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| Research on Identifying Soft Faults of Gas Sensor Based onK-means Algorithm |
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Received:February 18, 2020
Revised:February 25, 2020
Accepted:February 25, 2020
Published Online:May 14, 2020
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| DOI： |
| KeyWord:Monitoring system; fault identification; neural network; clustering algorithm;Gas sensor |
| Author | Institution |
| HU Yu |
State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing;China;CCTEG Chongqing Research Institute;China |
| ZHOU Daiyong |
State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing |
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| Abstract: |
| For the problem of soft faults in underground gas sensor equipment, such as data drift, the data is lower than or higher than the normal value for a long time, the data changes periodically and the data has a large value,the K-means algorithm based on adaptive clustering points with contour coefficients is proposed to identify the types of soft faults in gas sensors. The method uses the gas sensor soft fault signal collected by the monitoring system to perform packet decomposition processing, and combines the RBF neural network to perform soft fault recognition training of the contour coefficient K-means adaptive algorithm.The K-means adaptive algorithm can adaptively optimize the cluster center point, calculate the optimal center point using the iterative loop of the cluster center point, and select the best cluster point for K-means clustering, so thereby identifying the type of fault of the soft fault signal. Experiments show that the adaptive contour coefficient K-means algorithm can effectively identify the type of gas sensor soft fault and improve the accuracy of the coal mine safety monitoring system data. |
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