Rolling bearings are subject to various faults due to its long-time operation under harsh environment, which will lead to unexpected breakdown of machinery system and cause severe accidents. Deep learning methods recently have gained growing interests and extensively applied in the data-driven bearing fault diagnosis. However, current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification, which overlook the uncertainties that inevitably exist in actual practice. To tackle this issue, in this research we develop a probabilistic fault diagnosis framework that can account for the uncertainty effect in prediction, which bears practical significance. This framework fully leverages the probabilistic feature of Gaussian process classifier (GPC). To facilitate the establishment of high-fidelity GPC, the tailored feature extraction with dimensionality reduction method can be optimally determined through the cross validation-based grid search upon a prespecified method pool consisting of various kernel principal component analysis (KPCA) methods and stacked autoencoder. This strategy can ensure the complex nonlinear relations between the features and faults to be adequately characterized. Furthermore, the sensor fusion concept is adopted to enhance the diagnosis performance. As compared with the traditional deep learning methods, this proposed framework usually requires less labeled data and less effort for parameter tuning. Systematic case studies using the publicly accessible experimental rolling bearing dataset are carried out to validate this new framework. Various influencing factors on fault diagnosis performance also are thoroughly investigated.
An Advanced Convolutional Neural Network for Bearing Fault Diagnosis under Limited Data
Nanjing University of Science and Technology（南京理工大学）；Tiangong University（天津大学）；Xidian University（西安电子科技大学）；Tianjin University（天津大学）；Victoria University（维多利亚大学）；La Trobe University（拉筹伯大学）
College of Computer and Information Science, Southwest University, Chongqing, China（计算机与信息科学学院，西南大学，重庆，中国）；College of Vehicle Engineering, Chongqing Industry and Trade Polytechnic, Chongqing, China（车辆工程学院，重庆工业贸易职业技术学院，重庆，中国）
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Analog Devices, Inc.（亚德诺公司）；The Chinese University of Hong Kong（香港中文大学）
Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
Artificial Intelligence Research Institute, China University of Mining and Technology（中国矿业大学人工智能研究院）；School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）；National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology（中国矿业大学选煤国家工程研究中心）
Fusion of Quadratic Time-Frequency Analysis and Convolutional Neural Networks to Diagnose Bearing Faults Under Time-Varying Speeds
Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通信科学学院，塔尔皮莱大学）；Faculty of Medicine and the Department of Clinical Neurophysiology at the University of Helsinki and Helsinki University Hospital（医学学院及赫尔辛基大学临床神经生理学系，赫尔辛基大学医院）；Department of Electrical Engineering, Qatar University（电气工程系，卡塔尔大学）
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
AIWARE Limited Company（AIWARE有限公司）；AIT Austrian Institute of Technology GmbH（奥地利AIT技术研究院）；FPT University（FPT大学）；The University of Danang - University of Science and Technology（岘港大学-科技大学）
Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis
Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis（广东省石化设备故障诊断重点实验室）；School of Computer, Guangdong University of Petrochemical Technology（广东石油化工学院计算机学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Deep learning-based fault identification in condition monitoring
Western Norway University of Applied Sciences（西挪威应用科技大学）；Nepal Telecommunications Authority（尼泊尔电信管理局）；Institute for Geoinformatics, University of Münster（明斯特大学地理信息学研究所）
Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
Department of Electrical and Electronic Engineering, Federal University of Santa Catarina（电气与电子工程系，圣卡塔琳娜联邦大学）；Department of Mechanical Engineering, Federal University of Santa Catarina（机械工程系，圣卡塔琳娜联邦大学）
Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
Department of Computing Sciences, Tampere University（计算科学系，塔尔皮奥大学）；Department of Physiology, University of Helsinki（生理学系，赫尔辛基大学）；Department of Electrical Engineering, Qatar University（电气工程系，卡塔尔大学）
An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
Center for Risk and Reliability, University of Maryland, College Park（马里兰大学帕克分校风险与可靠性中心）；Department of Civil and Environmental Engineering, University of California, Los Angeles（加利福尼亚大学洛杉矶分校土木与环境工程系）；Garrick Institute for the Risk Sciences, University of California, Los Angeles（加利福尼亚大学洛杉矶分校加里克风险科学研究所）；Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University（香港理工大学工业与系统工程系）；Centre for Advances in Reliability and Safety Limited (CAiRS)（可靠性及安全进展中心有限公司）
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Zhejiang University（浙江大学）；University of Illinois Urbana–Champaign Institute（伊利诺伊大学厄巴纳-香槟分校学院）；Southeast University（东南大学）；Huazhong University of Science and Technology（华中科技大学）
