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.
Highlights
As the social demand for energy has been on the rise, solar water heaters (SWHs), an efficient means exploiting solar thermal energy, have gained popularity
Based on the real-time data collected in the system, the above-mentioned extracting methods are adopted to process the audio data of faults in solar water heating system (SWHS) and find their weighted averages
Audio data are difficult to be digitized, the sound of water heater collected can be transformed into waveform data which are used in research (Fu and Liu, 2017b)
Summary
As the social demand for energy has been on the rise, solar water heaters (SWHs), an efficient means exploiting solar thermal energy, have gained popularity. After studying the experimental data of faults including dusty solar collector, heating empty collector, heat transfer inefficiency and leakage due to the loose valve, we find the frame of discernment for the SWH in diagnosis H1⁄4{F1, F2, F3, F4}, where F1 refers to dusty solar thermal collector,. The mean value, variance, the mean value of slope, variance of slope and normalized cumulant, as well as the mean value and variance of all the waveform data are calculated (Kang et al, 2018) By combining all these values into a vector, the amplitude spectrum feature in frequency domain of samples is determined, which displays the waveform and energy distribution of signals in frequency domain.
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