Fault diagnosis for a turbine engine

Diao, Yixin; Passino, K.M.

doi:10.1016/j.conengprac.2003.11.012

Cited by 34 publications

(16 citation statements)

References 49 publications

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“…Turbomachinery performance deterioration analysis can be achieved by inducing deliberately some defects in the machine [7], or by building a model that simulates the conditions of mal function [8].…”

Section: Introductionmentioning

confidence: 99%

Radial Turbines Diagnosis in Turbocharging Application

Barelli

Bidini

Bonucci

2014

Journal of Engineering for Gas Turbines and Power

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The main purpose of this work is the development of a diagnostic procedure for radial turbines installed on internal combustion engine turbochargers. The study is part of a wider research activity regarding the development of diagnostic systems dedicated to cogenerative engine. The proposed procedure is able to formulate a judgment about the turbines efficiency through the real-time evaluation of an effective performance index, only on the basis of pressure measurements across the turbine. Such diagnostic proce dure was developed in reference to experimental data gathered in two acquisition cam paigns performed before and after a consistent maintenance intervention on engine turbines. The first part of the work approaches turbine failures issues, while in the second one experimental results are presented and analyzed. Finally, the study deals with the definition o f some turbine health state indices and their test in reference to the experimen tal data cited above. A particular index was validated as useful to be implemented in a diagnostic procedure for detection of turbine degradation.

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Section: Introductionmentioning

confidence: 99%

Radial Turbines Diagnosis in Turbocharging Application

Barelli

Bidini

Bonucci

2014

Journal of Engineering for Gas Turbines and Power

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“…Fault sensitivity of fault diagnosis refers to the ability to correctly determine the existence of a fault and then isolate its type. The fault-sensitivity result of the above fault-diagnosis scheme can also be achieved using a similar approach (Diao and Passino, 2000b).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Note that the effects of the faults are usually large; hence, the above assumptions can often be satisfied in real applications. (Refer to Diao and Passino (2000a) for the proof.) Fault sensitivity of fault diagnosis refers to the ability to correctly determine the existence of a fault and then isolate its type.…”

Section: Performance Evaluationmentioning

confidence: 99%

Intelligent fault-tolerant control using adaptive and learning methods

Diao

Passino

2002

Control Engineering Practice

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“…Proof: Consider the Lyapunov function candidate (32) Using vector derivatives and following [20] and [32], the time derivative of (32) is (33) Note that and the th derivative of the output error is so that (34) and from (13), (26), (29), and (27), the equation shown at the bottom of the next page holds true. Also, from (15)- (18), (23), and (24), we have Substitute the aforementioned equation into (33) and assume that the ideal parameters are constant (which is achieved by having and in (17) and (18) represent approximation errors from the time-varying part of system dynamics) so that and and substitute (30) and (31) into (33) Notice that we did not consider a projection modification to the previous update laws.…”

Section: Indirect Adaptive Controlmentioning

confidence: 99%

“…The general form of the model can be described as shown in (49)-(54) at the bottom of the page, where WF36 is the system input (the combustor fuel flow) and XNL XNH represents the system states (the fan rotor speed [32] and [34] for more details on how we have developed the nonlinear engine model using Takagi-Sugeno fuzzy systems).…”

Section: Simulation Examples: Jet Engine Controlmentioning

confidence: 99%

Adaptive neural/fuzzy control for interpolated nonlinear systems

Diao

Passino

2002

IEEE Trans. Fuzzy Syst.

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Abstract-Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. In this paper, we propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.

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Fault diagnosis for a turbine engine

Cited by 34 publications

References 49 publications

Radial Turbines Diagnosis in Turbocharging Application

Radial Turbines Diagnosis in Turbocharging Application

Intelligent fault-tolerant control using adaptive and learning methods

Adaptive neural/fuzzy control for interpolated nonlinear systems

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