Research On Life Forecasting Methods Of A DTG Based On Support Vector Machine

With the development of aerospace technology, aerospace products are evolving towards high reliability, long lifetime and great effectiveness. However, these necessary gesture controlling and measuring components, such as flywheel and gyroscope, are high cost and small batch, so how to estimate their reliability and lifetime has become an urgent challenge. As a result, this research on lifetime evaluation and forecasting of a high precision dynamically tuned gyroscope (DTG), which is widely applied in the navigation systems of these aerospace products, is significant.Due to the limitation of the lab's testing conditions, this thesis presents a kind of research scheme for single gyro lifetime testing and forecasting based on the factor that only one DTG can be employed. That is, this scheme adopts trend modeling and extrapolation to estimate and forecast the DTG lifetime. In this scheme, the 1:1 testing system of the single DTG is first built and executed to collect the testing data of different performance parameters, and then the key factor is studied and selected from the different performance parameters based on the corresponding analysis methods. Through the analysis and processing of the key factor, the corresponding DTG lifetime forecasting model based on SVM is built and investigated, which is employed to evaluate and forecast the DTG lifetime. The main research contents of the thesis are listed as follows:Firstly, owing to the configuration characteristic of the DTG and the testing conditions, a new lifetime evaluation scheme, the trend forecasting and extrapolation, is given to evaluate and forecast the lifetime of the single DTG. According to the given scheme, the different performance parameters of the DTG, such as gyro vibration, gyro drift (X- and Y-axis), gyro temperature, environmental temperature, gyro volt and power, are selected as the testing parameters, and the related testing circuits are designed, respectively. By designing of system soft and interface with Visual C++, the corresponding DTG lifetime testing system is built to accomplish the state measurement and data collection.Secondly, due to the superior ability of multi-resolution analysis, wavelet transform (WT) is introduced into the SVM, and thus a novel WT-SVM temperature compensation model is proposed and applied in the temperature modeling and compensation to reduce the influence of temperature variation on the long-term testing data of the different performance parameters. The corresponding modeling and compensating results indicate that the proposed WT-SVM model is feasible and effective. In addition, according to the change of long-term performance state of the DTG, the SVM-based recursive feature elimination strategy and the evaluation criterion of cross-validating error rate are adopted to conduct the self-learning identification and gyro feature selection. Consequently, gyro vibration is selected as the key performance parameter for the DTG's lifetime evaluating and forecasting investigation.Thirdly, according to the nonlinear and non-stationary characteristic of the gyro vibration, empirical mode decomposition (EMD) method is employed to analyze the gyro vibration signal. Aiming at the problems of intrinsic mode function (IMF) criterion in single EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are introduced into the EMD method, and an improved hybrid EMD-based analysis strategy is thus proposed and applied in the frequency-domain energy analysis of the gyro vibration. Simultaneously, owing to the change of long-term performance state of the DTG, the distribution approach of weight contribution based on SVM is employed to extract the frequency-domain energy features of the DTG, with which the energy trend of the gyro vibration denoting the gradual performance tendency of the DTG is built.Fourthly, combining the superiority of SVM forecasting and grey accumulated generating operation (AGO), a kind of new grey support vector machine (i.e. AGO-SVM model) forecasting model is proposed and applied in the forecasting analysis of gyro vibration energy. The modeling and forecasting results of the real energy data of the DTG show that the proposed strategy of the combination between AGO and SVM is effective and superior to the traditional grey model and single SVM method. In addition, according to the extracted gyro lifetime index (the vibration energy trend), the grey SVM model is exploited to forecast and analyze the residual lifetime of the DTG. Moreover, based on the statistical analysis of multiple subsection modeling and forecasting of the single DTG's energy data, the multi-step forecasting error model of the grey SVM model is built, which can be used to estimate the forecasting error and reliability of the DTG lifetime.In summary, the research of the thesis may be used as reference for lifetime forecasting research of other electromechanical rotating components in our aerospace or other fields.