Health monitoring and prognostics of blades and disks with blade tip sensors

Flotow, A. von; Mercadal, Mathieu; Tappert, P.

doi:10.1109/aero.2000.877917

Cited by 62 publications

(31 citation statements)

References 4 publications

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“…Blade tip clearance [Ja99], [Fl00], [Do99]: In turbine engines, the clearance of the blade tips must be kept low for the engine to be efficient, but not so low that there is rubbing against the race; this is made difficult by thermal expansion. Sensors can be used to monitor the clearance of the blades.…”

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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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%

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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“…This method can therefore be used to provide early detection of blade rubbing in gas turbine. Von Flotow et al [38] used capacitance blade tip sensor to detect parameters such as the time of arrival and angle of arrival and thus provide information to detect various types of blade faults such as loss of blade, crack, rubbing, and bend blade. A powerful and reliable technique for blade tip clearance measurements that can be used under extremely harsh environments is presented by Steiner [39].…”

Section: Blade Tip Monitoringmentioning

confidence: 99%

Condition Monitoring of Blade in Turbomachinery: A Review

Abdelrhman

Hee

Leong

et al. 2014

Advances in Mechanical Engineering

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Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

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“…Some of the advanced structural and mechanical health monitoring sensors under consideration include turbine blade monitoring sensors [33]. Blade monitoring sensors would provide the capability to measure blade tip clearance and blade time of arrival.…”

Section: Structural and Mechanical Component Health Managementmentioning

confidence: 99%

Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

Simon

Garg

Hunter

et al. 2004

Volume 2: Turbo Expo 2004

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NASA and the U.S. Department of Defense are conducting programs which support the future vision of “intelligent” aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.

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Health monitoring and prognostics of blades and disks with blade tip sensors

Cited by 62 publications

References 4 publications

Predicting Time to Failure Using the IMM and Excitable Tests

Predicting Time to Failure Using the IMM and Excitable Tests

Condition Monitoring of Blade in Turbomachinery: A Review

Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

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