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

August 24, 2022

Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii
Automatic Detection Fault Detection Post Fault Trajectory Smartphone Camera Non Contact Fault Prediction

August 19, 2022

Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Zhong Li, Matthijs van Leeuwen
Anomaly Detection Human Prediction Feature Selection Fault Detection Log Based Anomaly Detection Event Log Log Pattern

August 2, 2022

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
Giovanni Menegozzo, Diego Dall'Alba, Paolo Fiorini
Causal Relationship Fault Detection Manufacturing Process Data Driven Causal

July 27, 2022

Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network
Zhongzhi Li, Yunmei Zhao, Jinyi Ma, Jianliang Ai, Yiqun Dong
Deep Neural Network Classification Code Fault Detection Sensor Fault Flight Sensor

July 21, 2022

Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation
Oliver Mey, Deniz Neufeld
XAI Method Fault Detection Critical Review Use Case Vibration Data Low Cost Ambient Vibration

June 24, 2022

Vibration fault detection in wind turbines based on normal behaviour models without feature engineering
Stefan Jonas, Dimitrios Anagnostos, Bernhard Brodbeck, Angela Meyer
Fault Detection Convolutional Autoencoder Feature Engineering Wind Turbine Behavior Model Vibration Signal

May 25, 2022

A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains
Guodong Sun, Yang Zhou, Huilin Pan, Bo Wu, Ye Hu, Yang Zhang
Convolutional Neural Network Fault Detection Real Time Detection Non Maximum Suppression Vision Based System

May 13, 2022

Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data
Rony Shohet, Mohamed Kandil, Y. Wang, J. J. McArthur
Fault Detection Building Energy Rating

May 1, 2022

An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture
Wenbin Song, Di Wu, Weiming Shen, Benoit Boulet
Feature Fusion Fault Detection Deep Feature Denoising Autoencoder Industrial Machine Deep Learning Feature

April 29, 2022

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
Katharina Rombach, Dr. Gabriel Michau, Prof. Dr. Olga Fink
Domain Adaptation Fault Detection Controllable Generation Partial Domain Adaptation

April 27, 2022

Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection
Wenbin Song, Di Wu, Weiming Shen, Benoit Boulet
Anomaly Detection Meta Learning Fault Detection Shot Anomaly Detection Rolling Bearing High Diagram to Diagram Variability

April 21, 2022

April 17, 2022

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
Mohamed Baioumy, William Hartemink, Riccardo M. G. Ferrari, Nick Hawes
Bayesian Inference Fault Detection Sensory Input Fault Tolerant Control Model Degradation Accurate Learning

April 13, 2022

An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition
Chen Xiaona, Ahmad Tanvir, Ma Yinglong
Ensemble Learning Fault Detection Multi Label Text Classification Gradient Boosted Decision Tree Fault Classification

March 29, 2022

A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis
Guangwei Yu, Gang Li, Xingtong Si, Zhuoyuan Song
Fault Detection Adaptive Convolution Rolling Bearing Machinery Fault

March 19, 2022

Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals
Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
Convolutional Neural Network Fault Detection Event Detection Optical Network Optical Communication Short Fiber Fiber Fault Optical Time Domain Reflectometry

March 18, 2022

Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)
Ali Rohan
Transfer Learning Fault Detection Diagnosis System Domain Knowledge Transfer Imbalanced Domain Data Driven Prognostic Prognostic and Health Management Intelligent Fault Detection

March 2, 2022

Practical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data
Eduardo Abdon Sarquis Filho, Björn Müller, Nicolas Holland, Christian Reise, Klaus Kiefer, Bernd Kollosch, Paulo J. Costa Branco
Product Design Movie Recommendation Fault Detection Optical Experiment Real Data Photovoltaic System Rooftop Photovoltaic

February 23, 2022

ML-based Anomaly Detection in Optical Fiber Monitoring
Khouloud Abdelli, Joo Yeon Cho, Carsten Tropschug
Anomaly Detection Fault Detection Based Anomaly Detection Optical Network Fiber Fault

Fault Detection

Papers

Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes

Feature Selection for Fault Detection and Prediction based on Event Log Analysis

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods

Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network

Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation

Vibration fault detection in wind turbines based on normal behaviour models without feature engineering

A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains

Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data

An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection

A two-level machine learning framework for predictive maintenance: comparison of learning formulations

Resilient robot teams: a review integrating decentralised control, change-detection, and learning

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition

A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis

Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals

Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)

Practical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data

ML-based Anomaly Detection in Optical Fiber Monitoring