Digital twin model for chiller fault diagnosis based on SSAE and transfer learning-Reference-Cited by-同舟云学术

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Digital twin model for chiller fault diagnosis based on SSAE and transfer learning

Published:2023-09 Issue: Volume:243 Page:110718
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Ma Xin,Chen Fan,Wang Zhihan,Li Ke,Tian Changbin

Funder

Shandong University

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Shandong Jianzhu University

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference45 articles.

1. A steady-state empirical model for evaluating energy efficient performance of centrifugal water chillers;Wang;Energy Build.,2017

2. Outlier detection via multiclass deep autoencoding Gaussian mixture model for building chiller diagnosis;Tra;Energy Build.,2022

3. Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis;Tra;Build. Environ.,2022

4. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: a comprehensive review;Mirnaghi;Energy Build.,2020

5. A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data;Li;Appl. Energy,2021

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks;Computers in Industry;2024-12

2. A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine;Engineering Applications of Artificial Intelligence;2024-11

3. Automated layout generation from sites to flats using GAN and transfer learning;Automation in Construction;2024-10

4. Digital twin-driven prognostics and health management for industrial assets;Scientific Reports;2024-06-11

5. Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach;Energy;2024-06

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