for Maintenance Decision-Making

Understand

Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.

• Having access to the remaining useful life estimation or likelihood of failure in near future helps operators to assess the operating conditions and, therefore, provides better opportunities for sound repair and maintenance decisions.
• However, many operators believe remaining useful life estimation and failure prediction solutions are incomplete answers to the maintenance challenge.
• They argue that knowing the likelihood of failure in the future is not enough to make maintenance decisions that minimize costs and keep the operators safe.