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A Comparison of Filter-based Approaches for Model-based
- Prognostics,” 2012 IEEE Aerospace Conference
, 2012
"... Abstract — Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health ..."
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Cited by 22 (19 self)
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Abstract — Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities
An efficient deterministic approach to modelbased prediction uncertainty estimation
- In Annual conference of the
, 2012
"... Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the sys-tem. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know ..."
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Cited by 17 (14 self)
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Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the sys-tem. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajecto-ries of a system by using the unscented transform. Using this approach, we are not only able to reduce the computa-tional load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experi-mental data from a battery testbed. 1.
A model-based prognostics approach applied to pneumatic valves
- International Journal of the PHM Society
, 2011
"... Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is deri ..."
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Cited by 16 (13 self)
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Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general modelbased prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system. 1
Distributed damage estimation for prognostics based on structural model decomposition
- In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011
, 2011
"... Model-based prognostics approaches capture system knowl-edge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is p ..."
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Cited by 9 (7 self)
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Model-based prognostics approaches capture system knowl-edge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decom-position approach adapted from the diagnosis community, called possible conflicts, in order to both improve the com-putational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state esti-mate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach. 1.
Advanced methods for determining prediction uncertainty in model-based prognostics with application to planetary rovers
- In Annual conference of the
, 2013
"... Prognostics is centered on predicting the time of and time un-til adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a pre-diction phase, in which the state is projected forward ..."
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Cited by 6 (5 self)
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Prognostics is centered on predicting the time of and time un-til adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a pre-diction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognos-tic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both character-ize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, in-cluding Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction. 1.
An integrated model-based distributed diagnosis and prognosis framework
- In Proceedings of the 23rd internaAnnual Conference of the Prognostics and Health Management Society 2012 tional
, 2012
"... Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec-tion, isolation and identification of faults, while prognosis consists of prediction of the remain-ing useful life of systems. This paper presents an inte ..."
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Cited by 1 (1 self)
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Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec-tion, isolation and identification of faults, while prognosis consists of prediction of the remain-ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi-tion is used to perform the diagnosis and prog-nosis tasks in a distributed way. We show how different submodels can be automatically con-structed to solve the local diagnosis and prog-nosis problems. We illustrate our approach us-ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner. 1
Global Sensitivity Analysis applied to Model Inversion Problems: A Contribution to Rail Condition Monitoring
, 2015
"... Rising demands on railroad infrastructure operator by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management. Condition based preven-tive maintenance aims at strengthening the rail mode of trans-port through an optimized scheduling of maintenance actio ..."
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Rising demands on railroad infrastructure operator by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management. Condition based preven-tive maintenance aims at strengthening the rail mode of trans-port through an optimized scheduling of maintenance actions based on the actual and prognosticated infrastructure condi-tion, respectively. When applying model-based algorithms within the framework of Prognostics and Health Management unknown model parameters have to be identified first. Which of these parameters should be known as precisely as possi-ble can be figured out systematically by a sensitivity analysis. A comprehensive global sensitivity analysis, however, might be prohibitive by means of computation load when standard algorithms are implemented. In this study, it is shown how global parameter sensitivities can be calculated efficiently by combining Polynomial Chaos Expansion and Point Estimate Method principles. The proposed framework is demonstrated by a model inversion problem which aims to recalculate the track quality by measurements of the vehicle acceleration, i.e. analyzing the dynamic railway track-vehicle interaction. 1.
1Model-based Prognostics with Concurrent Damage Progression Processes
"... Abstract—Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to ..."
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Abstract—Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation. We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that sup-ports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active. Index Terms—model-based prognostics, particle filters, vari-ance control, centrifugal pumps I.
1Distributed Prognostics Based on Structural Model Decomposition
"... Abstract—Within systems health management, prognostics fo-cuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, ..."
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Abstract—Within systems health management, prognostics fo-cuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prog-
Predicting Remaining Driving Time and Distance of a Planetary Rover Under Uncertainty
"... The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation, it is important to make reliable predictions regarding the end-of-discharge (EOD) time, which can be used to estimate the remaining driving t ..."
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The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation, it is important to make reliable predictions regarding the end-of-discharge (EOD) time, which can be used to estimate the remaining driving time (RDT) and remaining driving distance (RDD). These quantities are stochastic in nature, not only because there are several sources of uncertainty that affect the rover's operation but also since the future operating conditions cannot be known precisely. This paper presents a computational methodology to predict these stochastic quantities, based on a model of the rover and its batteries. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decisionmaking. We consider two different types of driving scenarios and develop methods for each to characterize the associated uncertainty. Monte Carlo sampling and the inverse first-order reliability method are used to compute the stochastic predictions of EOD time, RDT, and RDD.
