Water chilling unit fault detection method based on improved FastICA

Abstract

The invention discloses a water chilling unit fault detection method based on an improved FastICA. The water chilling unit fault detection method introduces relaxation factors on the basis of the traditional FastICA, changes the original iterative manner, and effectively reduces the dependence of the algorithm on initial values. The water chilling unit fault detection method comprises two stages of ''offline modeling'' and ''online detection''. The ''offline modeling'' stage comprises the steps of: firstly, carrying out steady state treatment on a normal data sample; then extracting independent component information of the data by adopting the FastICA algorithm which introduces the relaxation factors; and finally calculating statistics I2 and SPE, and determining control limits by adopting a kernel density estimation method. The ''online detection'' stage comprises the steps of processing newly acquired refrigeration process data according to a model, calculating statistics and comparing the statistics with the control limits to judge whether the refrigeration process operates normally. The water chilling unit fault detection method based on the improved FastICA reduces the dependence of the FastICA algorithm on initial values, and ensures the safety and stability of the water chilling unit in the refrigeration process.

Description

technical field [0001] The invention relates to the technical field of data-driven fault diagnosis, in particular to a fault detection method for the refrigeration process of a chiller in the field of HVAC. Background technique [0002] Modern industrial and civil buildings generally use large water-cooled chillers as the cold source of central air conditioning. During the operation of the unit, various failures will inevitably occur. If they are not eliminated in time, the system operation parameters will seriously deviate from the required set values, which will bring discomfort to the indoor personnel and increase the energy consumption of the system. , shorten the service life of the equipment. Therefore, it is very important to detect and diagnose the fault of the chiller, prevent and eliminate the fault in time, and maintain the normal and efficient operation of the chiller. In the past few decades, the fault detection and diagnosis of the chiller (fault detection and ...

AI Technical Summary

Problems solved by technology

Although ICA has been well applied in biomedical signal processing, mixed speech signal separation, blind source separation, etc., its application in chiller process monitoring has rarely been reported.

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