Hangzhou Dianzi University

Qixing Zeng

Qiang Ding

Shuang Liu

Dechuan Chen

Chong Shi

A fault detection method for refrigeration units based on an improved deep learning model is provided, including the following steps: S: obtaining operating parameters of a refrigeration unit in a normal operating state and in states with different fault types as data sets; S: detecting local outliers in the data set by using a local outlier factor algorithm and removing the local outliers, and then expanding the data set by using adaptive synthetic sampling; S: normalizing the data set; S: constructing a fault detection model; and S: inputting the parameters of the tested refrigeration unit into the fault detection model, and judging whether the tested refrigeration unit has a fault and the type of the fault.

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Hangzhou, China

Qixing Zeng