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）
Recent generalizable fault diagnosis researches have effectively tackled the distributional shift between unseen working conditions. Most of them mainly focus on learning domain-invariant representation through feature-level methods. However, the increasing numbers of unseen domains may lead to domain-invariant features contain instance-level spurious correlations, which impact the previous models' generalizable ability. To address the limitations, we propose the Fourier-based Augmentation Reconstruction Network, namely FARNet.The methods are motivated by the observation that the Fourier phase component and amplitude component preserve different semantic information of the signals, which can be employed in domain augmentation techniques. The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains. To construct a more robust generalized model, we employ a multi-source domain data augmentation strategy in the frequency domain. Specifically, a Frequency-Spatial Interaction Module (FSIM) is introduced to handle global information and local spatial features, promoting representation learning between the two sub-networks. To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization. Through extensive experiments on the CWRU and SJTU datasets, FARNet demonstrates effective performance and achieves superior results compared to current cross-domain approaches on the benchmarks.
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…
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…
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）
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）
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）
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…
Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited target bearing data are available to conduct EFD, which makes it hard to adapt to the EFD task of new bearings. To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings. However, most existing methods only consider the distribution drift across different working conditions but ignore the d…
Georgia Institute of Technology（佐治亚理工学院）；University of California, Berkeley（加州大学伯克利分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
The rapid development of artificial intelligence and deep learning has provided many opportunities to further enhance the safety, stability, and accuracy of industrial Cyber-Physical Systems (CPS). As indispensable components to many mission-critical CPS assets and equipment, mechanical bearings need to be monitored to identify any trace of abnormal conditions. Most of the data-driven approaches applied to bearing fault diagnosis up-to-date are trained using a large amount of fault data collected a priori. In many practical applications, however, it can be unsafe and time-consuming to collect …
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）
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）；University of Ottawa（渥太华大学）；Sant Longowal Institute of Engineering and Technology（桑特隆戈瓦尔工程技术学院）
University of Electronic Science and Technology of China（电子科技大学）
Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. This paper proposes a novel deep metric learning model, where imbalanced fault data and a quadruplet data pair design manner are considered. Based on such data pair, a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution are proposed. This quadruplet loss pays special attention to imbalanced sample pa…
Unsupervised domain adaptation (UDA) has shown remarkable results in bearing fault diagnosis under changing working conditions in recent years. However, most UDA methods do not consider the geometric structure of the data. Furthermore, the global domain adaptation technique is commonly applied, which ignores the relation between subdomains. This paper addresses mentioned challenges by presenting the novel deep subdomain adaptation graph convolution neural network (DSAGCN), which has two key characteristics: First, graph convolution neural network (GCNN) is employed to model the structure of da…
Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time sensing and detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made a fault detection, classification, and location estimation process more challenging. Transmission line datasets are stream data which are continuously collected by various sensors and hence, require generalized and fast fault diagnosis approaches. Newly collected datasets including voltages and currents might…
Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）
In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis. In condition-based monitoring,it is challenging to form a large-scale well-annotated datasetdue to the expense of data acquisition and costly annotation.The generated data have a large number of redundant featureswhich degraded the performance of the machine learning models.To overcome this, we have utilized the advantages of minimumredundancy maximum relevance (mRMR) and transfer learningwith a deep learning model. In this work,mRMRis combinedwith d…
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）
China University of Mining and Technology（中国矿业大学）；Xuzhou Medical University（徐州医科大学）
Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, this assumption does not hold, especially when the working condition varies. In this paper, a new adversarial adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN consists of four parts, namely, a source feature extractor, a target feature extractor, a label classifier and a domain discriminator. The layers …
Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning
Zhejiang University and the University of Illinois Urbana–Champaign Joint Institute（浙江大学与伊利诺伊大学厄巴纳-香槟分校联合研究院）；College of Computer Science and Technology in Zhejiang University（浙江大学计算机科学与技术学院）
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
Aerostructures Innovation Research Hub (AIRHub), Swinburne University of Technology（航空结构创新研究中心（AIRHub），斯威本科技大学）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威本科技大学科学、计算与工程技术学院）；Tongji University（同济大学）
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising…
Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems
Intelligent Transportation School, Zhejiang Institute of Communications, China（浙江交通学院智能交通学院）；Center for Applied Intelligent Systems Research, Halmstad University, Sweden（哈马碧大学应用智能系统研究中心）
National University of Singapore（新加坡国立大学）；Temasek Laboratories（淡马锡实验室）
Accurate diagnosis of propeller faults is crucial for ensuring the safe and efficient operation of quadrotors. Training a fault classifier using simulated data and deploying it on a real quadrotor is a cost-effective and safe approach. However, the simulation-to-reality gap often leads to poor performance of the classifier when applied in real flight. In this work, we propose a deep learning model that addresses this issue by utilizing newly identified features (NIF) as input and utilizing domain adaptation techniques to reduce the simulation-to-reality gap. In addition, we introduce an adjust…
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）
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…
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）
The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of …
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 …
Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset…
Qatar University（卡塔尔大学）；Tampere University（坦佩雷大学）；West Virginia University（西弗吉尼亚大学）
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entir…
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 …
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）
China Jiliang University（中国计量大学）；Michigan Technological University（密歇根理工大学）
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 fra…
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Georgia Institute of Technology（佐治亚理工学院）；Northwestern University（西北大学）
In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we firs…
Alexandria University（亚历山大大学）；Egypt-Japan University of Science and Technology (E-JUST)（埃及日本科学技术大学）
Asset health monitoring continues to be of increasing importance on productivity, reliability, and cost reduction. Early Fault detection is a keystone of health management as part of the emerging Prognostics and Health Management (PHM) philosophy. This paper proposes a Hidden Markov Model (HMM) to assess the machine health degradation. using Principal Component Analysis (PCA) to enhance features extracted from vibration signals is considered. The enhanced features capture the second order structure of the data. The experimental results based on a bearing test bed show the plausibility of the p…
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）
University of California, Merced（加州大学默塞德分校）；Beijing University of Chemical Technology（北京化工大学）
Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper prop…
