SVM-DS fusion based soft fault detection and diagnosis in solar water heaters. Issue 3 (May 2019)

• Record Type: Journal Article
• Authors: Jiang, Song; Lian, Minjie; Lu, Caiwu; Ruan, Shunling; Wang, Zhe; Chen, Baiyu
• Is Part Of: Energy exploration & exploitation. Volume 37:Issue 3(2019)
• Page Start: 1125
• Page End: 1146
• Publication Date: 2019-05
• Subjects: Solar water heaters -- fault diagnosis -- support vector machine -- D-S evidence theory -- multi-source information fusion; Power resources -- Periodicals; 333.79
• DOI: 10.1177/0144598718816604
• Languages: English
• ISSNs: 0144-5987

Abstract

As faults in the solar water heaters are structurally complicated and highly correlated, an approach of fault diagnosis on the basis of support vector machine and D-S evidence theory has been proposed in this study, attempting to enhance the system's thermal efficiency and ensure its safety. In the approach presented, information of audio conditions, temperature at the outlet of solar thermal collectors, hourly flow and hourly heat transfer rate are accessible, which facilitate the feature evidence and are diagnosed by using "one-against-one" multi-class support vector machine. Experiments are conducted to diagnose fault information fusion and the results show that the diagnosis approach proposed in this study is of high reliability with fewer uncertainties, indicating that the approach is capable to recognize and diagnose solar water heater faults accurately.