Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning pr...Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.展开更多
This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two ver...This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two vertical edge items, which are the borderlines of both sides of the vehicle, by extending the horizontal edges inaccurately due to the illumination or noise existing on the image. The proposed method extracts the horizontal edges with the method of merging edges by using the horizontal edge information inside the Region of Interest （ROI）, which is set up on the pre-processing step. The bottona line is determined by detecting the shadow regions of the vehicle from the extracted hoodzontal edge one. The general width of the vehicle detecting and the extended edge analyzing methods are carried out side by side on the bottom line of the vehicle to determine width of the vehicle. Finally, the finmal vehicle is detected through the verification step. On the road image with conaplicate background, the vehicle detecting method based on the extended edge analysis is more efficient than the existing vehicle detecting method which uses the edge information. The excellence of the proposed vehicle detecting method is confirmed by carrying out the vehicle detecting experiment on the complicate road image.展开更多
基金Supported by the National Natural Science Foundation of China(61374137,61490701,61174119)the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds(2013ZCX02-03)
文摘Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.
基金supported bythe MKE(The Ministry of Knowledge Economy,Korea),the ITRC(Information Technology ResearchCenter)support program(NIPA-2010-(C1090-1021-0010)),the Brain Korea 21 Project in 2010
文摘This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two vertical edge items, which are the borderlines of both sides of the vehicle, by extending the horizontal edges inaccurately due to the illumination or noise existing on the image. The proposed method extracts the horizontal edges with the method of merging edges by using the horizontal edge information inside the Region of Interest （ROI）, which is set up on the pre-processing step. The bottona line is determined by detecting the shadow regions of the vehicle from the extracted hoodzontal edge one. The general width of the vehicle detecting and the extended edge analyzing methods are carried out side by side on the bottom line of the vehicle to determine width of the vehicle. Finally, the finmal vehicle is detected through the verification step. On the road image with conaplicate background, the vehicle detecting method based on the extended edge analysis is more efficient than the existing vehicle detecting method which uses the edge information. The excellence of the proposed vehicle detecting method is confirmed by carrying out the vehicle detecting experiment on the complicate road image.
