Research Article
Research on Intelligent Diagnosis of Fault Data of Large and Medium-Sized Pumping Stations Under Information Evaluation System
@INPROCEEDINGS{10.1007/978-3-030-51100-5_9, author={Ying-hua Liu and Ye-hui Chen}, title={Research on Intelligent Diagnosis of Fault Data of Large and Medium-Sized Pumping Stations Under Information Evaluation System}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2020}, month={7}, keywords={Information-based evaluation system Large and medium-sized pump station Fault data Intelligent diagnosis}, doi={10.1007/978-3-030-51100-5_9} }- Ying-hua Liu
Ye-hui Chen
Year: 2020
Research on Intelligent Diagnosis of Fault Data of Large and Medium-Sized Pumping Stations Under Information Evaluation System
ICMTEL
Springer
DOI: 10.1007/978-3-030-51100-5_9
Abstract
In order to improve the fault detection capability of large and medium-sized pump stations, the abnormal feature diagnosis of the fault data is required, and the intelligent diagnosis algorithm of the fault data of the large and medium-sized pump station under the information-based evaluation system is put forward. The fault data sensing information acquisition node distribution model of the large and medium-sized pump station is constructed, the multi-sensor fusion sampling method is adopted to sample the fault data of the large and medium-sized pump station, and the statistical feature quantity of the fault data of the large and medium-sized pump station is extracted. The fault data set of large and medium-sized pump station is used to detect and optimize the abnormal working condition of the fault data set of the large and medium-sized pump station, and the fault diagnosis of the large and medium-sized pump station is realized according to the detection result. The simulation results show that the accuracy of the fault data set of large and medium pump station is high, and the real-time and self-adaptability of the fault detection are better.
