Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Naderi, E.; Meskin, Nader; Khorasani, K.

doi:10.1115/gt2011-45143

Cited by 20 publications

(26 citation statements)

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“…In recent years, several methods for analyzing aircraft fuel fault are established: Firstly, fault tree and expert system based on knowledge [1][2][3]; Secondly, modeling and simulation of the physical model, such as using the Flow master fluid simulation software which is a useful way to diagnose the FRV faults [4][5][6]; Thirdly, transfer function analysis of the classical control theory [4] and; Finally, the health status is monitored for fault analysis [7][8][9][10]. With the help of Simulink, we studied the function of each element by establishing their models respectively and the FRV's simulation model is established on this basis.…”

Section: Introductionmentioning

confidence: 99%

Fault Modeling and Simulation of Aviation Engine Fuel Return Valve

Wang¹,

Lu²,

Shi³

2014

TOAUTOCJ

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Fuel return valve is an important part of aviation engines. It achieves the cooling of integrated drive generator by regulating the amount of fuel in the oil cooler of integrated drive generator. Fault modeling and simulation of fuel return valve can provide targeted guidance for its maintenance. This paper establishes fuel return valve's mathematical model which includes the flow control valve, the electromagnetic valves and other components. The model is simulated using Simulink, and the effects sub-component failure takes on the fuel return valve in flow and transient characteristics are analyzed, which can provide a theoretical support for its maintenance.

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Section: Introductionmentioning

confidence: 99%

Fault Modeling and Simulation of Aviation Engine Fuel Return Valve

Wang¹,

Lu²,

Shi³

2014

TOAUTOCJ

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“…Nonetheless, if the problem of unknown parameters evolves and the trend of each PC of the engine failure data can be solved according to model (18), an estimated wear degradation and RUL may be predicted.…”

Section: Health Index Equation Of the Turbofan Enginementioning

confidence: 99%

“…As described in section ''Turbofan engine model,'' 21 different outputs from the gas turbine simulation were [17][18][19] chosen to discuss in this article. Because of the large amount of output data, it is difficult to establish the degradation model.…”

Section: Kpca-based Degradation Modelmentioning

confidence: 99%

“…Figure 2 shows a flowchart of the engine that includes the main modules and describes the relevant detail of all model information. [17][18][19] For the thermodynamic turbofan engine models, rotor and volume dynamics are involved in the nonlinear system. The engine model considered the volume dynamics and damage factors and added in an unbalanced mass flow rate.…”

Section: Turbofan Engine Modelmentioning

confidence: 99%

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A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

Feng

Xiao

Liu

et al. 2016

Advances in Mechanical Engineering

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Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis-based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included), and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.

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“…Borguet presented adaptive filters to track gradual deterioration and rapid deterioration [27]. Naderi proposed a nonlinear multiple model fault detection and isolation scheme for health monitoring of jet engines [28].…”

mentioning

confidence: 99%

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring. OPEN ACCESSEnergies 2013, 6 493

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Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Cited by 20 publications

References 0 publications

Fault Modeling and Simulation of Aviation Engine Fuel Return Valve

Fault Modeling and Simulation of Aviation Engine Fuel Return Valve

A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

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