CAJ | 학술논문

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。
침대궤계설비진동신호서렬적비선성、비평은성특점，제출료일충기우상공간중구여유전우화지지향량회귀궤적설비상태추세예측방법。수선，채용상공간중구기술장일유진동신호시간서렬전화성구진형식，자괄응지선취특정，이상점작위수입특정훈련SVR예측기；연후응용자괄응유전산법대징벌인자、불민감계수이급고사핵관도진행동보우화，자동획취최가적건모삼수；최후구건SVR예측모형，병장기응용우모궤조진동신호예측。실험결과표명，무론시단보환시24보예측，본문소제유전우화SVR모형적예측정도도요비표준SVR모형적예측정도고，설명해방법대궤계설비적운행상태추세구유교호적예측능력。
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.