Статті в журналах: "Health Management (PHM)"

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Steenkamer, Betty, Esther de Weger, Hanneke Drewes, Kim Putters, Hans Van Oers, and Caroline Baan. "Implementing population health management: an international comparative study." Journal of Health Organization and Management 34, no. 3 (April 6, 2020): 273–94. http://dx.doi.org/10.1108/jhom-06-2019-0189.

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PurposeThe purpose of this paper is to gain insight into how population health management (PHM) strategies can successfully integrate and reorganize public health, health care, social care and community services to improve population health and quality of care while reducing costs growth, this study compared four large-scale transformation programs: Greater Manchester Devolution, Vancouver Healthy City Strategy, Gen-H Cincinnati and Gesundes Kinzigtal.Design/methodology/approachFollowing the realist methodology, this explorative comparative case-study investigated PHM initiatives' key features and participants' experiences of developing such initiatives. A semi-structured interview guideline based on a theoretical framework for PHM guided the interviews with stakeholders (20) from different sectors.FindingsFive initial program theories important to the development of PHM were formulated: (1) create trust in a shared vision and understanding of the PHM rationale to establish stakeholders' commitment to the partnership; (2) create shared ownership for achieving the initiative's goals; (3) create shared financial interest that reduces perceived financial risks to provide financial sustainability; (4) create a learning environment to secure initiative's credibility and (5) create citizens' and professionals' awareness of the required attitudes and behaviours.Originality/valueThe study highlights initial program theories for the implementation of PHM including different strategies and structures underpinning the initiatives. These insights provide a deeper understanding of how large-scale transformation could be developed.

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Lyu, Yongle, Zhuo Pang, Chuang Zhou, and Peng Zhao. "Prognostics and health management technology for radar system." MATEC Web of Conferences 309 (2020): 04009. http://dx.doi.org/10.1051/matecconf/202030904009.

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Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future.

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Wu, Ji. "Research on Prognostic and Health Management of Inertial Navigation System." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012075. http://dx.doi.org/10.1088/1742-6596/2252/1/012075.

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Abstract Prognostic and health management (PHM) system significantly reduces costs of maintenance, utilization, and assurance, and improves safety and availability of aircraft. By drawing on and absorbing foreign advanced experience, this paper deeply studies the key technology of PHM, and combines the specific features of the inertial navigation system to determine the program flow of the PHM system. This paper is described PHM technical related elements, which can be reformed the design of Inertial Navigation System and can push the development of the condition-based maintenance and PHM system.

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Wu, Ji. "Research on Prognostic and Health Management of Inertial Navigation System." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012075. http://dx.doi.org/10.1088/1742-6596/2252/1/012075.

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Abstract Prognostic and health management (PHM) system significantly reduces costs of maintenance, utilization, and assurance, and improves safety and availability of aircraft. By drawing on and absorbing foreign advanced experience, this paper deeply studies the key technology of PHM, and combines the specific features of the inertial navigation system to determine the program flow of the PHM system. This paper is described PHM technical related elements, which can be reformed the design of Inertial Navigation System and can push the development of the condition-based maintenance and PHM system.

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Tsui, Kwok L., Nan Chen, Qiang Zhou, Yizhen Hai, and Wenbin Wang. "Prognostics and Health Management: A Review on Data Driven Approaches." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/793161.

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Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

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Moumtzoglou, Anastasius, and Abraham Pouliakis. "Mapping Population Health Management Roadmap into Cervical Cancer Screening Programs." International Journal of Reliable and Quality E-Healthcare 4, no. 3 (July 2015): 1–18. http://dx.doi.org/10.4018/ijrqeh.2015070101.

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Population Health Management (PHM) aims to provide better health outcomes for preventing diseases, closing care gaps and providing more personalized care. Since the inception of the Pap test, cervical cancer (CxCa) decreased in countries applying cervical cancer programs, involving both prevention and treatment. In this article, the authors map the PHM roadmap to the design of cervical cancer screening programs and examine the effect on the supporting information technology systems. Notwithstanding screening programs have a tight relation to PHM; the mapping reveals numerous interventions involving additional data sources, and timeless reconfiguration.

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Long, Jiang, and Wei An Jiang. "Maintenance for Improving Manufacturing Equipments Availability Using Prognostics and Health Management." Advanced Materials Research 199-200 (February 2011): 543–47. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.543.

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There is a growing need for improving manufacturing equipments availability to achieve high levels of productivity. As a key complement to CBM and RCM, PHM is becoming a key enabler for achieving cost effective ultra-reliability and availability in tomorrow’s manufacturing equipments at an affordable cost. Based on traditional maintenance strategies, key issues pertaining to PHM application to manufacturing equipments, including health monitoring, diagnostics and prognostics, are discussed in this paper. As an example, a method for dynamic MFOP based maintenance strategy optimization using PHM and RUL estimation is presented.

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Sadegh Kouhestani, Hamed, Xiaoping Yi, Guoqing Qi, Xunliang Liu, Ruimin Wang, Yang Gao, Xiao Yu, and Lin Liu. "Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities." Energies 15, no. 18 (September 9, 2022): 6599. http://dx.doi.org/10.3390/en15186599.

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Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical role in achieving such a goal. PHM ensures the reliability and availability of batteries during their operational time with acceptable safety margin. In the past two decades, much of the focus has been directed towards the PHM of lithium-ion batteries, while little attention has been given to PHM of solid-state batteries. Hence, this report presents a holistic review of the recent advances and current trends in PHM techniques of solid-state batteries and the associated challenges. For this purpose, notable commonly employed physics-based, data-driven, and hybrid methods are discussed in this report. The goal of this study is to bridge the gap between liquid state and SSBs and present the crucial aspects of SSBs that should be considered in order to have an accurate PHM model. The primary focus is given to the ML-based data-driven methods and the requirements that are needed to be included in the models, including anode, cathode, and electrolyte materials.

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Bradshaw, Richard L., Kensaku Kawamoto, Kimberly A. Kaphingst, Wendy K. Kohlmann, Rachel Hess, Michael C. Flynn, Claude J. Nanjo, et al. "GARDE: a standards-based clinical decision support platform for identifying population health management cohorts." Journal of the American Medical Informatics Association 29, no. 5 (February 28, 2022): 928–36. http://dx.doi.org/10.1093/jamia/ocac028.

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Abstract Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. Objective The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. Materials and Methods An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. Results The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. Discussion GARDE’s component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

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Shao, Xin Jie, Li Jun Cao, Jin Hua Liu, Qiao Ma, Guang Tian, and Hui Bin Hu. "Research on Prognostics and Health Management System for Self-Propelled Gun." Advanced Materials Research 591-593 (November 2012): 1938–41. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1938.

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Considering the encapsulation, tremendous working stress, complicated fault mechanism and process, an object-oriented architecture Prognostics and Health Management (PHM) system of Self-Propelled Gun is put forward to solve the difficulties in modern equipments’ maintenance decision and safeguard. The relative key techniques, such as fault characteristics extraction, system identification, data fusion among sensors, adaptive fault thresholds setting, intelligent fault inference mechanism and fault prediction mechanism are discussed in detail. Above functional modules compose the PHM system. Their mutual relationships and data flow are shown as architecture graph, which is the basis of PHM system establishment. Simulation verification experiments indicate that the proposed architecture can satisfy the requirements of standardization, hierarchy and evolvement, as well as reducing the maintenance cost and difficulties.

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Cao, Zhi, Junhua Zhang, Zhenxiang Li, Xin Zhao, Dong Guo, and Hongyong Fu. "Study on Prognostics and Health Management of Fluid Loop System for Space Application." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012059. http://dx.doi.org/10.1088/1742-6596/2184/1/012059.

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Abstract Prognostics and Health Management (PHM) is an important technical means to realize condition-based maintenance of complex systems. As a complex space application engineering, the space application fluid loop system has the requirements of high safety, high reliability and long life. Therefore, this paper adopts PHM technology to comprehensively study the space application fluid loop system from two aspects of architecture and key technology respectively. Firstly, this paper introduces the current status of health management of fluid loop system for space application and the problems to be solved urgently. Secondly, the PHM system architecture and key technologies such as condition monitoring, health assessment, fault diagnosis, fault prediction and health management are described, and the PHM system research scheme of fluid loop for space application is established. Finally, a case study is carried out by taking the fluid loop main engine of the core power equipment as an example. The analysis results show that the PHM technology can effectively reduce the system failure rate, improve the system safety and reliability, and provide a strong guarantee for the long-term on-orbit operation of the system.

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Song, Han Qiang, Ben Wei Li, Tao Sun, and Yong Hua Wang. "Design of Prognostics and Health Management System for Turbofan Engine." Applied Mechanics and Materials 599-601 (August 2014): 2108–11. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.2108.

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The prognostics and health management system of next generation turbofan aero-engine is researched for better adapting military mission needs. The system canonical structure is designed on the basis of GE company open structure. Additionally, the gas path PHM architecture is especially analyzed, and the key technologies in the system are introduced. It gets the technology ways for the realization of the system development. And the work can provide support for the development of the PHM system from the top course structure view.

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Hsu, Tzu-Hsuan, Yuan-Jen Chang, He-Kai Hsu, Tsung-Ti Chen, and Po-Wen Hwang. "Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM)." Aerospace 9, no. 8 (August 20, 2022): 462. http://dx.doi.org/10.3390/aerospace9080462.

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Landing gear is an essential part of an aircraft. However, the components of landing gear are susceptible to degradation over the life of their operation, which can result in the shimmy effect occurring during take-off and landing. In order to reduce unplanned flight disruptions and increase the availability of aircraft, the predictive maintenance (PdM) technique is investigated in this study. This paper presents a case study on the implementation of a health assessment and prediction workflow for remaining useful life (RUL) based on the prognostics and health management (PHM) framework of currently in-service aircraft, which could significantly benefit fleet operators and aircraft maintenance. Machine learning is utilized to develop a health indicator (HI) for landing gear using a data-driven approach, whereas a time-series analysis (TSA) is used to predict its degradation. The degradation models are evaluated using large volumes of real sensor data from in-service aircraft. Finally, the challenges of implementing a built-in PHM system for next-generation aircraft are outlined.

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Zhang, Peng, Zeyu Gao, Lele Cao, Fangyang Dong, Yongjiu Zou, Kai Wang, Yuewen Zhang, and Peiting Sun. "Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy." Machines 10, no. 2 (January 19, 2022): 72. http://dx.doi.org/10.3390/machines10020072.

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Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

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An, Ziliang, Weixin Wang, and Taohan Chen. "Research on equipment health management system based on PHM." IOP Conference Series: Materials Science and Engineering 677 (December 10, 2019): 052013. http://dx.doi.org/10.1088/1757-899x/677/5/052013.

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Jiang, Na, Chunpeng Zhang, Yang Cao, and Rixin Zhan. "Application of prognostic and health management in avionics system." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 1–9. http://dx.doi.org/10.54097/hset.v7i.988.

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Currently, most aircraft avionics systems are maintained based on reported failures or periodic system replacement. However, the evolution of prognostic and health management (PHM) concepts from mechanical to electronic systems and further to avionics system maintenance has been driven by changes in weapon platform procurement and support requirements. At the same time, with the increasing complexity of avionics design, integrated modular avionics (IMA) came into being. The appearance of IMA design concept marks the gradual transition of avionics system from distributed joint architecture to integrated architecture, which also provides the foundation for PHM technology to be applied to avionics system. This paper reviews the application and research status of predictive and health management system technology in avionics system.

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Wang, Cheng, Tongtong Ji, Feng Mao, Zhenpo Wang, and Zhiheng Li. "Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods." Advances in Civil Engineering 2021 (January 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6685900.

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The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

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Nor, Ahmad Kamal Mohd, Srinivasa Rao Pedapati, Masdi Muhammad, and Víctor Leiva. "Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses." Sensors 21, no. 23 (December 1, 2021): 8020. http://dx.doi.org/10.3390/s21238020.

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Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.

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Yang, Wen Xue, Zhe Chen, and Feng Yang. "A Survey of Sensor Technologies for Prognostics and Health Management of Electronic Systems." Applied Mechanics and Materials 602-605 (August 2014): 2229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2229.

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Recently, the field of Prognostics and Health Management (PHM) for electronic products and systems has received increasing attention due to the potentialities to provide early warning of system failures, reduce life cycle costs, and forecast maintenance as needed. This paper introduces the sensors and their sensor technologies. The required attributes of sensors for the development for PHM of electronics are discussed. Finally, their trends in sensor systems are presented.

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Gu, Jie, and Michael Pecht. "Prognostics and Health Assessment Implementation for Electronic Products." Journal of the IEST 53, no. 1 (April 1, 2010): 44–58. http://dx.doi.org/10.17764/jiet.53.1.18763271g23n61x0.

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Traditional handbook-based reliability prediction methods for electronic products are inaccurate and misleading. In this paper, we will present a prognostics and health management (PHM) approach that is more suitable for reliability (remaining life) assessment for electronic products, since it considers actual operational and environmental loading conditions for individual products. The process for implementing PHM in electronics is discussed. Several examples of implementation in industry and defense are also given.

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Serxner, Seth, Steven P. Noeldner, and Daniel Gold. "Best Practices for an Integrated Population Health Management (PHM) Program." American Journal of Health Promotion 20, no. 5 (May 2006): 1–14. http://dx.doi.org/10.4278/0890-1171-20.5.tahp-1.

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Steenkamer, Betty, Hanneke Drewes, Kim Putters, Hans van Oers, and Caroline Baan. "Reorganizing and integrating public health, health care, social care and wider public services: a theory-based framework for collaborative adaptive health networks to achieve the triple aim." Journal of Health Services Research & Policy 25, no. 3 (March 16, 2020): 187–201. http://dx.doi.org/10.1177/1355819620907359.

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Objective Population health management (PHM) refers to large-scale transformation efforts by collaborative adaptive health networks that reorganize and integrate services across public health, health care, social care and wider public services in order to improve population health and quality of care while at the same time reducing cost growth. However, a theory-based framework that can guide place-based approaches towards a comprehensive understanding of how and why strategies contribute to the development of PHM is lacking, and this review aims to contribute to closing this gap by identifying the key components considered to be key to successful PHM development. Methods We carried out a scoping realist review to identify configurations of strategies (S), their outcomes (O), and the contextual factors (C) and mechanisms (M) that explain how and why these outcomes were achieved. We extracted theories put forward in included studies and that underpinned the formulated strategy-context-mechanism-outcome (SCMO) configurations. Iterative axial coding of the SCMOs and the theories that underpin these configurations revealed PHM themes. Results Forty-one studies were included. Eight components were identified: social forces, resources, finance, relations, regulations, market, leadership, and accountability. Each component consists of three or more subcomponents, providing insight into (1) the (sub)component-specific strategies that accelerate PHM development, (2) the necessary contextual factors and mechanisms for these strategies to be successful and (3) the extracted theories that underlie the (sub)component-specific SCMO configurations. These theories originate from a wide variety of scientific disciplines. We bring these (sub)components together into what we call the Collabroative Adaptive Health Network (CAHN) framework. Conclusions This review presents the strategies that are required for the successful development of PHM. Future research should study the applicability of the CAHN framework in practice to refine and enrich identified relationships and identify PHM guiding principles.

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Qu, Yuanju, Xinguo Ming, Siqi Qiu, Maokuan Zheng, and Zengtao Hou. "An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network." Sensors 19, no. 10 (May 21, 2019): 2338. http://dx.doi.org/10.3390/s19102338.

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With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

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Raouf, Izaz, Asif Khan, Salman Khalid, Muhammad Sohail, Muhammad Muzammil Azad, and Heung Soo Kim. "Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey." Mathematics 10, no. 18 (September 6, 2022): 3233. http://dx.doi.org/10.3390/math10183233.

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Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.

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Liu, Chun Rong, Dong Hu, Wei Min Lv, and Deng Jian Fang. "Lesson Learned in the Research and Development of Specialized Vehicle Health Management System: A Hybrid Approach for Decision Support System." Applied Mechanics and Materials 631-632 (September 2014): 1246–52. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.1246.

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The reality is that PHM is mostly a matter of the analytic method and process of building tailored and hybrid functional DSS to meet various requirements in field of maintenance and logistics support, not only creating models and algorithms in black box which is philosophical basis of so many PHM universal platforms, especially in the concept and design stage of systems. Some issues under discussions are presented firstly. Then, the critical cases analysis method is described, and the demonstration on the different level of specialized vehicle health management system is shown as human-computer interfaces. Lastly, the preliminary design approach to implement PHM system is presented. In addition, potential difficult points in practice are investigated for R&D of PHM.

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Wang, Wenbin. "Special Section on Prognostics and Systems Health Management (PHM), Extended Papers From the PHM Macau 2010 Conference." IEEE Transactions on Reliability 60, no. 1 (March 2011): 2. http://dx.doi.org/10.1109/tr.2011.2110190.

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McConnell, Macy, Dan Mobley, Alexander Gidal, and Michael Nagy. "Population Health Management during Student Pharmacist Introductory Experiential Education to Expand Clinical Pharmacist Impact." INNOVATIONS in pharmacy 10, no. 4 (November 18, 2019): 9. http://dx.doi.org/10.24926/iip.v10i4.2231.

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Objective: To evaluate the impact of incorporating pre-advanced pharmacy practice experience (pre-APPE) student pharmacists into three different population health management (PHM) projects. Methods: The prospective quality improvement projects incorporated three third-year student pharmacists who developed and conducted individual PHM projects over the course of three to seven months. The projects included hypoglycemia screening, hepatitis C virus and human immunodeficiency virus screening, and statin use evaluations for atherosclerotic cardiovascular disease risk reduction. Under the guidance of a clinical pharmacist, students developed project materials, conducted patient chart reviews, and contacted patients to make interventions such as recommendations for therapy, ambulatory patient monitoring, patient education, and arranging provider follow-up. Student impact was evaluated through the number of patients screened, the number of eligible patients contacted, and the total number of interventions or recommendations made. Student time spent was tracked throughout the projects. Results: Out of 244 patients screened, 198 patients met inclusion criteria and 162 patients were contacted or assessed by a student pharmacist. Students made a total of 319 interventions, including patient education (132), patient monitoring (132), pharmacotherapy recommendations (28), and arranging follow-up (27). On average students screened 33 patients per month, and, per patient, required 8.6 minutes for eligibility assessment and approximately 6 minutes for telephone interviews. Conclusion: This report demonstrates that pre-APPE student pharmacists are well-equipped to design and implement PHM projects. Utilization of student pharmacists in similar PHM programs can expand the pharmacist’s impact on patient care in the ambulatory care setting. Article Type: Original Research

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Hitsman, Brian, Phoenix A. Matthews, George D. Papandonatos, Kenzie A. Cameron, Sarah S. Rittner, Nivedita Mohanty, Timothy Long, et al. "An EHR-automated and theory-based population health management intervention for smoking cessation in diverse low-income patients of safety-net health centers: a pilot randomized controlled trial." Translational Behavioral Medicine 12, no. 9 (September 1, 2022): 892–99. http://dx.doi.org/10.1093/tbm/ibac026.

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Abstract This study tested the preliminary effectiveness of an electronic health record (EHR)-automated population health management (PHM) intervention for smoking cessation among adult patients of a federally qualified health center in Chicago. Participants (N = 190; 64.7% women, 82.1% African American/Black, 8.4% Hispanic/Latino) were self-identified as smokers, as documented in the EHR, who completed the baseline survey of a longitudinal “needs assessment of health behaviors to strengthen health programs and services.” Four weeks later, participants were randomly assigned to the PHM intervention (N = 97) or enhanced usual care (EUC; N = 93). PHM participants were mailed a single-page self-determination theory (SDT)-informed letter that encouraged smoking cessation or reduction as an initial step. The letter also addressed low health literacy and low income. PHM participants also received automated text messages on days 1, 5, 8, 11, and 20 after the mailed letter. Two weeks after mailing, participants were called by the Illinois Tobacco Quitline. EUC participants were e-referred following a usual practice. Participants reached by the quitline were offered behavioral counseling and nicotine replacement therapy. Outcome assessments were conducted at weeks 6, 14, and 28 after the mailed letter. Primary outcomes were treatment engagement, utilization, and self-reported smoking cessation. In the PHM arm, 25.8% of participants engaged in treatment, 21.6% used treatment, and 16.3% were abstinent at 28 weeks. This contrasts with no quitline engagement among EUC participants, and a 6.4% abstinence rate. A PHM approach that can reach all patients who smoke and address unique barriers for low-income individuals may be a critical supplement to clinic-based care.

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Ma, Hao Dong, Hong Zheng Fang, Shi Meng Cui, and Yi Xiong. "The Study of Prognostics and Health Management for the Air Cooling Equipments Based on FMA and Life Test." Applied Mechanics and Materials 313-314 (March 2013): 717–23. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.717.

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The demands of Prognostics and Health Management (PHM) for the air cooling equipment are constantly growing in the industrial field. However, most studies on PHM of fans are still in the exploratory stage, the failure modes and working conditions are mostly single and ideal. It proposed a PHM method for the brushless DC fan. Firstly it makes an analysis on the failure mode of the air cooling equipment. Secondly, two kinds of the prognostic tests are made, including fault simulation test and accelerated life test. After that, a prognostics method based-on the combination of reliability and data-driven models are proposed. The test results show that the proposed prediction method has a strong practical value.

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30

Omri, Nabil, Zeina Al Masry, Nicolas Mairot, Sylvian Giampiccolo, and Noureddine Zerhouni. "X-PHM: Prognostics and health management knowledge-based framework for SME." Procedia CIRP 104 (2021): 1595–600. http://dx.doi.org/10.1016/j.procir.2021.11.269.

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Baybutt, M., M. Baybutt, C. Minnella, A. E. Ginart, P. W. Kalgren, M. J. Roemer, and M. J. Roemer. "Improving Digital System Diagnostics Through Prognostic and Health Management (PHM) Technology." IEEE Transactions on Instrumentation and Measurement 58, no. 2 (February 2009): 255–62. http://dx.doi.org/10.1109/tim.2008.2005966.

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Vohnout, Sonia, Matt Engelman, and Eniko Enikov. "Miniature MEMS-based data recorder for prognostics and health management (PHM)." IEEE Instrumentation & Measurement Magazine 14, no. 4 (August 2011): 18–26. http://dx.doi.org/10.1109/mim.2011.5961365.

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Dicianno, Brad E., Andrea D. Fairman, Michael McCue, Bambang Parmanto, Erika Yih, Andrew McCoy, Gede Pramana, et al. "Feasibility of Using Mobile Health to Promote Self-Management in Spina Bifida." American Journal of Physical Medicine & Rehabilitation 95, no. 6 (June 2016): 425–37. http://dx.doi.org/10.1097/phm.0000000000000400.

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Sundaram, Sarvesh, and Abe Zeid. "Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework." Sensors 21, no. 18 (September 7, 2021): 5994. http://dx.doi.org/10.3390/s21185994.

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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

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Yao, Liang, and Jing Sun. "The Study of Prognostic and Health Management System for Smart Grid Based on Wireless Sensor Networks." Applied Mechanics and Materials 719-720 (January 2015): 426–30. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.426.

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This paper studies prognostic and health management (PHM) system for smart grid based on wireless sensor networks. The system function is analyzed firstly. Then, problems like networking hardware selection and data processing method have been discussed. The application of data fusion is discussed specially. In the end, the system platform based on a data-centric hierarchical technology architecture is designed with four layers: network configuration layer, network communication layer, platform basis layer and application layer. The research work of this paper is theoretical support for the realization of PHM for smart grid and the application of WSNs in smart grid.

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Tian, Zhi Xing, Zhang Wei, Li Na Hao, Shu Bing Liu, and Bing Yin Qu. "Preliminary Study of PHM System Based on Data Driven." Applied Mechanics and Materials 868 (July 2017): 299–304. http://dx.doi.org/10.4028/www.scientific.net/amm.868.299.

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Complex mechanical and electrical equipment contains a large amount of data with the implicit information. According to the development of PHM (Prognostics and Health Management) technology at home and abroad, and the wide application prospects of data driving methods, the overall framework of data driven PHM system for complex electromechanical equipment was designed. The data driven PHM implementation process of the complex mechanical and electrical equipment was described step by step, which provides important theoretical significance and application value for the PHM research of the complex mechanical and electrical equipment. Finally, the development trend and research challenges of data driven PHM method were analyzed.

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Wang, Pengjun, Jiahao Qin, Jiucheng Li, Meng Wu, Shan Zhou, and Le Feng. "Device Status Evaluation Method Based on Deep Learning for PHM Scenarios." Electronics 12, no. 3 (February 3, 2023): 779. http://dx.doi.org/10.3390/electronics12030779.

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The emergence of fault prediction and health management (PHM) technology has proposed a new solution and is suitable for implementing the functions of improving the intelligent management and control system. However, the research and application of the PHM model in the intelligent management and control system of electronic equipment are few at present, and there are many problems that need to be solved urgently in PHM technology itself. In order to solve such problems, this paper studies the application of the equipment-status-assessment method based on deep learning in PHM scenarios, in order to conduct in-depth research on the intelligent control system of electronic equipment. The experimental results in this paper show that the change in unimproved deep learning is very subtle before the performance change point, while improvements in deep learning increase the health value by about 10 times. Thus, improved deep learning amplifies subtle changes in health early in degradation and slows down mutations in health late at performance failure points. At the same time, comparing health-index-evaluation indicators, it can be concluded that although the monotonicity of the health index is low, its robustness and correlation are significantly improved. Additionally, it is very close to 1, making the health index curve more in line with traditional cognition and convenient for application. Therefore, an in-depth study of methods for health assessment by improving deep learning is of practical significance.

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Calabrese, Francesca, Alberto Regattieri, Marco Bortolini, Mauro Gamberi, and Francesco Pilati. "Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries." Applied Sciences 11, no. 8 (April 9, 2021): 3380. http://dx.doi.org/10.3390/app11083380.

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Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.

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Moradi, Ramin, and Katrina Groth. "On the application of transfer learning in prognostics and health management." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1300.

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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 modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, specially 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 TL application considerations and gaps is provided for improving the applicability of transfer learning in PHM.

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Wilson, Kathryn E., Tzeyu L. Michaud, Fabio A. Almeida, Robert J. Schwab, Gwenndolyn C. Porter, Kathryn H. Aquilina, Fabiana A. Brito, et al. "Using a population health management approach to enroll participants in a diabetes prevention trial: reach outcomes from the PREDICTS randomized clinical trial." Translational Behavioral Medicine 11, no. 5 (March 2, 2021): 1066–77. http://dx.doi.org/10.1093/tbm/ibab010.

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Abstract Population health management (PHM) strategies to address diabetes prevention have the potential to engage large numbers of at-risk individuals in a short duration. We examined a PHM approach to recruit participants to a diabetes prevention clinical trial in a metropolitan health system. We examined reach and representativeness and assessed differences from active and passive respondents to recruitment outreach, and participants enrolled through two clinical screening protocols. The PHM approach included an electronic health record (EHR) query, physician review of identified patients, letter invitation, and telephone follow-up. Data describe the reach and representativeness of potential participants at multiple stages during the recruitment process. Subgroup analyses examined proportional reach, participant differences based on passive versus active recruitment response, and clinical screening method used to determine diabetes risk status. The PHM approach identified 10,177 potential participants to receive a physician letter invitation, 60% were contacted by telephone, 2,796 (46%) completed telephone screening, 1,961 were eligible from telephone screen, and 599 were enrolled in 15 months. Accrual was unaffected by shifting clinical screening protocols despite the increase in participant burden. Relative to census data, study participants were more likely to be obese, female, older, and Caucasian. Relative to the patient population, enrolled participants were less likely to be Black and were older. Active respondents were more likely to have a higher income than passive responders. PHM strategies have the potential to reach a large number of participants in a relatively short period, though concerted efforts are needed to increase participant diversity.

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Saidi, Lotfi, and Mohamed Benbouzid. "Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends." Electronics 10, no. 22 (November 9, 2021): 2732. http://dx.doi.org/10.3390/electronics10222732.

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The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain

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Wen, Zhen Hua, Yuan Peng Liu, and Xin Yin. "Testability Design of the PHM System for Aero-Engines." Advanced Materials Research 544 (June 2012): 94–98. http://dx.doi.org/10.4028/www.scientific.net/amr.544.94.

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PHM (Prognostics and Health Management) for Aero-engines is an effective technical approach to balance the economy and safety of the flight in the total life cycle. In this paper, we mainly analyze the popular issues in the process of designing PHM system for aero-engines including the testability design concept, the scheme of condition monitoring and the utilization extent of condition information. Then presents some useful solutions and advices for the testability design respectively; and analyzes the influence of testability on health management strategies and the main source of uncertainty; then propose a roadmap for making test program based on the PHM requirements and evaluating test program, for improve the utilizing degree of monitoring information, we lastly presented common data fusion methods and some typical examples is illustrated.

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Gribbestad, Magnus, Muhammad Umair Hassan, and Ibrahim A. Hameed. "Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors." Journal of Marine Science and Engineering 9, no. 1 (January 4, 2021): 47. http://dx.doi.org/10.3390/jmse9010047.

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Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

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Abbas, Manzar. "Prognostics and Health Management (PHM) - State-of-the-art in Predictive Maintenance." International Conference on Electrical Engineering 9, no. 9th (May 1, 2014): 1. http://dx.doi.org/10.21608/iceeng.2014.30501.

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Moumtzoglou, Anastasius, and Abraham Pouliakis. "Population Health Management and the Science of Individuality." International Journal of Reliable and Quality E-Healthcare 7, no. 2 (April 2018): 1–26. http://dx.doi.org/10.4018/ijrqeh.2018040101.

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This article espouses that population health management (PHM) has been a discipline which studies and facilitates care delivery across a group of individuals or the general population. In the context of population health management, the life science industry has had no motivation to design drugs or devices that are only effective for a distinct segment of the population. The major outgrowth of the science of individuality, as well as the rising ‘wiki medicine', fully recognizes the uniqueness of the individual. Cloud computing, Big Data and M-Health technologies offer the resources to deal with the shortcomings of the population health management approach, as they facilitate the propagation of the science of individuality.

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Li, Anyi, Xiaohui Yang, Huanyu Dong, Zihao Xie, and Chunsheng Yang. "Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM." Sensors 18, no. 12 (December 14, 2018): 4430. http://dx.doi.org/10.3390/s18124430.

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An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

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Duncan, Monica. "Population health management and its relevance to community nurses." British Journal of Community Nursing 24, no. 12 (December 2, 2019): 596–99. http://dx.doi.org/10.12968/bjcn.2019.24.12.596.

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Local services can provide better and more joined-up care for patients when different organisations work collaboratively in an integrated system. Population health management (PHM) provides the shared data about local people's current and future health and wellbeing needs. Joint care planning and support addresses both the psychological and physical needs of an individual recognising the huge overlap between mental and physical wellbeing. Joint posts and joint organisational development are likely to become more commonplace and community nurses will have a vital contribution to planning and delivery of integrated care to improve health and care outcomes for their local populations.

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Sim, Jinwoo, Seokgoo Kim, Hyung Jun Park, and Joo-Ho Choi. "A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings." Applied Sciences 10, no. 16 (August 14, 2020): 5639. http://dx.doi.org/10.3390/app10165639.

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Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.

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Chang, Jiang, and Fang Wei. "Prognostics and Health Management of Mechanical Systems." Advanced Materials Research 694-697 (May 2013): 872–75. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.872.

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Reliability is an important issue to consider for mechanical systems. The state of art is regular checkup and maintenance to ensure normal operations. This is not good enough for safety-critical systems like gearboxes in vehicles and helicopters because the risk of system failure still exists, let alone the manpower and monetary cost required. Prognostics and health management (PHM) was first raised by the U.S. armed force, which should ideally be able to predict faults and schedule maintenance only when necessary by monitoring the system condition. In this paper, inspired by the idea of Built-In Self Test (BIST) in electronic systems, we propose a novel framework to fulfill the task of prognostics and health management with a set of smart sensors, consisting of embedded sensing elements, wireless communication modules and micro-controllers. Both the significance and challenges of the framework are discussed.

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Cofre-Martel, Sergio, Enrique Lopez Droguett, and Mohammad Modarres. "Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management." Sensors 21, no. 20 (October 14, 2021): 6841. http://dx.doi.org/10.3390/s21206841.

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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 data-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 applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

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