Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
Charlie DeStefano ◽  
David Jensen
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Health Management ◽  
Mission Planning ◽  
Life Extension ◽  
Prognostics And Health Management ◽  
Planning Strategy ◽  
Future Performance ◽  
Useful Life ◽  
Maintenance Schedules

Abstract This paper presents a novel Fault Adaptive Mission Planning (FAMP) framework for complex systems aimed at increasing useful-life and reducing downtime through condition-based decision-making. A hallmark of complex systems is that they typically have access to multiple mission plans that allow their mission objectives to be accomplished in a variety of ways. In hopes of exploiting this characteristic, FAMP is the process of increasing a system's useful-lifespan by first determining how each potential mission plan affects the system's degradation differently, and then by implementing a planning strategy that utilizes this information to repeatedly recalculate a new mission plan as the system degrades. Fault-augmented physics models identify how component degradation will affect the system's current and future performance for a given mission plan. Then, at various degradation-based thresholds, new mission plans are installed such that whenever possible, the healthiest components are used more, or in different ways, than the more degraded components. This process promotes balanced degradation, preventing useful-life from being wasted and reducing downtime through synchronized maintenance schedules. This work expands the prognostics and health management paradigm by enabling life extension and maintenance reduction through real-time FAMP.

scholarly journals Enhancing Decisions in Prognostics and Health Management Framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Omar Bougacha ◽  
Christophe Varnier
Keyword(s):  
Decision Making ◽  
Closed Loop ◽  
Health Management ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Management Framework ◽  
Useful Life ◽  
Research Studies

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplifiedexample is developed to illustrate the presented methodology of post-prognostic decision enhancement.

scholarly journals Review of Post-Prognostics Decision-Making in Prognostics and Health Management

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Omar Bougacha ◽  
Christophe Varnier ◽  
Noureddine Zerhouni
Keyword(s):  
Decision Making ◽  
Health Management ◽  
Health Assessment ◽  
Remaining Useful Life ◽  
Assessment Process ◽  
Decision Making Process ◽  
Prognostics And Health Management ◽  
Useful Life ◽  
Future Work ◽  
Term Management

Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.

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.

Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction

2021 ◽  
Author(s):  
Mohammad Rubyet Islam ◽  
Peter Sandborn
Keyword(s):  
Health Management ◽  
Standard Test ◽  
Remaining Useful Life ◽  
Software Systems ◽  
Prognostics And Health Management ◽  
Performance Parameters ◽  
Statistical Validation ◽  
Useful Life ◽  
Test Beds

Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

scholarly journals Physics Based Deep Learning Technique for Prognostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Khaled Akkad
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Health Management ◽  
Recognition Performance ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Learning Technique ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

Research on Complex System of Prognostics and Health Management

2014 ◽  
Vol 644-650 ◽  
pp. 899-902
Author(s):  
Xiao Ming Li ◽  
Xue Dong Lin ◽  
De Gang Li ◽  
Zhi Shu Feng ◽  
Peng Zhao
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Health Management ◽  
Condition Based Maintenance ◽  
Prognostics And Health Management ◽  
Integrated Framework ◽  
Applied Technology ◽  
Management Techniques ◽  
Management Capabilities

With the increasingly large and complex systems issues in the aspect of reliability, availability, economy and maintenance, the advantages of condition based maintenance become more and more marked which requires the system itself has the prognostics and health management capabilities. Based on this, the integrated framework and applied technology of prognostics and health management to complex systems are studied, and the advantages of prognostics and health management techniques are summarized.

Identifying Parameters for Nonlinear Degradation-Based Prognostics

2019 ◽  
pp. 237-260
Author(s):  
Feng Yang ◽  
Mohamed Salahuddin
Keyword(s):  
Parameter Identification ◽  
Induction Motors ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Health Index ◽  
Identification Method ◽  
Useful Life ◽  
Parameter Values

Prognostics and health management (PHM) methodologies are increasingly playing active roles in improving the availability, reliability, efficiency, productivity, and safety of systems in many industries. In predicting the remaining useful life (RUL), this chapter introduces a prognostics framework with health index (HI) formulation, with specific emphasis on incorporating and validating nonlinear HI degradations. The key issue to the success of this framework is how to identify appropriate parameters in describing the behavior of the nonlinear HI degradations. Using exponential HI degradation as an example in predicting the RULs of induction motors, this chapter discusses three different explorations in verifying the existence of good parameter values as well as identifying the appropriate parameters automatically. Comprehensive experiments were carried out with degradation process (DP) data from eight induction motors, and it was discovered that good parameters can be automatically determined with the proposed parameter identification method.

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