Multiple damage progression paths in model-based prognostics

Daigle, Matthew; Goebel, Kai

doi:10.1109/aero.2011.5747574

Cited by 52 publications

(72 citation statements)

References 13 publications

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“…In model-based prognostics [3], the system model is augmented to include these constraints, i.e., for M = (V, C), C EOL ⊂ C and the e i variables are included in V (as auxiliary variables in A). The causality of these constraints is always fixed such that for constraint c i , the causal assignment takes the following form:…”

Section: Model Decomposition For Predictionmentioning

confidence: 99%

“…Using the decomposition algorithm for the prediction problem, we find we cannot decompose the global model. 3 To compute any single pressure we need to compute also the other pressures. That is, each submodel has constraints with causal assignments α 2 -α 4 and α 20 -α 27 with the EOL constraint for that submodel.…”

Section: Model Decomposition For Predictionmentioning

confidence: 99%

“…Model-based SHM approaches [1][2][3][4] are usually favored over purely datadriven methods [5] when knowledge about the system and its behaviors is available. Model-based approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems.…”

Section: Introductionmentioning

confidence: 99%

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A structural model decomposition framework for systems health management

Roychoudhury

Daigle

Bregón

et al. 2013

2013 IEEE Aerospace Conference

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Abstract-Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

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Section: Model Decomposition For Predictionmentioning

confidence: 99%

Section: Model Decomposition For Predictionmentioning

confidence: 99%

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A structural model decomposition framework for systems health management

Roychoudhury

Daigle

Bregón

et al. 2013

2013 IEEE Aerospace Conference

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“…The parameter j eo is estimated online since it depends on the amount of electrolyte present in the capacitor, as the volume changes with time j eo changes and is estimated accordingly. Details on the methodology are presented in [15], [16]. 2) Prediction of Remaining Useful Life Results: Fig.…”

Section: B Prognostics Based On Thermal Overstress Experimentsmentioning

confidence: 99%

Prognostics of Power Electronics, methods and validation experiments

Kulkarni

Celaya

Biswas

et al. 2012

2012 IEEE AUTOTESTCON Proceedings

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Abstract-Failure of electronic devices is a concern for future electric aircrafts that will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. As a result, investigation of precursors to failure in electronics and prediction of remaining life of electronic components is of key importance. DC-DC power converters are power electronics systems employed typically as sourcing elements for avionics equipment. Current research efforts in prognostics for these power systems focuses on the identification of failure mechanisms and the development of accelerated aging methodologies and systems to accelerate the aging process of test devices, while continuously measuring key electrical and thermal parameters. Preliminary model-based prognostics algorithms have been developed making use of empirical degradation models and physics-inspired degradation model with focus on key components like electrolytic capacitors and power MOSFETs (metal-oxide-semiconductorfield-effect-transistor). This paper presents current results on the development of validation methods for prognostics algorithms of power electrolytic capacitors. Particularly, in the use of accelerated aging systems for algorithm validation. Validation of prognostics algorithms present difficulties in practice due to the lack of run-to-failure experiments in deployed systems. By using accelerated experiments, we circumvent this problem in order to define initial validation activities.

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“…For fault identification, once the number of fault candidates is reduced to less than a predefined number, for each fault candidate, a hypothesized fault model for that particular fault candidate is generated, and joint state-parameter estimation is performed [6]. For prognosis, the end of life of the system is predicted, using, for each hypothesized fault candidate, a predictor based on a fault progression model integrated with the nominal model [7]. Finally, we present results of several diagnosis and prognosis experiments performed on the simulation model of the WRS.…”

Section: Introductionmentioning

confidence: 99%

Model-based diagnosis and prognosis of a water recycling system

Roychoudhury

Hafiychuk

Goebel

2013

2013 IEEE Aerospace Conference

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Abstract-A water recycling system (WRS) deployed at NASA Ames Research Center's Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

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Multiple damage progression paths in model-based prognostics

Cited by 52 publications

References 13 publications

A structural model decomposition framework for systems health management

A structural model decomposition framework for systems health management

Prognostics of Power Electronics, methods and validation experiments

Model-based diagnosis and prognosis of a water recycling system

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