制冷技术2025，Vol.45Issue(2)：22-29,49,9.DOI:10.3969/j.issn.2095-4468.2025.02.104
基于卷积神经网络-微调的多联机故障诊断迁移研究
Research on Fault Diagnosis Migration for Variable Refrigerant Flow System Based on Convolutional Neural Network with Fine-Tuning Algorithm
摘要
Abstract
A fault diagnosis migration method based on CNN-FT(convolutional neural network with fine-tuning)is proposed,which leverages the informative prior knowledge from the source-domain variable refrigerant flow system to establish a diagnostic model for the target variable refrigerant flow system.Firstly,the source-domain is pre-trained,and the optimal CNN model is found by parameter optimization.Then the pre-training model is migrated to the target domain,and only a small amount of target data is used to train the top layer of CNN,with an accuracy of 86.71%.The previous network layer is thawed in turn for fine-tuning,and the accuracy rate is improved to 95.83%,which is significantly better than the target domain specific training(81.02%)and the source domain model direct migration(33.45%).关键词
多联机系统/故障诊断/卷积神经网络/迁移学习/微调Key words
Variable refrigerant flow system/Fault diagnosis/Convolutional neural network/Transfer learning/Fine-tuning分类
土木建筑引用本文复制引用
蒋敏辉,陈焕新,苟伟..基于卷积神经网络-微调的多联机故障诊断迁移研究[J].制冷技术,2025,45(2):22-29,49,9.基金项目
国家自然科学基金(No.51876070). （No.51876070）
