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.

• →New AI framework uses reinforcement learning to detect machinery faults by learning from normal operational sequences rather than fault examples.
• →Adversarial Inverse Reinforcement Learning approach eliminates the need for manual reward engineering and fault labeling.
• →Method successfully tested on three run-to-failure benchmark datasets showing robust early fault detection capabilities.
• →Framework assigns low anomaly scores to normal operations and high scores to faulty conditions.
• →Approach leverages RL's sequential reasoning capabilities to better align with the temporal nature of machinery fault detection.