在水煤浆气化装置中合成气管道温度降低会造成酸性气体冷凝,对管道内壁产生腐蚀甚至导致穿孔泄漏。为及时发现合成气管道漏损点并精确定位,研究基于分布式光纤测温系统(distributed temperature sensing,DTS)的合成气管道漏损检测与定...在水煤浆气化装置中合成气管道温度降低会造成酸性气体冷凝,对管道内壁产生腐蚀甚至导致穿孔泄漏。为及时发现合成气管道漏损点并精确定位,研究基于分布式光纤测温系统(distributed temperature sensing,DTS)的合成气管道漏损检测与定位。提出一种基于自适应方差阈值的DTS检测定位算法,首先运用层次聚类对检测到的信号进行识别,将漏损信号识别出来,然后将识别出的漏损信号经过方差处理、自适应阈值设定对漏损点位置进行定位,该算法能够对合成气管道漏损点进行定位。将该方法与固定阈值法和选择性平均阈值法进行比较,其定位精度分别提高了0.32 m和0.17 m。并在煤气化现场进行测温实验,对现场漏损点位置进行了精确定位。展开更多
Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
文摘在水煤浆气化装置中合成气管道温度降低会造成酸性气体冷凝,对管道内壁产生腐蚀甚至导致穿孔泄漏。为及时发现合成气管道漏损点并精确定位,研究基于分布式光纤测温系统(distributed temperature sensing,DTS)的合成气管道漏损检测与定位。提出一种基于自适应方差阈值的DTS检测定位算法,首先运用层次聚类对检测到的信号进行识别,将漏损信号识别出来,然后将识别出的漏损信号经过方差处理、自适应阈值设定对漏损点位置进行定位,该算法能够对合成气管道漏损点进行定位。将该方法与固定阈值法和选择性平均阈值法进行比较,其定位精度分别提高了0.32 m和0.17 m。并在煤气化现场进行测温实验,对现场漏损点位置进行了精确定位。
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
