A fault diagnosis method for refrigeration system

Abstract

The fault diagnosis method for the refrigeration system comprises the following steps of through a simulation water chilling unit fault experiment, carrying out acquisition and processing to obtain training unit data and test unit data; Setting the node number and the layer number of the deep neural network; Establishing a deep neural network model, and determining a topological structure of the deep neural network, the topological structure comprising an input layer number, a weight value and a threshold value of the deep neural network; Determining the number of training steps of the deep neural network; Training in the deep neural network model by applying the training group data to obtain a fault diagnosis model; Training the deep neural network by adopting a small-batch momentum random gradient descent method; Setting a learning rate of the deep neural network; Calculating a loss function C; Optimizing the learning rate by adopting a simulated annealing algorithm; Obtaining an optimal learning rate; Training ending conditions are met, and a trained fault diagnosis model is obtained; And performing fault diagnosis on the test group data in the step S2 by using the trained faultdiagnosis model to obtain a fault diagnosis result.

Description

technical field [0001] The invention belongs to the refrigeration field, and in particular relates to a method for diagnosing a refrigeration system fault based on SA-DNN. Background technique [0002] Modern industrial and civil buildings are inseparable from refrigeration systems. The components of refrigeration systems are complex, resulting in frequent failures and various types during operation. The occurrence of faults affects the cooling effect, increases energy consumption, shortens the service life of the equipment, and brings potential safety hazards. Therefore, it is particularly important to ensure the operation quality of the refrigeration system. Real-time status detection and fault diagnosis of the refrigeration system can not only ensure the normal operation of the refrigeration system, but also detect and repair problems in time. In the past few decades, the fault diagnosis and detection technology of the refrigeration system has been a research hotspot. In...

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Problems solved by technology

In addition, neural networks usually have problems such as difficult to adjust the learning rate and difficult to determine the initial weight threshold.

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