Method for diagnosing HVAC (heating, ventilation and air conditioning) system gradual failure based on deep learning

Abstract

The invention discloses a method for recognizing and diagnosing a HVAC system gradual fault based on the deep learning. The method uses the operational data of the health system to train the principalcomponent analysis (PCA) and the cyclic neural network (RNN) model, uses the PCA model to recognize the system fault, and uses the RNN model to diagnose the system change process and the gradual fault, thereby judging the specific fault of the system. Since the RNN belongs to the deep neural network, and has the characteristic of history information storing, the model can well fit a highly nonlinear time-varying system similar to HVAC. The method has high precision for early gradual fault diagnosis, and can accurately predict parameters of a unsteady system; and the data acquisition difficulty is greatly reduced. The method has a good effect on the gradual fault diagnosis, and is a feasible and high-precision gradual fault recognizing and diagnosing method.

Description

Technical field [0001] The invention relates to the field of operation and maintenance of heating, ventilation and air conditioning (HVAC) systems. Specifically, it includes deep learning, sensor technology, testing technology, and refrigeration principles. Background technique [0002] With the rapid development of the world economy, energy consumption is increasing day by day, and the problem of energy shortage has become increasingly prominent. Building energy consumption accounts for a considerable proportion of total energy consumption. In Europe and America, building energy consumption accounts for more than 40% of total energy consumption. With the development of the economy, the share of China's building energy consumption is also increasing year by year. As of 2010, China's building energy consumption accounted for 27.3%, making China the world's second largest building energy consumer after the United States. In building energy consumption, nearly half of the energy...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the deficiencies of the existing HVAC system gradient fault diagnosis methods, which rely on on-site diagnosis, low precision, and poor universality, the present invention provides a deep learning-based HVAC system gradient with high precision and good universal adaptability. Fault diagnosis method; the present invention focuses on solving three problems of existing HVAC system gradual fault diagnosis: (1) large dependence on fault data; (2) unable to accurately measure the system in an unsteady state; (3) unable to accurately measure gradual faults Forecast the development trend

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