SVM-DS fusion based soft fault detection and diagnosis in solar water heaters

Author:

Jiang Song1ORCID,Lian Minjie12,Lu Caiwu1,Ruan Shunling1,Wang Zhe3,Chen Baiyu4

Affiliation:

1. School of Management, Xi'an University of Architecture and Technology, Shaanxi, China

2. Sinosteel Mining Co., Ltd, Beijing, China

3. School of Science, Xi'an Jiaotong University, Xi'an, China

4. College of Engineering, University of California, Berkeley, Berkeley, USA

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.

Funder

National Natural Science Foundation of China

Excellent doctorate cultivation fund of Xi'an University Of Architecture And Technology

Natural Science Foundation of Shaanxi Province

ey technology projects of safety prevention and control of major accidents in State Administration of work safety

Publisher

SAGE Publications

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

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