Water chilling unit fault diagnosis method and system based on Bayesian optimization LightGBM, and medium

Abstract

The invention discloses a water chilling unit fault diagnosis method and system based on Bayesian optimization LightGBM, and a medium. The method comprises the following steps: collecting and storing on-site historical data of a water chilling unit through a sensor; preprocessing the historical data; performing feature selection by using a two-step method combining an embedding method and a recursive feature elimination method; using the historical data subjected to data preprocessing and feature selection for training a LightGBM model, combining a Bayesian optimization algorithm with a ten-fold cross validation mode to determine an optimal hyper-parameter combination of the LightGBM model, and then obtaining a trained LightGBM diagnosis model; and preprocessing the real-time operation data and inputting the data into the diagnosis model to obtain a water chilling unit fault diagnosis result. Parameters of the diagnosis model can be rapidly determined, the operation state of the water chilling unit can be rapidly and accurately evaluated, key fault features can be extracted, and the method is used for guiding engineering practice and strengthening on-site operation maintenance of the water chilling unit.

Description

technical field [0001] The invention belongs to the refrigeration field, and in particular relates to a method, system and medium for diagnosing a chiller fault based on Bayesian optimized LightGBM. Background technique [0002] The chiller is the main energy-consuming equipment of the water-cooled central air-conditioning system, and its energy consumption accounts for more than 40% of the total energy consumption of the building. During the operation of the chiller, due to untimely maintenance, improper maintenance, and unreasonable operating parameter settings, etc., corresponding faults may occur. These faults may reduce the operating efficiency of the central air conditioning system, shorten the service life of the equipment, and may even lead to safety accidents. Fault Detection and Diagnosis (FDD) technology is based on the real-time monitoring of system operating parameters to determine whether there is a failure and determine the type of failure, which is helpful fo...

AI Technical Summary

Problems solved by technology

However, the performance of the LightGBM algorithm model is affected by its own hyperparameters, and the hyperparameter search efficiency of optimization algorithms such as grid search algorithm, random search algorithm, and particle swarm optimization algorithm is low.

Images