Fault Diagnosis Method

Fault diagnosis methods aim to automatically identify and locate malfunctions in complex systems, improving safety, efficiency, and maintenance. Current research emphasizes addressing data scarcity and noisy environments through techniques like generative adversarial networks, multi-scale convolutional neural networks, and graph neural networks, often incorporating attention mechanisms and self-supervised learning to enhance performance. These advancements are significantly impacting various industries, enabling predictive maintenance and improving the reliability of equipment ranging from rolling bearings and electrical motors to photovoltaic arrays and power electronics converters.

Papers

November 11, 2024

Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm
Jiayan Fang, Siwei Li, Yichun Wu
Practical Algorithm Deep Convolutional Neural Network DH Research Temporal Convolutional Network Fault Diagnosis Nuclear Reactor Fault Diagnosis Method Intelligent Fault Diagnosis Sparrow Search Algorithm

May 30, 2024

Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry
Mengjie Gan, Penglong Lian, Zhiheng Su, Jiyang Zhang, Jialong Huang, Benhao Wang, Jianxiao Zou, Shicai Fan
Industry Assignment Fault Diagnosis Multi Scale Graph Fault Diagnosis Method Zero Shot Industrial Fault Diagnosis

March 21, 2024

Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model
Maoxuan Zhou, Wei Kang, Kun He
Multi Scale Rolling Bearing Fault Diagnosis Method Bearing Fault

November 27, 2023

Active Foundational Models for Fault Diagnosis of Electrical Motors
Sriram Anbalagan, Sai Shashank GP, Deepesh Agarwal, Balasubramaniam Natarajan, Babji Srinivasan
Active Learning Fault Detection Fault Diagnosis Method Machinery Fault Electrical Motor

October 20, 2023

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems
Jihoon Chung, Zhenyu Kong
Medical Diagnosis Robotic Assembly Fault Diagnosis Post Fault Trajectory Variational Bayesian Inference Fault Diagnosis Method Sparse Bayesian Learning

August 29, 2023

Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Jiang Liu, Wei Dai
Domain Adaptation Rolling Bearing Fault Diagnosis Method Bearing Fault

August 16, 2023

An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns
Tae Yeob Kang, Haebom Lee, Sungho Suh
Empirical Study Fault Detection Root Cause Fault Diagnosis Method Pattern Filter

July 4, 2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
Baorui Dai, Gaëtan Frusque, Tianfu Li, Qi Li, Olga Fink
Unsupervised Domain Adaptation Adversarial Learning Industrial Disturbing Noise Domain Adversarial Domain Specific Task Fault Diagnosis Method Wavelet Packet Transform

March 24, 2023

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules
Jiaqi Qu, Lu Wei, Qiang Sun, Hamidreza Zareipour, Zheng Qian
Convolutional Neural Network Convolutional Block Attention Fault Diagnosis Fault Diagnosis Method Dust Storm

November 1, 2022

Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features
Kou Lei, Liu Chuang, Cai Guo-Wei, Zhang Zhe, Zhou Jia-Ning, Wang Xue-Mei
Neural Network Fault Diagnosis Method Synthetic Feature Transient Fault

October 27, 2022

Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression
Lei Kou, Chuang Liu, Guowei Cai, Zhe Zhang
Neural Network Fault Diagnosis Method Power Electronics Converter Wavelet Compression

October 3, 2022

Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features
Saif S. S. Al-Wahaibi, Qiugang Lu
Convolutional Neural Network Global Feature Global Convolution Fault Diagnosis Method

August 30, 2022

System Resilience through Health Monitoring and Reconfiguration
Ion Matei, Wiktor Piotrowski, Alexandre Perez, Johan de Kleer, Jorge Tierno, Wendy Mungovan, Vance Turnewitsch
Health Monitoring Fault Diagnosis Prognostic Model Fault Diagnosis Method Model Based Diagnosis System Resilience

April 23, 2022

Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings
Zhenhua Tan, Jingyu Ning, Kai Peng, Zhenche Xia, Danke Wu
Extreme Learning Machine Chaotic Dynamic Rolling Bearing Fault Classification Fault Diagnosis Method

December 11, 2021

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry
Karl Löwenmark, Cees Taal, Stephan Schnabel, Marcus Liwicki, Fredrik Sandin
Language Supervision Text Supervision Fault Diagnosis Method Intelligent Fault Diagnosis Natural Language Annotation

November 16, 2021

Graph neural network-based fault diagnosis: a review
Zhiwen Chen, Jiamin Xu, Cesare Alippi, Steven X. Ding, Yuri Shardt, Tao Peng, Chunhua Yang
Convolutional Neural Network Graph Drawing Narrative Review Graph Attention Network Fault Diagnosis Method

Fault Diagnosis Method

Papers

Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm

Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry

Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model

Active Foundational Models for Fault Diagnosis of Electrical Motors

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems

Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation

An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features

Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression

Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features

System Resilience through Health Monitoring and Reconfiguration

Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

Graph neural network-based fault diagnosis: a review