The scope of data-driven fault diagnosis models is greatly extended through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational efficiency and feature representation, while the latest Transformer architecture based on attention mechanism has not yet been applied in this field. To solve these problems, we propose a novel time-frequency Transformer (TFT) model inspired by the massive success of vanilla Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract effective abstractions from the time-frequency representation (TFR) of vibration signals. On this basis, a new end-to-end fault diagnosis framework based on time-frequency Transformer is presented in this paper. Through the case studies on bearing experimental datasets, we construct the optimal Transformer structure and verify its fault diagnosis performance. The superiority of the proposed method is demonstrated in comparison with the benchmark models and other state-of-the-art methods.
Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based …
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Guangdong Mechanical and Electrical College（广东机电职业技术学院）；School of Artificial Intelligence, Shenzhen Polytechnic University（深圳职业技术大学人工智能学院）；Department of Computer Science, University of Macau（澳门大学计算机科学系）
The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault diagnosis of rolling bearings pertain to the accuracy and real-time requirements. Most existing methods focus on ensuring the accuracy, and the real-time requirement is often neglected. In this paper, considering both requirements, we propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping, named logistic-ELM. First, we identify 1…
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）
Shanghai University（上海大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. Aiming at the common faults of rolling bearings, we propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN). Using raw bearing vibration signals as the inputs, MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults. Ball mixing is a ball bearing production quality problem that is difficult to identif…
Nanyang Technological University（南洋理工大学）；University of California, Davis（加州大学戴维斯分校）
Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretati…
Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
Guangdong University of Petrochemical Technology（广东石油化工学院）；Guangdong University of Technology（广东工业大学）；Guangdong Provincial Key Laboratory of Petrochemical Equipment and Fault Diagnosis（广东省石化装备故障诊断重点实验室）
An Unsupervised Framework for Dynamic Health Indicator Construction and Its Application in Rolling Bearing Prognostics
School of Data Science（数据科学学院）；Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong（香港数据科学研究所，香港城市大学，香港）；College of Mechanical（机械学院）；Electrical Engineering, Harbin Engineering University, Harbin 150001, China（电气工程学院，哈尔滨工程大学，哈尔滨150001，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）
Although data-driven fault diagnosis methods have been widely applied, massive labeled data are required for model training. However, a difficulty of implementing this in real industries hinders the application of these methods. Hence, an effective diagnostic approach that can work well in such situation is urgently needed.In this study, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method, which combines semi-supervised learning with improved deep embedded clustering (IDEC), is proposed to jointly explore scarce labeled data and massive unlabeled data. In the fir…
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）
Izmir University of Economics（伊兹密尔经济大学）；Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；Iowa State University（爱荷华州立大学）
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of them addressed bearing fault severity classification for early fault diagnosis with high enough accuracy. 1D Convolutional Neural Networks (CNNs) have indeed achieved good performance for detecting RM bearing faults from raw vibration and current sig…
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）
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data
Faculty of Electrical and Computer Engineering, Tarbiat Modares University（塔里克·莫达雷斯大学电气与计算机工程学院）；Faculty of Technology and Engineering, University of Mazandaran（马赞德兰大学技术与工程学院）
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）
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）
Integrating Frequency Guidance into Multi-source Domain Generalization for Bearing Fault Diagnosis
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China（教育部多媒体可信感知与高效计算重点实验室）；Xiamen University（厦门大学）；School of Informatics（信息学院）；Institute of Artificial Intelligence（人工智能研究所）
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）
Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operatio…
The emergence of large scaled sensor networks facilitates the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or un-formulated. In this paper, we have developed an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of in the signal space. Learning in the model space is implemented by fitting a series of models using …
BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Liaoning Liaohe Laboratory（辽宁辽河实验室）；Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province（辽宁省智能检测与装备技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
