Advanced test cell diagnostics for gas turbine engines

Roemer, Michael; Kacprzynski, Gregory J.; Schoeller, M.; Howe, R.; Friend, Richard H.

doi:10.1109/aero.2001.931313

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…By counting the number and size of particles gives a clue to the health of the system. Detection of foreign objects entering an engine [Sh00], [Ro01]: For turbine engines, foreign objects that enter the engine can be detected and classified as damaging or non-damaging, based on their size and composition.…”

Section: Prior Workmentioning

confidence: 99%

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Predicting Time to Failure Using the IMM and Excitable Tests

Phelps

Willett

Kirubarajan

et al. 2007

IEEE Trans. Syst., Man, Cybern. A

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Prognostics, which refers to the inference of an expected time-to-failure for a system, is made difficult by the need to track and predict the trajectories of real-valued system parameters over essentially unbounded domains, and by the need to prescribe a subset of these domains in which an alarm should be raised. In this paper we propose an idea, one whereby these problems are avoided: instead of physical system or sensor parameters, a vector corresponding to the failure probabilities of the system's sensors (which of course are bounded within the unit hypercube) is tracked. With the help of a system diagnosis model, the corresponding the fault signatures can be identified as terminal states for these probability vectors. To perform the tracking, Kalman filters and interacting multiple model (IMM) estimators are implemented for each sensor. The work that has been completed thus far shows promising results in both large and small-scale systems, with the impending failures being detected quickly and the prediction of the time until this failure occurs being determined accurately.

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Section: Prior Workmentioning

confidence: 99%

“…and "If a failure has occurred, which one?" Techniques for diagnostic information fusion and decision might be physical models/expert knowledge/rule-based [Ro00], [Ga01]; neural networks trained by real data [Br00], [Ga01], [Br99], [Ja99]; fuzzy logic [Br00], [Ga01], [Br99]; or statistical (Bayesian, Dempster-Shafer) [Ro01].…”

Section: Prior Workmentioning

confidence: 99%

Predicting Time to Failure Using the IMM and Excitable Tests

Phelps

Willett

Kirubarajan

et al. 2007

IEEE Trans. Syst., Man, Cybern. A

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show abstract

“…A static pattern analysis approach was proposed by Patel et al [79] and Arkov et al [90], where the observation of gas turbine status was expressed by a probability density or histogram approach and any deviation of the engine from its normal condition can be indicated by a low likelihood of the observation. A probabilistic fault diagnostic approach was introduced by Ghiocel and Roemer [124] and Roemer and Ghiocel [125] and was further described by Ghiocel and Altmann [126] and used by Roemer et al [127]. In the method, both the monitored and fault data uncertainties were considered and described with PDFs.…”

Section: Expert Systemsmentioning

confidence: 99%

Performance-analysis-based gas turbine diagnostics: A review

2002

Proceedings of the Institution of Mechanical Engineers, Part A:

166

126

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Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960s, when L. A. Urban introduced gas path analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aerospace and industrial applications. To date, a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance-analysis-based methods available thus far for gas turbine fault diagnosis in the open literature.

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Upgrading engine test cells for improved troubleshooting and diagnostics

Roemer

Orsagh

Schoeller

et al.

Proceedings, IEEE Aerospace Conference

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Abstract-Upgrading military engine test cells with advanced diagnostic and troubleshooting capabilities will play a critical role in increasing aircraft availability and test cell effectiveness while simultaneously reducing engine operating and maintenance costs. Sophisticated performance and mechanical anomaly detection and fault classification algorithms utilizing thermodynamic, statistical, and empirical engine models are

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Advanced test cell diagnostics for gas turbine engines

Cited by 4 publications

References 1 publication

Predicting Time to Failure Using the IMM and Excitable Tests

Predicting Time to Failure Using the IMM and Excitable Tests

Performance-analysis-based gas turbine diagnostics: A review

Upgrading engine test cells for improved troubleshooting and diagnostics

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