159 citations · 162 across the 6 of their papers we have counts for
6 papers
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…
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…
Data-driven Machinery Fault Diagnosis: A Comprehensive Review
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the mas…
HUMS2023 Data Challenge Result Submission
Dhiraj Neupane, Lakpa Dorje Tamang, Ngoc Dung Huynh +2
We implemented a simple method for early detection in this research. The implemented methods are plotting the given mat files and analyzing scalogram images generated by performing…
SHINE: Deep Learning-Based Accessible Parking Management System
Dhiraj Neupane, Aashish Bhattarai, Sunil Aryal +3
The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwi…