Identification of sensor faults on turbofan engines using pattern recognition techniques

Aretakis, N.; Mathioudakis, K.; Stamatis, A.

doi:10.1016/j.conengprac.2003.09.011

Cited by 35 publications

(11 citation statements)

References 22 publications

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“…Many scholars have conducted a lot of research on such issues deeply, and there are a larger number of research results in the literature. Based on the KF, different neural networks are used to diagnose sensor faults of aerospace control systems [10][11][12]; Aretakis uses geometric pattern recognition technology and KF algorithm to solve the problem of slow drift in sensors [13]; a set of linear Kalman filter are used to diagnose sensor faults after linearization at operating point of the system [14]; a novel Kalman filter is designed to diagnose multi-sensor faults when systems existing colored noise [15]; in order to solve the non-Gaussian distribution problem of wind speed and measured noise in wind power generation systems, a novel filtering algorithm is proposed to identify and isolate the sensor fault [16]; a set of extended Kalman filter are used to diagnose attitude sensor faults [17]. Aiming to the problem of actuator faults in control systems, an improved multiple fading factor strong-tracking nonlinear filter algorithm is proposed to diagnose stuck-at faults and swing faults of the actuator [18]; in order to solve the problem of flywheel faults in satellite attitude control systems, a two-stage EKF algorithm is designed [19]; the actuator and sensor fault are regarded as system states, system states are optimized through the optimal and robust three-stage Kalman filter, and finally achieve the reconstruction of system faults [20].…”

Section: Introductionmentioning

confidence: 99%

Effect of Faults on Kalman Filter of State Vectors in Linear Systems

Song¹,

Shou-song²

2019

IJDST

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Kalman filter (KF) is composed of a set of recursion algorithms which can be used to estimate the optimal state of the linear system, and widely used in the control system, signal processing and other fields. In the practical application of the KF, it is an unavoidable problem that how faults or anomalies are infectious to the estimation value of state vectors in the linear system, which must be paid much attention to and solved down. In this paper, the effect of sensor faults and control input anomalies on the Kalman filtering values of state vectors is discussed, the transmission relationship is established to analyze the estimation deviation of state vectors which comes from pulse or step faults/anomalies, and a sufficient condition is deduced for the convergence of the estimation deviation of state vectors; Four different system models with 3-dimension state vector and 2-dimension observation vector are selected for simulation calculation and comparative analysis, simulation results show that sensor faults and control input anomalies in linear systems may cause significant deviations in the estimation value of state vectors for a long time, and there are distinct differences in the estimation value of state vectors. The research results provide a certain theoretical reference for us to analyze system fault types and to identify fault.

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Section: Introductionmentioning

confidence: 99%

Effect of Faults on Kalman Filter of State Vectors in Linear Systems

Song¹,

Shou-song²

2019

IJDST

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“…Neural Networks (ANN) [22][23][24], GPA [8], pattern recognition [25], Bayesian Belief network [26], etc. These methods are able to detect gas path sensor faults although they have different advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm

2017

Aeronaut. j.

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Gas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results.In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines.

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“…Exhaust temperature of gas turbines is one of the major variables that should be controlled for safe and reliable operation of turbine and a good indicator of the health conditions and system performance. The validation of measured data and diagnosis possible faults in temperature sensors would be necessary in advance [1].…”

Section: Introductionmentioning

confidence: 99%

“…Undetected faults on temperature sensors may lead to false alarms on system health condition, undesired system trip or endanger entire system safety due to overheating and exceeded thermal stress on turbine blades [1,2].…”

Section: Introductionmentioning

confidence: 99%

Temperature sensor fault diagnosing in heavy duty gas turbines using Laguerre network-based hierarchical fuzzy systems

Chaibakhsh

Amirkhani

Piredeir

2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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This study present an application of Laguerre network-based hierarchical fuzzy modeling approach in fault diagnosis of the temperature sensors in industrial heavy duty gas turbines. The recorded experimental data from the performances of a V94.2 gas turbine unit were employed in modeling stage. A comparison between the responses of the models and real data indicate the capability of the model for long-term prediction of the turbine outlet temperature at different operating conditions. The differences between the models and measured values were defined as the residuals. To deal with uncertainties and disturbances, the thresholds bounds were considered for the residuals. The residuals deviations with respect to threshold boundaries yield to symptoms, which were analyzed in a Takagi-Sugeno fuzzy inference expert system. The performances of fault detection and fault diagnosis system were evaluated by subjecting the sensors to faults. The obtained results show that the faults are successfully detected and diagnosed.

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Identification of sensor faults on turbofan engines using pattern recognition techniques

Cited by 35 publications

References 22 publications

Effect of Faults on Kalman Filter of State Vectors in Linear Systems

Effect of Faults on Kalman Filter of State Vectors in Linear Systems

Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm

Temperature sensor fault diagnosing in heavy duty gas turbines using Laguerre network-based hierarchical fuzzy systems

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