Fault Detection

Fault detection research aims to automatically identify anomalies or malfunctions in diverse systems, from power grids and industrial machinery to satellite constellations and even large language models. Current efforts heavily utilize machine learning, employing various architectures like neural networks (including recurrent and Bayesian variants), autoencoders, and diffusion models, often coupled with techniques like attention mechanisms and knowledge distillation to improve accuracy and interpretability. This field is crucial for enhancing safety, reliability, and efficiency across numerous industries through predictive maintenance, improved diagnostics, and more robust system operation.

Papers

February 9, 2022

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
Masoud Jalayer, Amin Kaboli, Carlotta Orsenigo, Carlo Vercellis
Long Short Term Memory Fault Detection Noisy Data Extreme Learning Machine Group Imbalance Industrial Machine Hybrid Framework

January 20, 2022

Transfer Learning for Fault Diagnosis of Transmission Lines
Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, Mengchu Zhou
Transfer Learning Fault Detection Fault Diagnosis Power Line

December 25, 2021

Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems
Syed Wali, Irfan Khan
Machine Learning Person Identification Fault Detection Fault Diagnosis Post Fault Trajectory Statistical Signature Interpretable Fault

December 8, 2021

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization
Zelin Ren, Xuebing Yang, Yuchen Jiang, Wensheng Zhang
Dimensionality Reduction Principal Component Analysis Fault Detection Autoencoder Based PCa Detection Kernel Principal Component Analysis

December 6, 2021

Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
Lukas Bommes, Mathis Hoffmann, Claudia Buerhop-Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Andreas Maier, Ian Marius Peters
Anomaly Detection Fault Detection Defect Detection Supervised Contrastive Learning Photovoltaic Module

December 5, 2021

Using Convolutional Neural Networks for fault analysis and alleviation in accelerator systems
Jashanpreet Singh Sraw, Deepak M C
Convolutional Neural Network Real Time Fault Detection Semiconductor Manufacturing AID User System Reliability Accelerator Tuning

November 29, 2021

A Natural Language Processing and Deep Learning based Model for Automated Vehicle Diagnostics using Free-Text Customer Service Reports
Ali Khodadadi, Soroush Ghandiparsi, Chen-Nee Chuah
Deep Learning Natural Language Processing Full Model Fault Detection Data Driven Approach Vehicle Level

Fault Detection

Papers

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Transfer Learning for Fault Diagnosis of Transmission Lines

Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning

Using Convolutional Neural Networks for fault analysis and alleviation in accelerator systems

A Natural Language Processing and Deep Learning based Model for Automated Vehicle Diagnostics using Free-Text Customer Service Reports