Bearing Fault

Bearing fault diagnosis aims to accurately and efficiently detect defects in rolling bearings to prevent catastrophic equipment failures. Current research heavily emphasizes the use of deep learning models, including convolutional neural networks (CNNs), graph neural networks (GNNs), and hybrid architectures combining CNNs with multilayer perceptrons (MLPs), often enhanced by techniques like attention mechanisms and knowledge distillation to improve accuracy and efficiency. These advancements are crucial for improving industrial safety and reliability, reducing maintenance costs, and optimizing operational efficiency across various sectors.

Papers

October 15, 2024

Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM
Moirangthem Tiken Singh, Rabinder Kumar Prasad, Gurumayum Robert Michael, N. Hemarjit Singh, N. K. Kaphungkui
Long Short Term Memory Graph Attention Network Fault Detection Bearing Fault

August 12, 2024

Bearing Fault Diagnosis using Graph Sampling and Aggregation Network
Jiaying Chen, Xusheng Du, Yurong Qian, Gwanggil Jeon
Fault Diagnosis Aggregation Network Graph Sampling Bearing Fault

June 10, 2024

DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuning
Ovanes Petrosian, Li Pengyi, He Yulong, Liu Jiarui, Sun Zhaoruikun, Fu Guofeng, Meng Liping
Knowledge Distillation Fault Detection Fault Diagnosis Rolling Bearing Bearing Fault

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

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

June 1, 2022

Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Jing-Xiao Liao, Hang-Cheng Dong, Zhi-Qi Sun, Jinwei Sun, Shiping Zhang, Feng-Lei Fan
Deep Learning Quadratic Neuron Quadratic Attention Bearing Fault

April 24, 2022

Graph Neural Network-based Early Bearing Fault Detection
Xusheng Du, Jiong Yu
Gene Level GNN Graph Neural Vibration Signal Non Euclidean Bearing Fault Euclidean Data

December 16, 2021

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model
V. Sinitsin, O. Ibryaeva, V. Sakovskaya, V. Eremeeva
Deep Learning Efficient Hybrid Accelerometer Signal Rolling Bearing Bearing Fault

Bearing Fault

Papers

Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM

Bearing Fault Diagnosis using Graph Sampling and Aggregation Network

DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuning

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

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

Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis

Graph Neural Network-based Early Bearing Fault Detection

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model