| Related citation: | Kai Song,Bing Wang,Ming Diao,Hongquan Zhang,Zhenyu Zhang.Fault Detection and Recovery for Full Range of Hydrogen Sensor Based on Relevance Vector Machine[J].Journal of Harbin Institute Of Technology(New Series),2015,22(6):37-44.DOI:10.11916/j.issn.1005-9113.2015.06.005. |
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| Author Name | Affiliation | | Kai Song | School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China | | Bing Wang | College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China
Chinese Electron Science and Technology Conglomerate 49th Research Institute,Harbin 150001, China | | Ming Diao | College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China | | Hongquan Zhang | College of Automation,Harbin Engineering University,Harbin 150001,China | | Zhenyu Zhang | No.703 Research Institute of China Shipbuilding Industry Corporation,Harbin 150001, China |
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| Abstract: |
| In order to improve the reliability of hydrogen sensor, a novel strategy for full range of hydrogen sensor fault detection and recovery is proposed in this paper. Three kinds of sensors are integrated to realize the measurement for full range of hydrogen concentration based on relevance vector machine (RVM). Failure detection of hydrogen sensor is carried out by using the variance detection method. When a sensor fault is detected, the other fault-free sensors can recover the fault data in real-time by using RVM predictor accounting for the relevance of sensor data. Analysis, together with both simulated and experimental results, a full-range hydrogen detection and hydrogen sensor self-validating experiment is presented to demonstrate that the proposed strategy is superior at accuracy and runtime compared with the conventional methods. Results show that the proposed methodology provides a better solution to the full range of hydrogen detection and the reliability improvement of hydrogen sensor. |
| Key words: hydrogen concentration measurement full range fault detection fault recovery relevance vector machine |
| DOI：10.11916/j.issn.1005-9113.2015.06.005 |
| Clc Number:TP277 |
| Fund: |
