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.

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.

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.

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.

scholarly journals An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data

2021 ◽  
Vol 11 (11) ◽  
pp. 5180
Author(s):  
Donghwan Kim ◽  
Seungchul Lee ◽  
Daeyoung Kim
Keyword(s):  
Health Management ◽  
Real Life ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Prognostics And Health Management ◽  
Health Index ◽  
Computing Technology ◽  
Simulation Data ◽  
Failure Data ◽  
Useful Life

As technology advances, the equipment becomes more complicated, and the importance of the Prognostics and Health Management (PHM) to monitor the condition of the equipment has risen. In recent years, various methodologies have emerged. With the development of computing technology, methodologies using machine learning and deep learning are gaining attention, in particular. As these algorithms become more advanced, the performance of detecting anomalies and predicting failures has improved dramatically. However, most of the studies are cases that depend on simulation data or assumed abnormal conditions. In addition, regardless of the existence of run-to-failure data, the methodologies are difficult to apply to the industrial site directly. To solve this problem, we propose a Predictive Maintenance (PdM) framework based on unsupervised learning in this paper, which can be applied directly in the industrial field regardless of run-to-failure data. The proposed framework consists of data acquisition, preprocessing data, constructing a Health Index, and predicting the remaining useful life. We propose a framework that can create and monitor models even when there are no accumulated run-to-failure data. The proposed framework was conducted in two different real-life cases, and the usefulness and applicability of the proposed methodology were verified.

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.

Prognostics and Health Management Software for Gas Turbine Engine Bearings

2007 ◽  
Author(s):  
Michael J. Roemer ◽  
Carl S. Byington
Keyword(s):  
Gas Turbine ◽  
High Speed ◽  
Health Management ◽  
Gas Turbine Engine ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Turbine Engine ◽  
Model Based ◽  
Useful Life ◽  
Specific Implementation

Based on the results of a successful Phase I and II SBIR program performed by Impact Technologies, a suite of Prognostics and Health Management (PHM) algorithms have been developed for detecting incipient faults in the critical bearings associated with aircraft gas turbine engines. The component-level prognostic approach is presented that utilizes available sensor information from vibration transducers, along with material-level component fatigue models to calculate remaining useful life for the engine’s critical components. Specifically, correlation between the sensed data and fatigue-based damage accumulation models were developed to provide remaining useful life assessments for life limited components. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearings’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored vibration features at later stages when incipient failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and specific implementation of this prognosis approach with application to high speed bearings is illustrated herein, using gas turbine engine and bearing test rig data as validation for the methods.

A data-driven intelligent hybrid method for health prognosis of lithium-ion batteries

2021 ◽  
pp. 1-11
Author(s):  
Vimal Singh Bisht ◽  
Mashhood Hasan ◽  
Hasmat Malik ◽  
Sandeep Sunori
Keyword(s):  
Health Assessment ◽  
Lithium Ion ◽  
Transformation Method ◽  
Internal Resistance ◽  
Remaining Useful Life ◽  
Health Index ◽  
Great Saving ◽  
Useful Life ◽  
And Storage ◽  
Value Of Health

For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time

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