Intelligent fault diagnosis is essential to safe operation of machinery. However, due to scarce fault samples and data heterogeneity in field machinery, deep learning based diagnosis methods are prone to over-fitting with poor generalization ability. To solve the problem, this paper proposes a personalized federated learning framework, enabling multi-task fault diagnosis method across multiple factories in a privacypreserving manner. Firstly, rotating machines from different factories with similar vibration feature data are categorized into machine groups using a federated clustering method. Then, a multi-task deep learning model based on convolutional neural network is constructed to diagnose the multiple faults of machinery with heterogeneous information fusion. Finally, a personalized federated learning framework is proposed to solve data heterogeneity across different machines using adaptive hierarchical aggregation strategy. The case study on collected data from real machines verifies the effectiveness of the proposed framework. The result shows that the diagnosis accuracy could be improved significantly using the proposed personalized federated learning, especially for those machines with scarce fault samples.
School of Mechanical Engineering, Federal University of Uberlândia（Uberlândia联邦大学机械工程学院）；University of Padova（帕多瓦大学）；Federal University of São João del Rei（圣若昂德尔雷伊联邦大学）
The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis…
Deep learning and big data algorithms have become widely used in industrial applications to optimize several tasks in many complex systems. Particularly, deep learning model for diagnosing and prognosing machinery health has leveraged predictive maintenance (PdM) to be more accurate and reliable in decision making, in this way avoiding unnecessary interventions, machinery accidents, and environment catastrophes. Recently, Transformer Neural Networks have gained notoriety and have been increasingly the favorite choice for Natural Language Processing (NLP) tasks. Thus, given their recent major a…
Federal University of Uberlândia（乌贝兰迪亚联邦大学）；University of Padova（帕多瓦大学）；Federal University of São João del Rei（圣若昂德尔雷伊联邦大学）
Artificial Intelligence (AI) is one of the approaches that has been proposed to analyze the collected data (e.g., vibration signals) providing a diagnosis of the asset's operating condition. It is known that models trained with labeled data (supervised) achieve excellent results, but two main problems make their application in production processes difficult: (i) impossibility or long time to obtain a sample of all operational conditions (since faults seldom happen) and (ii) high cost of experts to label all acquired data. Another limitating factor for the applicability of AI approaches in this…
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…
Polarized Direct Cross-Attention Message Passing in GNNs for Machinery Fault Diagnosis
Department of Automation, College of Artificial Intelligence, China University of Petroleum (Beijing)（自动化系，人工智能学院，中国石油大学（北京））；Department of Safety Engineering, College of Safety and Ocean Engineering, China University of Petroleum (Beijing)（安全工程系，安全与海洋工程学院，中国石油大学（北京））
A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis
University of Cambridge（剑桥大学）；Heidelberg University（海德堡大学）；University of Liverpool（利物浦大学）；Roche Innovation Center（罗氏创新中心）；The Cambridge Centre for AI in Medicine（剑桥医学人工智能中心）；University of California Los Angeles（加州大学洛杉矶分校）
City University of Hong Kong（香港城市大学）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电气工程系（注：根据提取规则，机构粒度到大学/学院/公司/研究院级别即可，此处仅提取到大学级别，故正确应为City University of Hong Kong））
Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. T…
CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification
Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University（大数据与智能软件工程研究中心，软件学院，新疆大学）；Hunan Province Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University（生物信息学湖南省重点实验室，计算机科学与工程学院，中南大学）；School of Science and Engineering, University of Dundee（科学与工程学院，邓迪大学）；Division of Biomedical Engineering, University of Saskatchewan（生物医学工程系，萨斯喀彻温大学）；Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University（生物信息学湖南省重点实验室，计算机科学与工程学院，中南大学）
With the development of deep learning (DL) techniques, rotating machinery intelligent diagnosis has gone through tremendous progress with verified success and the classification accuracies of many DL-based intelligent diagnosis algorithms are tending to 100\%. However, different datasets, configurations, and hyper-parameters are often recommended to be used in performance verification for different types of models, and few open source codes are made public for evaluation and comparisons. Therefore, unfair comparisons and ineffective improvement may exist in rotating machinery intelligent diagn…
Politecnico di Milano（米兰理工大学）；University of Victoria（维多利亚大学）；Swiss Federal Institute of Technology at Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing…
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Faculty of Engineering and Natural Sciences（工程与自然科学学院）；Department of Information and Communications Engineering（信息与通信工程系）；Department of Management, Economics and Industrial Engineering（管理、经济与工业工程系）
RmGPT: A Foundation Model with Generative Pre-trained Transformer for Fault Diagnosis and Prognosis in Rotating Machinery
School of Aeronautics and Astronautics, Shanghai Jiao Tong University（航空宇航学院，上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；MINES Paris-PSL University（巴黎-里昂商学院）；Energy Department, Politecnico di Milano（能源部门，米兰理工学院）；Beijing Aeronautical Technology Research Center（北京航空航天科技研究所）
Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis
organization= Centre for Effciency；Performance Engineering, University of Huddersfield , city= Huddersfield , country= HD1 3DH, UK；organization= National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences , city= Beijing , country= China；organization= Nanjing Forestry University , city= Beijing , country= China；organization= School of Mechanical Electrical Engineering, Beijing Information Science \& Technology University , city= Beijing,100192 , country= China；organization= Key Laboratory of Modern Measurement \& Control Technology, Ministry of Education, Beijing Information Science \& Technology University , city= Beijing , country= China；organization= Department of Production Engineering, KTH Royal Institute of Technology , city= Stockholm , country= Sweden
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
Inria Paris（法国国家信息与自动化研究所巴黎中心）；Centre de Recherche des Cordeliers（科德利埃研究所）；Inserm（法国国家健康与医学研究院）；Université Paris Cité（巴黎西岱大学）；Sorbonne Université（索邦大学）；Hôpital Necker（内克尔医院）；Assistance Publique - Hôpitaux de Paris（巴黎公共医疗援助医院）
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time and money. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for expert knowledge and human work. The emergence of deep learning architectures in co…
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…
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds fur…
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Yale University（耶鲁大学）；University of Southern California（南加州大学）；University of California Irvine（加州大学欧文分校）；University of Science and Technology of China（中国科学技术大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University of Bern（伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）
Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning
Sepuluh Nopember Institute of Technology（印度尼西亚 Sepuluh Nopember 理工学院）；National Institute of Advanced Industrial Science and Technology（日本国立先进工业科学技术研究院）
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）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）
The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the …
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）
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 …
Indian Institute of Information Technology Design and Manufacturing Jabalpur（印度信息技术设计与制造学院）；Birla Institute of Technology and Science – Pilani（比尔拉理工学院）；National Institute of Technology Karnataka（卡纳塔克国立理工学院）；VIT University（韦洛尔理工大学）
Gear drives are one of the most widely used transmission system in many machinery. Sound signals of a rotating machine contain the dynamic information about its health conditions. Not much information available in the literature reporting suitability of sound signals for fault diagnosis applications. Maximum numbers of literature are based on FFT (Fast Fourier Transform) analysis and have its own limitations with non-stationary signals like the ones from gears. In this paper, attempt has been made in using sound signals acquired from gears in good and simulated faulty conditions for the purpos…
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…
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…
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…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Veterans Affairs Tennessee Valley Healthcare System（退伍军人事务部田纳西河谷医疗系统）
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on the various images (e.g., radiological, pathological, and camera images) and non-image data (e.g., clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in …
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection
School of Artificial Intelligence and Computer Science, Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education（江南大学人工智能与计算机科学学院，智能医疗技术工程研究中心，教育部）；School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University（上海理工大学计算机与信息工程学院，人工智能研究院）；School of Internet of Things Engineering, Wuxi University（无锡大学物联网工程学院）
Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Institute of Artificial Intelligence Research, Jiangsu Second Normal University（江苏第二师范学院人工智能研究所）；Digital Research Institute, ENN Group（ENN集团数字研究院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算、工程与数学科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology（麻省理工学院哈佛-麻省理工健康科学与技术系）；Affective Computing group, MIT Media Lab, Massachusetts Institute of Technology（麻省理工学院媒体实验室情感计算研究组）；Center for Pain and the Brain, Harvard Medical School（哈佛医学院疼痛与大脑研究中心）；Department of Anesthesiology, Boston Children’s Hospital（波士顿儿童医院麻醉科）
Currently self-report pain ratings are the gold standard in clinical pain assessment. However, the development of objective automatic measures of pain could substantially aid pain diagnosis and therapy. Recent neuroimaging studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for pain detection. This is a brain-imaging technique that provides non-invasive, long-term measurements of cortical hemoglobin concentration changes. In this study, we focused on fNIRS signals acquired exclusively from the prefrontal cortex, which can be accessed unobtrusively, and derived an …
Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0
Hamadan University of Medical Sciences（哈马丹医科大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Motamed Cancer Institute（莫塔梅德癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Technological Virtual Collaboration (TECVICO Corp.)（技术虚拟协作公司）
Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history an…
AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
Humboldt-Universität zu Berlin, Institut für Physik, AG Moderne Optik（柏林洪堡大学物理学院现代光学小组）；University of Doha for Science and Technology, College of Engineering and Technology, Department of Mechanical Engineering Technology（多哈科学技术大学工程学院机械工程技术系）
