基于STL分解的平均故障间隔时间组合预测

由于影响数控机床刀架系统平均故障间隔时间的因素较多,采用单一的模型作预测无法充分提取已知数据中的隐含信息,导致预测困难。应用基于STL进行时间序列分解的组合模型预测算法,将原始故障数据分解为趋势项、季节项和随机项,应用指数平滑法和支持向量机回归分别对前两项数据进行预测,根据时序分解的加法模型将其结合,得到组合模型预测结果,并将组合模型与单一的预测模型进行对比分析。实例证明:组合模型预测优于单一模型预测。此方法应用于MTBF的预测,有助于工作人员针对故障发生时间点提前采取措施,同时为数控机床可靠性评估提供了新的研究方法。

Combined Prediction of Mean Time Between Failures Based on STL Decomposition

There are many factors that affect the mean time between failures of CNC machine tool holder system, it is difficult to extract the hidden information from the known data by using a single model. A combined model based on STL time series decomposition forecasting algorithm was applied was decompose the original fault data into trend term, seasonal term and random term.Exponential smoothing method and support vector machine regression were applied to forecast the first two data respectively. The predictions were combined according to the additive model of temporal decomposition.So combined model prediction results were obtained, and the combination model was compared with single forecast model. The example shows that the combined model is superior to the single model. This method is applied to the prediction of MTBF, which is helpful for the staff to take measures in advance according to the time point of failure, and provides a new research method for the reliability assessment of CNC machine tools.