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Predicting Research of Mechanical Gyroscope Life Based on Wavelet Support Vector

机译：基于小波载体载体的机械陀螺仪研究预测研究

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Mechanical gyroscope has characters of high cost and few quantity. In order not to take 1:1 experiment to evaluate its performance and life, we propose a life prediction method that combined wavelet analysis and support vector machine (SVM). First, we use wavelet analysis to do pretreatment on life data to reduce some interference information to improve the data smoothness and weaken data randomness. Then we use SVM to model those preprocessed data. The choosing of model parameters is based on genetic algorithm to search optimal value globally and get prediction data. In order to prove the superiority of this model, we choose the life data of dynamically tuned gyroscope in literature. SVM model and WA-SVM model were used to predict gyroscope's life and their results were compared. We give root-mean-square error of different model to make the comparison more obviously. The results show that better prediction effect and its root-mean-square error is just 3.47%.
机译：机械陀螺有成本高，数量少的特征。为了不服用1：1实验来评估其性能和生活，我们提出了一种寿命预测方法，将小波分析和支持向量机（SVM）组合。首先，我们使用小波分析来进行生命数据的预处理，以减少一些干扰信息，以提高数据平滑度并削弱数据随机性。然后我们使用SVM来模拟那些预处理的数据。模型参数的选择基于遗传算法来全局搜索最佳值并获得预测数据。为了证明这种模型的优越性，我们选择文献中动态调谐陀螺仪的生命数据。 SVM模型和WA-SVM模型用于预测陀螺仪的生命，并比较它们的结果。我们给出不同模型的根均方误差，更明显地进行比较。结果表明，更好的预测效果及其根均方误差仅为3.47％。

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