基于相空间重构和遗传优化SVR的机械设备状态趋势预测

针对机械设备振动信号序列的非线性、非平稳性特点，提出了一种基于相空间重构与遗传优化支持向量回归机的设备状态趋势预测方法。首先，采用相空间重构技术将一维振动信号时间序列转化成矩阵形式，自适应地选取特征，以相点作为输入特征训练SVR预测器；然后应用自适应遗传算法对惩罚因子、不敏感系数以及高斯核宽度进行同步优化，自动获取最佳的建模参数；最后构建SVR预测模型，并将其应用于某机组振动信号预测。实验结果表明，无论是单步还是24步预测，本文所提遗传优化SVR模型的预测精度都要比标准SVR模型的预测精度高，说明该方法对机械设备的运行状态趋势具有较好的预测能力。

Condition Trend Prediction of Mechanical Equipments Based on Phase Space Reconstruction and Genetic Optimization Support Vector Regression

Aiming at the nonlinear and non-stationary characteristics of vibration signal sequence of mechanical equipments, a condition trend prediction method for mechanical equipments is proposed based on phase space reconstruction and genetic optimization support vector regression （SVR）. First of all, a one-dimensional time series of vibration signals are transformed into a matrix by use of phase space reconstruction technique, and its features are selected adaptively. The phase points are imported to SVR model as input features and the SVR predictor is trained. Then, adaptive genetic algorithm is applied to optimize the penalty factor C, non-sensitive factor and Gaussian kernel width synchronously. The best model parameters are obtained automatically. Finally, the SVR prediction model is constructed and is applied to vibration signal prediction of a machine unit. The experimental results show that whether for single-step or 24-step prediction, the prediction accuracy of the proposed genetic optimization SVR is higher than that of the conventional SVR, indicating that the proposed method has a good ability for prediction of the condition trend of the mechanical equipments.