| Summary: | Chiller fault detection based on neural network is a data-based analysis method. The fault detection efficiency relies on the quality of the training data and the mesasured data.The wavelet transfer method which can remove the measurement nosise is used to improve the detection efficiencies of chiller.The results show that wavelet transfer make the detection efficiencies of fault level improved, especially the first level. The increase of the first level detection rate will be able to timely identify the chiller fault, and take the measures to prevent further deterioration of chiller fault, which is of important significance to reduce energy consumption and improve the reliability of the air-conditioning system and ensure the indoor thermal comfort. The FDD (fault detection and diagnosis）strategy is validated through using ASHRAE Project data, which shows that the detection rate is improved obviously.
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