Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
arXiv:2602.22297v1 Announce Type: new Abstract: 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 n
arXiv:2602.22297v1 Announce Type: new Abstract: 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.
Executive Summary
This article presents a novel approach to machinery fault detection (MFD) using adversarial inverse reinforcement learning (AIRL). The authors formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences. The proposed framework, AIRL-MFD, leverages a discriminator to learn an anomaly score, indicating deviations from normal operating behavior. When evaluated on three benchmark datasets, the model demonstrates early and robust fault detection. This work contributes to the development of reinforcement learning (RL)-based diagnostics in data-driven industrial settings, aligning RL's sequential reasoning with MFD's temporal structure. The authors' approach bypasses the need for manual reward engineering and fault labels, offering a more efficient and effective solution to MFD.
Key Points
- ▸ Formulates MFD as an offline inverse reinforcement learning problem
- ▸ Uses AIRL to learn a reward function from healthy operational sequences
- ▸ Demonstrates early and robust fault detection on three benchmark datasets
Merits
Strength in avoiding manual reward engineering
The authors' approach bypasses the need for manual reward engineering, which can be a time-consuming and challenging task in MFD.
Demerits
Limitation in handling complex fault scenarios
The proposed framework may struggle to handle complex fault scenarios where anomalies are not well-represented in the training data.
Assumes access to healthy operational sequences
The authors' approach assumes access to healthy operational sequences, which may not be available in all industrial settings.
Expert Commentary
The proposed framework, AIRL-MFD, is a significant contribution to the field of MFD, leveraging the strengths of RL and AIRL to develop an efficient and effective solution. However, the authors' approach assumes access to healthy operational sequences, which may not be available in all industrial settings. Additionally, the proposed framework may struggle to handle complex fault scenarios where anomalies are not well-represented in the training data. Nevertheless, the authors' work opens a path toward RL-based diagnostics in data-driven industrial settings, aligning RL's sequential reasoning with MFD's temporal structure.
Recommendations
- ✓ Further research is needed to explore the application of AIRL-MFD in industrial settings where healthy operational sequences are not available.
- ✓ Extensions to the proposed framework to handle complex fault scenarios should be explored, potentially through the incorporation of additional data sources or more advanced learning techniques.
