Aeroengine Condition Monitoring Technique And System Based On Gas Path Parameter Sample

Aeroengine is the kernel power supply of aircraft, which structure is complex and which operation environment is bad, so it is the main source of aircraft trouble, and its condition is the direct influencing factor of flight safty and airline company benefit. In this dissertation, aeroengine condition monitoring technique and software system based on aeroengine gas path parameters is studied under the supports of the National Natural Science Foundation of China (Grant No. 60572174) and the Air-China Scientific Research Foundation.Aeroengine gas path parameter sample is a non-stationary time series blending stochastic noise essentially, of which there are frequently singularity data such as isolated mutational data and trend mutational data, etc. Wavelet transform is a kind of multi-scales time-frequency analysis tool, which can extract the characteristics of isolated data point mutation and data trend mutation excellently, consequently, wavelet is adapt to process non-stationary aeroengine gas path parameter data and of which to identify the data singularity. In order to improve the usability of aeroengine performance data and identify the abnormal condition of aeroengine in time, and to reconstruct original data accurately, signal mutation identifying and reconstruction techniques are proposed based on modulus local maxima curves searching of continuous wavelet transform coefficients of signal, which can identify signal mutation and denoise at the same time, thus, the original signal can be resumed effectively. To resolve the problem that the computing speed of continuous wavelet transform method is very low, discrete Fourier transform based the continuous wavelet transform and inverse transform algorithms of signal are proposed. To further improve the accuracy of the mutational data identify and original data reconstruction, a simulation example is presented to research the processing methods of edge effect and false modulus local maxima curves which may come forth in the searching process of modulus local maxima curves of continuous wavelet transform coefficients. The data preprocess instance of aeroengine gas path performance parameter indicates the technique proposed can reconstructe efficientlly aeroengine performance parameter original data and the reconstruction effect is better than the effect of wavelet soft threshold denoising. A Hybrid Recurrent Process Neural Network is proposed to resolve the problems that building the prediction model of complex system condition is hard and the prediction precision is hard to be guaranteed. Concretely, the topological structure of this network is designed, and the network is simplified by introducing a set of appropriate orthogonal basis functions to expand the input functions and the connection weight functions of the network. And a learning algorithm base on resilient backpropagation is proposed for the network. The validation of this technique is proved by a benchmark of the Mackey-Glass chaos time series prediction using the network proposed and several tradition artificial neural networks. A practical utilization of the aeroengine gas path performance parameter data prediction by above networks demonstrates this point in terms of aeroengine condition monitoring too, the prediction results indicate Hybrid Recurrent Process Neural Network is an efficient aeroengine condition monitoring tool.Because tradition data fitting techniques based on least square procedure have inadaptabilities in fitting two aeroengine performance parameters which are both random variable, a Euclidean Distance Least Squares Support Vector Regression technique is proposed, which processes the random noise of two fitting variables at the same time, and a corresponding realization algorithm based on quadratic programming toolbox of Matlab is presented. The validity of the Euclidean Distance Least Squares Support Vector Regression technique is proved by a instance of aeroengine fleet trend analysis. In addition, A technique fitting aeroengine performance parameter time series is proposed summarily based on multi-order polynomial regression method to resolve the problem that fitting the time series of the different aeroengine performance parameter may need different fitting method.Analytic Hierarchy Process is a practical effective multiple criteria decision making method, But the consistency check is needed to determine its judgment matrix, and the grading criterions of its evaluating indicators observed values are generally determined by expert experience so that the non-determinacy comes forth. Therfore, a Grey Analytic Hierarchy Process based on Grey Cluster Evaluating Theory is proposed, and for aeroengine, the comprehensive evaluating indicators system of aeroengine condition is built. And the synthetic weight and the grey classification of every aeroengine condition evaluating indicator is computed or is present, and the triangle-type weight fuctioin based the parameters observed values of aeroengine condition evaluating indicators are graded. Finally, a instance of aeroengine condition comprehensive evaluation is presented.Orienting the application background of the aeroengine condition monitoring and the engineering management of Air-China, software system requirement is analyzed, and the function model and the data model and the architecture of the system are designed in turn. Subsequently, based on the aeroengine performance monitoring and condition evaluation techniques proposed, a aeroengine condition monitoring system orienting aeroengine life-cycle management is developed. The system includes serveral function modules such as the information management module of maintenance and condition monitoring of aeroengine; the aeroengine condition trend analysis module; the autoalarm module monitoring aeroengine condition parameters; the aeroengine condition comprehensive evaluation module; and the aeroengine removal time prediction module, etc. As one significant part of the aeroengine cycle-life management system, the aeroengine condition monitoring system has been applied to the project of aeroengine condition monitoring in Air-China, and the system validition is proved by the test run effects.