Sensor Fault Diagnosis for Air Handling Unit of Heating Ventilation and Air Conditioning Based on Voting Mechanism

被引：0

作者：

Yan Y. ^{[1
,2
]}

Cai J. ^{[1
,2
,3
]}

Wu Q. ^{[4
]}

Zhang X. ^{[5
]}

Yang Y. ^{[1
,2
]}

机构：

[1] School of Automation, Nanjing University of Information Science and Technology, Nanjing

[2] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing

[3] Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan

[4] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai

[5] College of Electrical Engineering, Zhejiang University, Hangzhou

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Boltzmann machine; Decentralized; Fault diagnosis; Sensor; Voting mechanism;

D O I：

10.11999/JEIT221506

中图分类号：

学科分类号：

摘要：

Existing fault diagnosis methods developed for Air Handling Unit (AHU) of Heating Ventilation and Air Conditioning (HVAC) tend to be centralized. The few distributed methods usually require solving a large number of time-consuming optimization problems, making it impossible to complete fault diagnosis in a timely manner. In response to the above challenges, a distributed fault diagnosis method based on a novel voting mechanism is proposed. In this method, a novel voting mechanism is proposed to establish a Boltzmann machine to describe the sensor network, determine the edge weights of the Boltzmann machine through mutual voting among sensors, and iterate over the state of the Boltzmann machine, which is also the state of the sensors, based on the edge weights to locate the sensor faults. Moreover, a novel voting strategy based on Euclidean distance is designed to determine the voting values. Additionally, a method is developed to reset the Boltzmann machine’s weight matrix by adding a node to the Boltzmann machine, which maintains the original voting relationship among the sensors while symmetrizing the Boltzmann machine to ensure convergence of the iteration of the Boltzmann machine state. This method does not need solving many optimization problems, leading to lower computational requirements compared to existing distributed methods. The proposed method is validated using actual data provided by ASHRAE Project RP-1312. The experimental results show that the proposed method can accurately and efficiently diagnose bias and drift faults in AHU sensors. © 2024 Science Press. All rights reserved.

引用

页码：258 / 266

页数：8

共 17 条

[1] KATIPAMULA S, BRAMBLEY M R., Methods for fault detection, diagnostics, and prognostics for building systems - a review, part I[J], HVAC& R Research, 11, 1, pp. 3-25, (2005)
[2] LIAO Huanyue, CAI Wenjian, CHENG Fanyong, Et al., An online data-driven fault diagnosis method for air handling units by rule and convolutional neural networks, Sensors, 21, 13, (2021)
[3] YAN Ying, CAI Jun, LI Tao, Et al., Fault prognosis of HVAC air handling unit and its components using hidden-semi Markov model and statistical process control, Energy and Buildings, 240, (2021)
[4] YAN Xiao'an, Minping JIA, Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings[J], Mechanical Systems and Signal Processing, 122, pp. 56-86, (2019)
[5] SHAO Haidong, XIAO Yiming, YAN Shen, Simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis[J/OL], Journal of Mechanical Engineering, 2022, pp. 1-10, (2022)
[6] WANG Luyao, WU Bin, DU Zhimin, Et al., Sensor fault detection and diagnosis for data center air conditioning system based on LSTM neural network[J], CIESC Journal, 69, S2, pp. 252-259, (2018)
[7] SHAO Haidong, YAN Shen, XIAO Yiming, Semi-supervised bearing fault diagnosis using improved graph attention network under time-varying speeds[J/OL], Journal of Electronics & Information Technology, 2022, pp. 1-9, (2022)
[8] LIU Jingjing, ZHANG Min, WANG Hai, Et al., Sensor fault detection and diagnosis method for AHU using 1-D CNN and clustering analysis, Computational Intelligence and Neuroscience, 2019, (2019)
[9] WANG Zhuozheng, DONG Yingjie, LIU Wei, Et al., A novel fault diagnosis approach for chillers based on 1-D convolutional neural network and gated recurrent unit, Sensors, 20, 9, (2020)
[10] REPPA V, PAPADOPOULOS P, POLYCARPOU M M, Et al., A distributed architecture for HVAC sensor fault detection and isolation[J], IEEE Transactions on Control Systems Technology, 23, 4, pp. 1323-1337, (2015)

← 1 2 →