Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in the modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, especially deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however, their performance is heavily dependent on the training data and how well that data represents the test data. This issue mandates fine-tuning and even training the models from scratch when there is a slight change in operating conditions or equipment. Transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application. In this paper, a unified definition for transfer learning and its different types is provided, Prognostics and Health Management (PHM) studies that have used transfer learning are reviewed in detail, and finally, a discussion on transfer learning application considerations and gaps is provided for improving the applicability of transfer learning in PHM.
Cyber-Physical Systems Group, Vienna University of Technology（维也纳工业大学网络物理系统组）
This paper studies an intelligent ultimate technique for health-monitoring and prognostic of common rotary machine components, particularly bearings. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse auto-encoder. Then, the correlation of the extracted attributes of the initial samples (presumably healthy at the beginning of the test) with the succeeding samples is calculated and passed through a moving-average filter. The normalized output is named auto-encoder correlation-based (AEC) rate which stands for an informati…
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）；Civil and Environmental Engineering Department & Garrick Institute for the Risk Sciences, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系及加里克风险科学研究所）
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several da-ta-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for…
Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
School of Epidemiology and Public Health, University of Ottawa（渥太华大学流行病学与公共卫生学院）；Children’s Hospital of Eastern Ontario Research Institute（东部儿童医院研究学院）；Department of Nephrology and Medical Intensive Care, Charité - Universitaetsmedizin Berlin（柏林夏里特医学院肾内科与医学重症科）；Division of Medical Oncology, Department of Medicine, The University of Ottawa（渥太华大学医学系肿瘤科）；Department of Oncology, McMaster University（麦马斯特大学肿瘤科）；Cancer Therapeutics Program, The Ottawa Hospital Research Institute（渥太华医院研究学院癌症治疗计划）；Ottawa Hospital Cancer Center, The Ottawa Hospital Research Institute（渥太华医院癌症中心，渥太华医院研究学院）；CHUM, Division of Medical Oncology and Hematology, Université de Montréal（蒙特利尔大学CHUM，医学肿瘤学与血液学部）；Canadian Cancer Trials Group, Queen’s University（加拿大癌症试验组，皇后大学）；Diagnostic Development, Ontario Institute for Cancer Research（安大略癌症研究所以及诊断发展部）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学实验室医学与病理学部）；Department of Surgery, Leiden University Medical Center（莱顿大学医学中心外科部）；St. Augustinus Hospital, Antwerp, Belgium（比利时安特卫普圣奥古斯丁医院）；Department of Pathology, Glasgow, United Kingdom（英国格拉斯哥大学病理部）；Department of Gynecology and Obstetrics, University Center Mainz, Mainz, Germany（德国马尔堡大学中心妇科与妇产科部）；National and Kapodistrian University of Athens, Medical School, Athens, Greece（希腊雅典国家与卡波迪斯托里亚大学医学院）
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to pre-define the exact failure thresholds for a machinery operating in a dynamic environment with a high level of uncertainty. In this case, dynamic thresholds depicted by the discrete states is a very attractive way to estimate the RUL of a dynamic machinery. Currently, there are …
University of California Los Angeles（加利福尼亚大学洛杉矶分校）
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open APIs (e.g., Qiskit from IBM) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a…
Gaussian process regression is a powerful method for predicting states based on given data. It has been successfully applied for probabilistic predictions of structural systems to quantify, for example, the crack growth in mechanical structures. Typically, predefined mean and covariance functions are employed to construct the Gaussian process model. Then, the model is updated using current data during operation while prior information based on previous data is ignored. However, predefined mean and covariance functions without prior information reduce the potential of Gaussian processes. This p…
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
Singapore University of Technology and Design（新加坡科技设计大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）前沿人工智能研究中心）；Chongqing University（重庆大学）；Nanyang Technological University（南洋理工大学）
Knowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics
Information and Communications Technology Cluster, Singapore Institute of Technology（信息与通信技术集群，新加坡理工学院）；Department of EEE, Chaitanya Bharathi Institute of Technology（电子工程系，查蒂兰·巴哈瓦蒂理工学院）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；School of Materials Science and Engineering, Nanyang Technological University（材料科学与工程学院，南洋理工大学）
The State of Lithium-Ion Battery Health Prognostics in the CPS Era
San Jose State University（圣何塞州立大学）；Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）；Manipal Institute of Technology（马尼帕尔理工学院）；University of Denver（丹佛大学）
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）
PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
Singapore Institute of Technology（新加坡理工学院）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Nanyang Technological University (NTU)（南洋理工大学）
Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used f…
University of Rochester（罗切斯特大学）；University of Washington（华盛顿大学）
Surgical Site Infection (SSI) is a national priority in healthcare research. Much research attention has been attracted to develop better SSI risk prediction models. However, most of the existing SSI risk prediction models are built on static risk factors such as comorbidities and operative factors. In this paper, we investigate the use of the dynamic wound data for SSI risk prediction. There have been emerging mobile health (mHealth) tools that can closely monitor the patients and generate continuous measurements of many wound-related variables and other evolving clinical variables. Since exi…
Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover, such approaches to prognostics do not generalize to new domains. Consequently, domain-agnostic data-driven machine learning approaches to prognostics are desirable. Damage progression is a path-dependent process and explicitly modeling the temporal patterns is critical for accu…
Department of Applied Mechanics（应用力学系）；Institut FEMTO-ST, UBFC/UFC/ENSMM/CNRS/UTBM（FEMTO-ST 研究所，UBFC/UFC/ENSMM/CNRS/UTBM）
[This paper was initially published in PHME conference in 2016, selected for further publication in International Journal of Prognostics and Health Management.] This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM) for fault detection and prognostics of equipments based on sensors' data. It is a particular dynamic Bayesian network that allows to represent the dynamics of a system by means of a Hidden Markov Model (HMM) and an autoregressive (AR) process. The Markov chain assumes that the system is switching back and forth between internal states while the AR process ens…
Departamento de Ciencias de la Computación e Inteligencia Artificial（计算机科学与人工智能系）；Datrik Intelligence, S.A.（Datrik智能股份公司）；Universidad de Sevilla（塞维利亚大学）
The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as Remaining Useful Life (RUL). Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that this kind of b…
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
Iowa State University（爱荷华州立大学）；ETH（苏黎世联邦理工学院）；University of Michigan（密歇根大学）；University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）；EPFL（洛桑联邦理工学院）；Sandia National Laboratories（桑迪亚国家实验室）；Georgia Institute of Technology（佐治亚理工学院）；The Hong Kong Polytechnic University（香港理工大学）；University of Connecticut（康涅狄格大学）
Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost. To address these issues, this work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to enhance the accu…
Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial…
In manufacturing, unexpected failures are considered a primary operational risk, as they can hinder productivity and can incur huge losses. State-of-the-art Prognostics and Health Management (PHM) systems incorporate Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain the health status of equipment, and thus reduce the downtime, maintenance cost and increase the productivity. Unfortunately, IoT sensors and DL algorithms, both are vulnerable to cyber attacks, and hence pose a significant threat to PHM systems. In this paper, we adopt the adversarial example crafting …
A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression
Department of Information Systems University of Maryland Baltimore County（信息系统系大学马里兰大学巴尔的摩县）；Department of Information Systems Colorado State University Pueblo（信息系统系科罗拉多州立大学普劳尔）
When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
Singapore Institute of Technology（新加坡科技学院）；Institute of Materials Research and Engineering（材料研究与工程研究所）；Agency for Science, Technology and Research（科技研究局）；Institute of High Performance Computing（高性能计算研究所）
Institut Clément Ader（克莱蒙·阿代尔研究所）；Université de Toulouse（图卢兹大学）；ISAE-SUPAERO（法国高等航空航天学院）
With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to in…
Vocal Prognostic Digital Biomarkers in Monitoring Chronic Heart Failure: A Longitudinal Observational Study
Centre for Digital Health Interventions, ETH Zurich（苏黎世联邦理工学院数字健康干预中心）；Agentic System Lab, ETH Zurich（苏黎世联邦理工学院代理系统实验室）；Cardiology, University Heart Center Zurich, University Hospital Zurich（苏黎世大学医院心脏中心心脏病学）；Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital（麻省总医院喉外科与嗓音康复中心）；Department of Surgery, Harvard Medical School（哈佛医学院外科学系）；Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School（哈佛医学院医学科学部言语与听觉生物科学与技术）；MGH Institute of Health Professions（麻省总医院健康职业学院）；Centre for Digital Health Interventions, University of St. Gallen（圣加仑大学数字健康干预中心）
Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
Department of Industrial and Systems Engineering, University of Florida（工业与系统工程系，佛罗里达大学）；School of Forest, Fisheries, and Geomatics Sciences, University of Florida（森林、渔业与地理科学学院，佛罗里达大学）
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study
Department of Information and Computing Sciences, Utrecht University（乌特勒支大学信息与计算科学系）；Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）
RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
Queen Mary University of London（女王玛丽大学）；Shanghai Sixth People’s Hospital Affiliated to SJTU School of Medicine（上海第六人民医院附属复旦大学医学院）；University College London（大学学院伦敦）
State University of New York at Binghamton（纽约州立大学宾汉姆顿分校）；Virtua Health（Virtua医疗集团）
Post-discharge care management coordinates patients' referrals to improve their health after being discharged from hospitals, especially elderly and chronically ill patients. In a care management setting, health referrals are processed by a specialized unit in the managed care organization (MCO), which interacts with many other entities including inpatient hospitals, insurance companies, and post-discharge care providers. In this paper, a machine-learning-guided discrete event simulation framework to improve health referrals processing is proposed. Random-forest-based prediction models are dev…
Center for Industrial Artificial Intelligence（工业人工智能中心）；University of Maryland（马里兰大学）
In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held betwe…
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
University of Milano - Bicocca（米兰比可卡大学）；Fondazione IRCCS Istituto Nazionale dei Tumori（意大利国家肿瘤研究所（IRCCS基金会））；F. Hoffmann - La Roche Ltd（罗氏公司）；University of Twente（特文特大学）；Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫勒人工智能研究所（IDSIA））
Constraint-Guided Learning of Data-driven Health Indicator Models: An Application on the Pronostia Bearing Dataset
KU Leuven（鲁汶大学）；Leuven.AI - KU Leuven institute for AI（鲁汶人工智能研究所（鲁汶大学））；Flanders Make（佛兰德斯制造研究院）；Addis Ababa University（亚的斯亚贝巴大学）；Ghent University（根特大学）
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…
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate in a large state-wide population whether we can use artificial intelligence (AI) techniques to identify clusters of patient trajectories within the broader diabetes population in order to create cost-effective, narrowly-focused case management intervention strategies to reduce d…
Northeastern University（东北大学）；University of Maryland（马里兰大学）；New York University School of Medicine（纽约大学医学院）；NYU Langone Health（纽约大学朗格尼健康中心）
Comorbid chronic conditions are common among people with type 2 diabetes. We developed an Artificial Intelligence algorithm, based on Reinforcement Learning (RL), for personalized diabetes and multi-morbidity management with strong potential to improve health outcomes relative to current clinical practice. In this paper, we modeled glycemia, blood pressure and cardiovascular disease (CVD) risk as health outcomes using a retrospective cohort of 16,665 patients with type 2 diabetes from New York University Langone Health ambulatory care electronic health records in 2009 to 2017. We trained a RL …
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
University of Hamburg（汉堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University of Southern Denmark（南丹麦大学）；Næstved-Slagelse-Ringsted Hospitals（奈斯特韦兹-斯拉厄尔瑟-灵斯泰兹医院）
Test-Time Learning and Inference-Time Deliberation for Efficiency-First Offline Reinforcement Learning in Care Coordination and Population Health Management
AmarDoctor: An AI-Driven, Multilingual, Voice-Interactive Digital Health Application for Primary Care Triage and Patient Management to Bridge the Digital Health Divide for Bengali Speakers
MedAi Bangladesh Limited（MedAi孟加拉有限公司）；Central Police Hospital, Dhaka, Bangladesh（达卡警察医院）；University of Birmingham, UK（英国伯明翰大学）
