Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection

Researchers propose a new approach using Adversarial Inverse Reinforcement Learning for machinery fault detection that learns from healthy operational data without requiring manual fault labels. The framework treats fault detection as a sequential decision-making problem and demonstrates effective early fault detection on three benchmark datasets.