Please use this identifier to cite or link to this item:
Title: Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units
Authors: Wu, Bingjie
Cai, Wenjian
Cheng, Fanyong
Chen, Haoran
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Wu, B., Cai, W., Cheng, F. & Chen, H. (2022). Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units. Energy and Buildings, 257, 111608-.
Journal: Energy and Buildings
Abstract: An advanced deep learning-based method that employs transformer architecture is proposed to diagnose the simultaneous faults with time-series data. This method can be directly applied to transient data while maintaining the accuracy without a steady-state detector so that the fault can be diagnosed in its early stage. The transformer architecture adopts a novel multi-head attention mechanism without involving any convolutional and recurrent layers as in conventional deep learning methods. The model has been verified by an on-site air handling unit with 6 single-fault cases, 7 simultaneous-fault cases, and normal operating conditions with satisfactory performances of test accuracy of 99.87%, Jaccard score of 99.94%, and F1 score of 99.95%. Besides, the attention distribution reveals the correlations between features to the corresponding fault. It is found that the length of the sliding window is key to the model performance, and a trade-off is made for the window length between the model performance and the diagnosis time. Based on the similar idea, another sequence-to-vector model based on the gated recurrent unit (GRU) is proposed and benchmarked with the transformer model. The results show that the transformer model outperforms the GRU model with a better Jaccard score and F1 score in less training time.
ISSN: 0378-7788
DOI: 10.1016/j.enbuild.2021.111608
Schools: School of Electrical and Electronic Engineering 
Research Centres: SJ-NTU Corporate Lab
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Sep 30, 2023

Web of ScienceTM
Citations 20

Updated on Oct 1, 2023

Page view(s)

Updated on Oct 3, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.