Abstract
Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.
Highlights
The operation condition of aircraft turbofan engine is tough due to the extreme temperature, strong vibration environment, broad load range and so on and the reliability of aircraft turbofan engine must be ensured over the lifetime [1]
Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry [2]
A diagnosis method based on the LM algorithm is proposed in this paper
Summary
The operation condition of aircraft turbofan engine is tough due to the extreme temperature, strong vibration environment, broad load range and so on and the reliability of aircraft turbofan engine must be ensured over the lifetime [1]. Energies 2018, 11, 181 foreign object damage (FOD) will lead to a sharp shift of engine performance, which is called abrupt failure [10] These physical failures in each gas path component would result in degradation of the thermodynamic efficiency and the flow capacity, which are called health parameters. Accurate estimation of the performance parameters of engine components over engine lifetime and reliably detecting the gradual degeneration and abrupt fault are expected to play a critical role in effective engine diagnostics and maintenance [12,13]. Several technologies have been applied in the performance parameters estimation and the fault diagnosis of aircraft turbofan engine, including data driven approaches and model based approaches. The feature extraction and pattern classification based on data driven methodology are applied in fault detection for aircraft turbofan engine by Sarkar [14]. Method in the transient case is evaluated through a simulation in Section 4, and the performance comparisons among the proposed method, EKF method and PF method in the abrupt fault case and the gradual degeneration case are implemented and discussed later; and Section 5 concludes this paper
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