Aircraft Engine - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Aircraft Engine

The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Aircraft Engine – Free Register to Access Experts & Abstracts

Hongkai Jiang – One of the best experts on this subject based on the ideXlab platform.

  • Robust incipient fault identification of Aircraft Engine rotor based on wavelet and fraction
    Aerospace Science and Technology, 2010
    Co-Authors: Zhongsheng Wang, Hongkai Jiang
    Abstract:

    Abstract Signal de-noising and diagnosis of the weak signature are crucial to Aircraft Engine prognostics in which case features are often very weak and masked by noise. Robust methods are needed to provide more evident information for Aircraft Engine incipient fault diagnosis and prognostics. This paper develops enhanced and robust prognostic methods for Aircraft Engine including wavelet based method for weak signature enhanced for adaptive de-noising and correlation dimension based for incipient fault diagnosis. Firstly, the adaptive wavelet de-noising method is used to reduce noise of the vibration signal. Then, correlation dimension of the vibration signal after de-noising is computed, and the correlation dimension is used as the character parameter for identifying the fault deterioration grade. Experiment of the Aircraft Engine rotor is carried out. The experimental results demonstrate that: (1) the different rotor faults show different kinematics mechanisms; (2) the singular signal of incipient fault on Aircraft Engine rotor can be effectively extracted by adaptive de-noising; (3) the incipient fault of Aircraft Engine rotor can be fast distinguished by the correlation dimension; (4) it provides a effective way for robust incipient fault identification of Aircraft Engine rotor to combine wavelet with fraction.

  • Robust Incipient Fault Diagnosis Methods for Enhanced Aircraft Engine Rotor Prognostics
    Second International Conference on Innovative Computing Informatio and Control (ICICIC 2007), 2007
    Co-Authors: Zhongsheng Wang, Hongkai Jiang
    Abstract:

    Signal de-noising and diagnosis of the weak signature are crucial to Aircraft Engine prognostics in which case features are often very weak and masked by noise. Robust methods are needed to provide more evident information for Aircraft Engine incipient fault diagnosis and prognostics. This paper develops enhanced and robust prognostic methods for Aircraft Engine including wavelet based method for weak signature enhanced for adaptive de-noising and correlation dimension based for incipient fault diagnosis. Firstly, the adaptive wavelet de-noising method is used to reduce noise of the vibration signal. Then, correlation dimension of the vibration signal after de-noising is computed, and the correlation dimension is used as the character parameter for identifying the fault deterioration grade. Experiment on the rotor of Aircraft Engine is carried out. The experimental results demonstrate that: (1) the different rotor faults show different kinematics mechanisms; (2) the singular signal of incipient fault on Aircraft Engine rotor can be effectively extracted by adaptive de-noising; (3) the correlation dimensions of different faults can be easily distinguished and used as characteristic of nonlinear faults of the rotor.

Wu Zhe – One of the best experts on this subject based on the ideXlab platform.

  • A New Method of Parameters Optimization of Aircraft Engine Acceleration Control
    Computer Simulation, 2014
    Co-Authors: Wu Zhe
    Abstract:

    Parameters optimization of Aircraft Engine acceleration control is very important for weighting military Aircraft performance. For the problem of the poor effect of Aircraft Engine acceleration controller parameters optimization method,according to the problem that characteristics of Aircraft Engine is non- linear and time- varying,on the basis of the conventional N- M simplex nonlinear optimization algorithm,this paper combined with the translation error threshold judgment,introduced the improved simplex of vertex translation to optimize the parameters of proportion,integral and differential coefficients of PID controller. Take the integral of the product of adjusting time,the error sum of squares and compressor surge margin as the objective function,C language was used to compile control algorithm in the optimization process based on CodeBlocks platform. The simulation results show that the method has faster convergent speed and good real- time performance,and can meet the requirements of Aircraft Engine acceleration which requires short response time meanwhile avoids surge and over- temperature.

Zhongsheng Wang – One of the best experts on this subject based on the ideXlab platform.

  • Robust incipient fault identification of Aircraft Engine rotor based on wavelet and fraction
    Aerospace Science and Technology, 2010
    Co-Authors: Zhongsheng Wang, Hongkai Jiang
    Abstract:

    Abstract Signal de-noising and diagnosis of the weak signature are crucial to Aircraft Engine prognostics in which case features are often very weak and masked by noise. Robust methods are needed to provide more evident information for Aircraft Engine incipient fault diagnosis and prognostics. This paper develops enhanced and robust prognostic methods for Aircraft Engine including wavelet based method for weak signature enhanced for adaptive de-noising and correlation dimension based for incipient fault diagnosis. Firstly, the adaptive wavelet de-noising method is used to reduce noise of the vibration signal. Then, correlation dimension of the vibration signal after de-noising is computed, and the correlation dimension is used as the character parameter for identifying the fault deterioration grade. Experiment of the Aircraft Engine rotor is carried out. The experimental results demonstrate that: (1) the different rotor faults show different kinematics mechanisms; (2) the singular signal of incipient fault on Aircraft Engine rotor can be effectively extracted by adaptive de-noising; (3) the incipient fault of Aircraft Engine rotor can be fast distinguished by the correlation dimension; (4) it provides a effective way for robust incipient fault identification of Aircraft Engine rotor to combine wavelet with fraction.

  • Robust Incipient Fault Diagnosis Methods for Enhanced Aircraft Engine Rotor Prognostics
    Second International Conference on Innovative Computing Informatio and Control (ICICIC 2007), 2007
    Co-Authors: Zhongsheng Wang, Hongkai Jiang
    Abstract:

    Signal de-noising and diagnosis of the weak signature are crucial to Aircraft Engine prognostics in which case features are often very weak and masked by noise. Robust methods are needed to provide more evident information for Aircraft Engine incipient fault diagnosis and prognostics. This paper develops enhanced and robust prognostic methods for Aircraft Engine including wavelet based method for weak signature enhanced for adaptive de-noising and correlation dimension based for incipient fault diagnosis. Firstly, the adaptive wavelet de-noising method is used to reduce noise of the vibration signal. Then, correlation dimension of the vibration signal after de-noising is computed, and the correlation dimension is used as the character parameter for identifying the fault deterioration grade. Experiment on the rotor of Aircraft Engine is carried out. The experimental results demonstrate that: (1) the different rotor faults show different kinematics mechanisms; (2) the singular signal of incipient fault on Aircraft Engine rotor can be effectively extracted by adaptive de-noising; (3) the correlation dimensions of different faults can be easily distinguished and used as characteristic of nonlinear faults of the rotor.

Weizhong Yan – One of the best experts on this subject based on the ideXlab platform.

  • On improving performance of Aircraft Engine gas path fault diagnosis
    Transactions of the Institute of Measurement and Control, 2009
    Co-Authors: Weizhong Yan, Kai Goebel
    Abstract:

    Aircraft Engine fault diagnosis plays a crucial role in cost-effective operations of Aircraft Engines. However, designing an Engine fault diagnostic system with the desired performance is a challenging task, because of several characteristics associated with Aircraft Engines. Geared towards achieving the highest possible performance of fault diagnosis, this paper explores strategies on improving diagnosis performance. Specifically, we introduce flight regime mapping and a two-level multiple classifier system as means to improve classification performance. By designing a real-world Aircraft fault diagnostic system, we demonstrate that the strategies adopted in this study are effective in improving the performance of Aircraft Engine fault diagnostic systems.

  • Application of Random Forest to Aircraft Engine Fault Diagnosis
    The Proceedings of the Multiconference on "Computational Engineering in Systems Applications", 2006
    Co-Authors: Weizhong Yan
    Abstract:

    Aircraft Engine fault diagnosis plays a critical role in modern, cost-effective condition-based maintenance strategy in Aircraft industry. Due to several inherent characteristics associated with Aircraft Engines, accurately diagnosing Aircraft Engine faults is a challenging classification problem. As a result, Aircraft Engine fault diagnosis has been an active research topic attracting tremendous research interests in machine learning community. In this paper, random forest classifier, a recently emerged machine learning technique, is applied to Aircraft Engine fault diagnosis in an attempt to achieve more accurate and reliable classification performance. Our primary objective is to evaluate effectiveness of random forest classifier on Aircraft Engine fault diagnosis. By designing a real-world Aircraft Engine fault diagnostic system, this paper investigates design details of random forest classifier and evaluates its performance. In this paper, we also make some efforts on investigating strategies for improving random forest performance specifically for Aircraft Engine fault diagnosis problem

  • A MULTIPLE CLASSIFIER SYSTEM FOR Aircraft Engine FAULT DIAGNOSIS
    , 2006
    Co-Authors: Weizhong Yan, Kai F. Goebel
    Abstract:

    Multiple classifier systems (MCS) are considered as one of the most significant advances in pattern classification in recent years. Numerous studies (both theoretical and empirical) have proved that MCS are effective in achieving improved classification performance for various application problems. Aircraft Engine fault diagnosis plays a crucial rule in cost- effective operation of Aircraft Engines. By accurately detecting and reliably diagnosing impending Engine faults, Aircraft Engine fault diagnosis can help to increase Engine on-wing time, reduce maintenance turnaround time, reduce Aircraft life-cycle costs, and increase flight safety. However, designing a reliable Aircraft Engine fault diagnostic system is a challenging task, due to a number of characteristics of Aircraft Engines. These characteristics include the wide range of flight regime that Aircraft Engines are operated over and that Engines experience normal wear that needs to be differentiated from faults. Motivated by a goal of achieving the highest possible performance of fault diagnosis, we introduce MCS to Aircraft Engine fault diagnosis. By designing a real-world MCS-based Aircraft fault diagnostic system, we demonstrate that MCS is effective in improving the performance of Aircraft Engine fault diagnostic systems.

Dongfang Luo – One of the best experts on this subject based on the ideXlab platform.

  • Research and Application of Blind Signal Separation Algorithm to the Aircraft Engine Vibration Signal and Fault Diagnosis Based on Fast ICA
    Journal of Convergence Information Technology, 2012
    Co-Authors: Dongfang Luo, Huijuan Sun, Xinling Wen
    Abstract:

    The daily maintenance work to the Aircraft Engine takes a lot of human, financial and material resources, once the Engine failure will cause a major flight accidents. Therefore, quick and effective prediction to the Aircraft Engine fault can guarantee the Engine reliable work and the plane flight safety. In this paper, the Fast ICA blind source separation algorithm was applied to some type of double rotor Aircraft Engine’s vibration signal, and the simulation results shown that the proposed Fast ICA algorithm has got a good separation effect, effectively isolated Aircraft Engine vibration signal containing noises. According to the fault type characters of the Aircraft Engine, Fast ICA blind source separation algorithm identified the fault type of the Aircraft Engine, which has fewer iteration times, low computing complexity, the separation effect and stability is good.

  • State Detection Method to the Aircraft Engine Based on the Time Domain Parameters Analysis Technology
    , 2012
    Co-Authors: Dongfang Luo
    Abstract:

    The state detection method to the Aircraft Engine is very important to assure the Aircraft‘s safety flight, which has developed a new technology to realize the fault diagnosis to the Aircraft Engine. The collection of Aircraft Engine vibration signal can be used to complete the Aircraft Engine state detection and the fault diagnosis. In this study, the pretreated Aircraft Engine‘s vibration signal was analyzed based on the time domain method, through the simulation, we can identify the Aircraft Engine‘s current state from the normal flight to the fault state. And the time domain parameters can accurately judge current Aircraft Engine state, which provide the research basis for the subsequent Aircraft Engine fault diagnosis type.