A Hierarchical Model-based approach to Systems Health Management

Biswas, Gautam; Mahadevan, Sankaran

doi:10.1109/aero.2007.352943

Cited by 21 publications

(12 citation statements)

References 24 publications

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“…The most significant damage mechanism for pumps is impeller wear, represented as a decrease in impeller area A [20,21]. Since the impeller area is proportional to b 0 , a decrease causes a decrease in the pump pressure, and, hence, the pump efficiency.…”

Section: Pump Modelingmentioning

confidence: 99%

A comparison of filter-based approaches for model-based prognostics

Daigle

Saha

Goebel

2012

2012 IEEE Aerospace Conference

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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 stateparameter 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 through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

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Section: Pump Modelingmentioning

confidence: 99%

A comparison of filter-based approaches for model-based prognostics

Daigle

Saha

Goebel

2012

2012 IEEE Aerospace Conference

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“…The parameter b 0 is proportional to impeller area A [22]. Flow through the impeller, Q i , is computed using the pressure differences:…”

Section: A Nominal Modelmentioning

confidence: 99%

“…It is represented as a decrease in impeller area A [22], [23]. Since the impeller area is proportional to b 0 , a decrease in impeller area causes a decrease in the pump pressure, and, hence, the pump efficiency.…”

Section: B Damage Modelingmentioning

confidence: 99%

Distributed Prognostics Based on Structural Model Decomposition

2014

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Abstract-Within systems health management, prognostics focuses 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 prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.

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“…These functions are parameterized by a set of unknown wear parameters, forming the unknown parameter vector θ(t). Impeller wear is represented as a decrease in impeller area A [7,9]. We use the erosive wear equation [12].…”

Section: Damage Modelingmentioning

confidence: 99%

“…Because pumps typically see high usage, they can particularly benefit from prognostics and health management solutions to ensure satisfactory system performance, extended component lifetime, and limited downtime. Model-based diagnosis has been investigated previously with centrifugal pumps [5][6][7]. However, most prognostics approaches for pumps have been data-driven, usually based on pump vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Multiple damage progression paths in model-based prognostics

Daigle

Goebel

2011

2011 Aerospace Conference

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Abstract-Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our modelbased prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.

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A Hierarchical Model-based approach to Systems Health Management

Cited by 21 publications

References 24 publications

A comparison of filter-based approaches for model-based prognostics

A comparison of filter-based approaches for model-based prognostics

Distributed Prognostics Based on Structural Model Decomposition

Multiple damage progression paths in model-based prognostics

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