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Design and experiment on intelligent fuzzy monitoring system for corn planters

International Journal of Agricultural and Biological Engineering 2013年第3期6卷 11-18页

作者： Du Ruicheng Gong Bingcai Liu Ningning Wang Chenchen Yang Zidong Ma Mingjian School of Agriculture and Food Engineering Shandong University of TechnologyZibo 255049ShandongChina

When sowing summer corn without tillage,it is necessary to ensure that the furrow opener is free from straw congestion and that the spacing of the sowing can be adjusted according to the breeds of corn and the preset seeding rate per *** the basis of the structural features of newly developed no-tillage corn fertilizers,an intelligent fuzzy monitoring system for corn planters was developed in this *** system facilitates automatic control of the spacing adjustment and the status monitor for the fertilizer tank,seed tank,and seeding *** to the preset number of rows,line spacing,number of plants per acre,and seed germination rate,the control rate can be calculated through designing in surveillance *** control rate is output to the fuzzy controller through the digital output module of the CAN *** control is applied to the DC motor for stepless adjustment of the spacing.A system for video surveillance of the working status of a planter is developed for displaying a real-time video image of the planter operation and achieving an anti-congestion status monitoring of a no-tillage planting operation in a dusty *** field trials,the detection accuracy was 91.4%.The seed-clogging fault-alarm accuracy was 96.0%.The entire system remained stable and reliable.

关键词： corn planters stepless adjustment plant spacing fuzzy control monitoring system fault alarm

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Data Mining with Improved Apriori Algorithm on Wind Generator alarm Data

Data Mining with Improved Apriori Algorithm on Wind Generato...

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第25届中国控制与决策会议

作者： Chao Tong Peng Guo School of Control and Computer Engineering North China Electric Power University

ISBN: (纸本)9781467355322

alarm system is an important subsystem of Supervisory Control and Data Acquisition （SCADA） system in wind turbines. Because of the bad operating environment and condition switching frequently, this system has the problems of alarm too frequently and too large amount of alarm data which greatly reduce the effectiveness of the alarm system. Remove the excessive redundant alarm and refine the valid information are significant to early find wind turbines abnormal operation, to research the causal connection between different faults and to reduce the operators’ workload. Data mining is an effective way to solve this problem. This paper used association rules on improved Apriori algorithm to analysis the alarm information which happened before and after blade angle asymmetry fault. Combined with the running mechanism of variable-pitch systems, we find the implied causal relationships between faults, then filter out minor redundant information, refine effective leading fault alarms and at last greatly reduce the number of alarm, improve the operators’ work efficiency.

关键词： Data Mining fault alarm Pitch Control fault Condition Monitoring

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