Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
This work addresses machinery fault detection for industrial settings by aligning RL's sequential reasoning with temporal data, offering a data-driven diagnostic approach.
The paper tackles machinery fault detection by formulating it as an offline inverse reinforcement learning problem, learning reward dynamics from healthy sequences without manual labels, and achieves early and robust fault detection with low anomaly scores for normal samples and high scores for faulty ones on three benchmark datasets.
Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement Learning to train a discriminator that distinguishes between normal (expert) and policy-generated transitions. The discriminator's learned reward serves as an anomaly score, indicating deviations from normal operating behaviour. When evaluated on three run-to-failure benchmark datasets (HUMS2023, IMS, and XJTU-SY), the model consistently assigns low anomaly scores to normal samples and high scores to faulty ones, enabling early and robust fault detection. By aligning RL's sequential reasoning with MFD's temporal structure, this work opens a path toward RL-based diagnostics in data-driven industrial settings.