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.

iFEED: Interactive Feature Extraction for Engineering Design

2016 ◽  
Author(s):  
Hyunseung Bang ◽  
Daniel Selva
Keyword(s):  
Knowledge Discovery ◽  
Design Space ◽  
Information Overload ◽  
Human Subjects ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
Trade Offs ◽  
Design Variables ◽  
Design Selection ◽  
High Level

One of the major challenges faced by the decision maker in the design of complex engineering systems is information overload. When the size and dimensionality of the data exceeds a certain level, a designer may become overwhelmed and no longer be able to perceive and analyze the underlying dynamics of the design problem at hand, which can result in premature or poor design selection. There exist various knowledge discovery and visual analytic tools designed to relieve the information overload, such as BrickViz, Cloud Visualization, ATSV, and LIVE, to name a few. However, most of them do not explicitly support the discovery of key knowledge about the mapping between the design space and the objective space, such as the set of high-level design features that drive most of the trade-offs between objectives. In this paper, we introduce a new interactive method, called iFEED, that supports the designer in the process of high-level knowledge discovery in a large, multiobjective design space. The primary goal of the method is to iteratively mine the design space dataset for driving features, i.e., combinations of design variables that appear to consistently drive designs towards specific target regions in the design space set by the user. This is implemented using a data mining algorithm that mines interesting patterns in the form of association rules. The extracted patterns are then used to build a surrogate classification model based on a decision tree that predicts whether a design is likely to be located in the target region of the tradespace or not. Higher level features will generate more compact classification trees while improving classification accuracy. If the mined features are not satisfactory, the user can go back to the first step and extract higher level features. Such iterative process helps the user to gain insights and build a mental model of how design variables are mapped into objective values. A controlled experiment with human subjects is designed to test the effectiveness of the proposed method. A preliminary result from the pilot experiment is presented.

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.

An Example Performance Trade-Off Evaluation Using the Integration Framework for Architecture Development (IFAD) for Conceptual Vehicle Design

2005 ◽  
Author(s):  
Xiaoyu Gu ◽  
Peter A. Fenyes
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Design ◽  
Vehicle Performance ◽  
Integration Framework ◽  
Trade Offs ◽  
Design Variables ◽  
Design Changes ◽  
High Level ◽  
Architecture Development ◽  
Geometrical Dimensions

The Integration Framework for Architecture Development (IFAD) is an integrated framework that provides fast and consistent discipline analysis results and identifies discipline consequences corresponding to vehicle design changes. This information is valuable for balancing and integration in the early design phase. In this paper, the IFAD framework is utilized to conduct an example multi-objective multi-disciplinary optimization to evaluate vehicle performance trade-offs for a hypothetical vehicle. We consider design changes on high-level geometrical dimensions including front overhang, rear overhang and vehicle width at rocker. We also study vehicle configurations including choice of materials and tires and choice of powertrains. A commonly used multi-objective genetic algorithm (MOGA) technique, Non-dominated Sorting Genetic Algorithm (NSGAII [1]) is chosen because of the mixed types of design variables involved (i.e., continuous design variables representing high-level geometrical dimensions and discrete design variables representing vehicle configurations such as powertrain selection and material choice). Vehicle performance analyses in a range of disciplines such as geometry, aerodynamics and energy are carried out automatically through IFAD. The use of response surface modeling (RSM) is desired due to the large number of evaluations typical for a MOGA application. A comparison of the engineering performance trade-offs based on two different sets of performance objectives is presented.

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 On the application of transfer learning in prognostics and health management

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Ramin Moradi ◽  
Katrina Groth
Keyword(s):  
Transfer Learning ◽  
Data Storage ◽  
Health Management ◽  
Operating Conditions ◽  
Data Availability ◽  
Training Data ◽  
Fine Tuning ◽  
Prognostics And Health Management ◽  
Big Data Storage ◽  
Previous Training

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.

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

Pakistan's Export Possibilities

1973 ◽  
Vol 12 (1) ◽  
pp. 1-30
Author(s):  
Syed Nawab Haider Naqvi
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Policy Maker ◽  
Time Horizon ◽  
Price Level ◽  
Balance Of Payments ◽  
Domestic Saving ◽  
Trade Offs ◽  
High Level

The recent uncertainties about aid flows have underscored the need for achieving an early independence from foreign aid. The Perspective Plan (1,965-85) had envisaged the termination of Pakistan's dependence on foreign aid by 1985. However, in the context of West Pakistan alone the time horizon can now be advanced by several years with considerable confidence in its economy to pull the trick. The difficulties of achieving independence from foreign aid can be seen by reference to the fact that aid flows make it possible for the policy-maker to pursue such ostensibly incompatible objectives as a balance in international payments (i.e., foreign aid finances the balance of payments), higher rates of economic growth (Lei, it pulls up domestic saving and investment levels), a high level of employment (i.e., it keeps the industries working at a fuller capacity than would otherwise be the case), and a reasonably stable price level (i.e., it lets a higher level of imports than would otherwise be possible). Without aid, then a simultaneous attainment of all these objectives at the former higher levels together with the balance in foreign payments may become well-nigh impos¬sible. Choices are, therefore, inevitable not for definite places in the hierarchy of values, but rather for occasional "trade-offs". That is to say, we will have to" choose how much to sacrifice for the attainment of one goal for the sake of somewhat better realization of another.

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.

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