【摘要】 在80组齿轮箱磨损试验数据基础上,以磨损严重度指数为参数,分析了磨损过程的非平稳性,并采用自回归-求和-滑动平均时间序列(ARIMA)模型对这个过程进行了趋势分析.通过差分算子和标准化的处理,将试验数据序列进行了平稳化处理,推导出描述非平稳磨损过程的ARIMA(4,1,1)模型的具体结构.结合齿轮磨损试验和Hodrick-Prescott滤子方法分析,发现这种算法的趋势分析精度比以前使用的算法提高了9 8%,而且实时性好,适合在线铁谱仪现场运行使用.更多还原

【Abstract】 Based on 80 groups of experiment data and the severity index of wear chosen as research object, the nonstationary wear course is analyzed. Auto regressive integrated moving average (ARIMA) model is adopted to analyze the nonstationary series, which can be turned into stationary series by using difference operator and standardization. ARIMA(4,1,1) model is deduced to fit wear course. Hodrick-Prescott filter analysis and experiment data show that the prediction precision can be improved by 98% than before, and a lower computing cost can be achieved. Therefore it is suitable to take this algorithm as an embedded software for on-site application of on-line ferrograph.更多还原