scholarly journals Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Xiaoning Jin ◽  
Brian A. Weiss ◽  
David Siegel ◽  
Jay Lee
Keyword(s):  
Health Management ◽  
Manufacturing Firms ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Present Level ◽  
Maintenance Strategies ◽  
Manufacturing Enterprises ◽  
Specific Factors ◽  
The Impact ◽  
Future Growth

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Prognostics and Health Management of Wind Energy Infrastructure Systems

2022 ◽  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Data Augmentation ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Quality Of Data ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
The Impact

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

scholarly journals A Framework for System Design Optimization Based on Maintenance Scheduling With Prognostics and Health Management

2013 ◽  
Author(s):  
Bo Yang Yu ◽  
Tomonori Honda ◽  
Syed Zubair ◽  
Mostafa H. Sharqawy ◽  
Maria C. Yang
Keyword(s):  
Health Management ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Prognostics And Health Management ◽  
Maintenance Strategies ◽  
Design Component ◽  
Trade Offs ◽  
Design Variables ◽  
System Design Optimization ◽  
High Level

The optimal maintenance scheduling of systems with degrading components is highly coupled with the design of the system and various uncertainties associated with the system, including the operating conditions, the interaction of different degradation profiles of various system components, and the ability to measure and predict degradation using prognostics and health management (PHM) technologies. Due to this complexity, designers need to understand the correlations and feedback between the design variables and lifecycle parameters to make optimal decisions. A framework is proposed for the high level integration of design, component degradation, and maintenance decisions. The framework includes constructing screening models for rapid design evaluation, defining a multi-objective robust optimization problem, and using sensitivity studies to compare trade-offs between different design and maintenance strategies. A case example of power plant condenser is used to illustrate the proposed framework and advise how designers can make informed comparisons between different design concepts and maintenance strategies under highly uncertain lifecycle conditions.

Maintenance for Improving Manufacturing Equipments Availability Using Prognostics and Health Management

2011 ◽  
Vol 199-200 ◽  
pp. 543-547 ◽  
Author(s):  
Jiang Long ◽  
Wei An Jiang
Keyword(s):  
Health Monitoring ◽  
Health Management ◽  
Cost Effective ◽  
Prognostics And Health Management ◽  
Maintenance Strategy ◽  
Maintenance Strategies ◽  
Key Issues ◽  
Reliability And Availability

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.

scholarly journals Uncertainty in PHM

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan ◽  
Marcos E. Orchard
Keyword(s):  
Health Management ◽  
Research Work ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Engineering Systems ◽  
Sources Of Uncertainty ◽  
Arduous Task ◽  
Operational Decision Making ◽  
Inaccurate Estimation ◽  
The Impact

Uncertainty plays an important role in diagnostics, prognostics, and health management of engineering systems. The presence of uncertainty leads to an imprecise understanding of the behavior of such systems; as a result, this may adversely affect the results of diagnostics and prognostics. In particular, this may lead to an inaccurate estimation of the remaining useful life, which in turn affects operational decision-making. While several researchers have recognized the importance of uncertainty in prognostics and health management (PHM), there has not been a significant amount of research work that addresses the impact of uncertainty in different PHM activities. This is challenging because there are various sources of uncertainty that affect PHM, their interactions are not fully understood, and therefore, it is an arduous task to perform different PHM activities by systematically accounting for these sources of uncertainty. However, when this can be accomplished, it would be possible to estimate the uncertainty and confidence in the results of diagnostics and prognostics, and quantify the risk involved in prognostics-based decisionmaking.

scholarly journals Review of PHM Data Competitions from 2008 to 2017

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaodong Jia ◽  
Bin Huang ◽  
Jianshe Feng ◽  
Haoshu Cai ◽  
Jay Lee
Keyword(s):  
Future Development ◽  
Health Management ◽  
Failure Detection ◽  
Data Driven ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Development Cost ◽  
Major Research ◽  
Industrial Sectors ◽  
Public Resources

Recently, the data driven approaches are winning popularity in Prognostics and Health Management (PHM) community due to its great scalability, reconfigurability and the reduced development cost. As the data-driven approaches flourished, the data competitions hosted by the PHM society over the last ten years contribute a valuable repository of public resources for benchmarks and improvements.  To better define the directions for future development, this paper reviews the cutting-edge PHM methodologies and analytics based on the data competitions over the last decade. In this review, the goal of PHM and the major research tasks are stated and depicted, then the methodologies and analytics for the PHM practices are summarized in terms of failure detection, diagnosis, assessment and prediction, and the applications of PHM in various industrial sectors are highlighted as well. The data competitions in the last ten years are utilized as examples and case studies to support the ideas presented in this paper. Based on all the discussions and reviews, the current challenges and future opportunities are pointed out, and a conclusion remark is given at the end of the paper to summarize the current achievements and to foresee the future trends.

What kind of Russian firms get state support? The analysis of changes in priorities under crisis conditions

2020 ◽  
pp. 47-62
Author(s):  
Andrei A. Yakovlev ◽  
Nina V. Ershova ◽  
Olga M. Uvarova
Keyword(s):  
Social Development ◽  
Organizational Support ◽  
Manufacturing Firms ◽  
Business Associations ◽  
State Support ◽  
Support Provision ◽  
Manufacturing Enterprises ◽  
State Participation ◽  
Crisis Conditions ◽  
Different Levels

The paper analyzes the shifts in government priorities in terms of support of big and medium manufacturing enterprises amid 2008—2009 and 2014—2015 crises. Based on the data of 2009, 2014 and 2018 surveys of Russian manufacturing firms, using logit regressions we identify factors that affect the receipt of financial and organizational support at different levels of government. The analysis shows that in 2012—2013 the share of manufacturing firms that received state support shrank significantly as compared to 2007—2008; moreover, the support concentrated on enterprises that had access to lobbying resource (such as state participation in the ownership or business associations membership). In 2016—2017 the scale of state support coverage recovered. However, the support at all levels of government was provided to firms that carried out investment and provided assistance to regional or local authorities in social development of the region, while the factor of state participation in the ownership became insignificant. The paper provides possible explanation for these shifts in the criteria of state support provision in Russia.

Voices of Distress: The Emotional Peril of Not Attaining a Dietetic Internship in Ontario

2013 ◽  
Vol 1 (3) ◽  
pp. 9
Author(s):  
Jennifer Lee Brady ◽  
Annie Hoang ◽  
Olivia Siswanto ◽  
Jordana Riesel ◽  
Jacqui Gingras
Keyword(s):  
Empirical Research ◽  
Current Model ◽  
Well Being ◽  
Structured Interview ◽  
Application Process ◽  
Dietetic Education ◽  
Dietetic Internship ◽  
The Impact ◽  
And Training ◽  
Future Growth

Obtaining dietetic licensure in Ontario requires completion of a Dietitians of Canada (DC) accredited four-year undergraduate degree in nutrition and an accredited post-graduate internship or combined Master’s degree program. Given the scarcity of internship positions in Ontario, each year approximately two-thirds of the eligible applicants who apply do not receive a position XX, XX, XX, XX, XX, XX, in press). Anecdotally, not securing an internship position is known to be a particularly disconcerting experience that has significant consequences for individuals’ personal, financial, and professional well-being. However, no known empirical research has yet explored students’ experiences of being unsuccessful in applying for internship positions. Fifteen individuals who applied between 2005 and 2009 to an Ontario-based dietetic internship program, but were unsuccessful at least once, participated in a one-on-one semi-structured interview. Findings reveal that participants’ experiences unfold successively in four phases that are characterized by increasingly heightened emotional peril: naïveté, competition, devastation, and frustration. The authors conclude that the current model of dietetic education and training in Ontario causes lasting distress to students and hinders the future growth and vitality of the dietetic profession. Further research is required to understand the impact of the current model on dietetic educators, internship coordinators, and preceptors as coincident participants in the internship application process.

A Review on Prognostics and Health Management : 2013~2018

2019 ◽  
Vol 19 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Hyun Su Sim ◽  
Jun-Gyu Kang ◽  
Yong Soo Kim
Keyword(s):  
Health Management ◽  
Prognostics And Health Management
Sign in / Sign up

Export Citation Format

Share Document