| 英文摘要 | During the flight, if a fault occurs on the engine which works as the aircraft heart, it will be a direct threat to the flight safety. But if we can know the cause of the fault in advance, and then carry out specific maintenance or replacement, it will greatly reduce the possibility of the aircraft crash. In addition, if the engine maintenance costs can be minimized in its life cycle, the airlines will obtain great benefits. Just under such a background, the air power and airlines have proposed the concept of Prognostics and health management (PHM) successively. In fact, prognostics is the premise of health management, therefore, prediction technology is a part of health management techonology. Currently, the most famous aviation manufacturers (P&W, GE and Rolls-Royce) were separately launched one or several engine condition monitoring system or systems. Although, these systems can monitor, record, diagnosis and evaluate health condition, but they do not have the ability to predict. It is related to the lack of aircraft engine prediiton theoretical research, so it is necessary to conduct in-depth study on it. An aircraft engine is a complex and changing with time nonlinear system, so the nonlinear time series analysis theory is applied to it. What is more, prediction models based ont the theory have been successfully applied in the areas of finance, management and biology and others, which provides a wealth of experience for the application in engine health management. Therefore, this paper focuses on the nonlinear time series analysis theory, and make research on three aspects: eingine performance prediction, fault prediction and life prediction. For performance prediction, a method based on Wavelet Transform and ARIMA models is proposed. Benifiting from the wavelet decomposition of signals, this method presents a higher prediction accuracy compared with the traditional methods of time series analysis. However, wavelet transform has no responds to the subtle small changes in the signals of early processing, and ARIMA models don’t have ability to handle the time cumulative effect. Thus, a new method based on EMD and PNN is proposed. As the experiment shows, this method performances better. For the engine fault prediction problem, some typical fault modes of engines are produced, and then this paper presents the processing to predict engine faults. Because there are a large amount of fault modes, wearing fault is chosed to verify th... |
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