159 citations · 194 across the 29 of their papers we have counts for
4 papers · 1 filter
Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions. This misses coupling faults,…
Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings. While reinforcement learni…
Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the target network fixed, improv…
Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek +1
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 deci…