基于L2-BP神经网络的变风量空调系统TRNSYS仿真模型故障诊断
Fault Diagnosis of Variable Air Volume Air Conditioning System TRNSYS Simulation Model Based on L2-BP Neural Network
摘要: 变风量空调系统的故障检测与诊断对于提高建筑环境质量和实现节能至关重要。该系统故障类型复杂、数据量巨大、非线性特性强，传统的基于模型和基于规则的方法较难应用。本文提出了一种基于数据驱动的L2-BP神经网络故障诊断方法，首先通过TRNSYS模拟软件建立VAV空调系统仿真模型，获得四种常见故障的仿真模拟数据，然后利用运行数据和实际数据对L2-BP神经网络故障诊断模型进行验证。结果表明，本文提出的故障诊断模型能够克服单一方法的局限性，针对单一故障进行有效识别。
Abstract: Fault detection and diagnosis of variable air volume air conditioning systems is crucial to improving the quality of building environment and achieving energy conservation. The system has complex fault types, huge data volume, and strong nonlinear characteristics. Traditional model-based and rule-based methods are difficult to apply. This paper proposes a data-driven L2-BP neural network fault diagnosis method. First, a VAV air conditioning system simulation model is established through TRNSYS simulation software to obtain simulation data of four common faults. Then, the L2-BP neural network fault diagnosis model is verified using operating data and actual data. The results show that the fault diagnosis model proposed in this paper can overcome the limitations of a single method and effectively identify a single fault.
文章引用：龚婉婷. 基于L2-BP神经网络的变风量空调系统TRNSYS仿真模型故障诊断[J]. 建模与仿真, 2024, 13(6): 6493-6502. https://doi.org/10.12677/mos.2024.136593

参考文献

[1] Pérez-Lombard, L., Ortiz, J. and Pout, C. (2008) A Review on Buildings Energy Consumption Information. Energy and Buildings, 40, 394-398. [Google Scholar] [CrossRef] 
[2] Lee, D. and Cheng, C. (2016) Energy Savings by Energy Management Systems: A Review. Renewable and Sustainable Energy Reviews, 56, 760-777. [Google Scholar] [CrossRef] 
[3] Beghi, A., Brignoli, R., Cecchinato, L., Menegazzo, G. and Rampazzo, M. (2015) A Data-Driven Approach for Fault Diagnosis in HVAC Chiller Systems. 2015 IEEE Conference on Control Applications (CCA), Sydney, 21-23 September 2015, 966-971. [Google Scholar] [CrossRef] 
[4] Chakraborty, D. and Elzarka, H. (2019) Early Detection of Faults in HVAC Systems Using an XGBoost Model with a Dynamic Threshold. Energy and Buildings, 185, 326-344. [Google Scholar] [CrossRef] 
[5] Beghi, A., Brignoli, R., Cecchinato, L., Menegazzo, G., Rampazzo, M. and Simmini, F. (2016) Data-Driven Fault Detection and Diagnosis for HVAC Water Chillers. Control Engineering Practice, 53, 79-91. [Google Scholar] [CrossRef] 
[6] 王海涛, 陈友明, 陈永康, 等. 用参数自整定模型在线检测空气处理机组故障[J]. 土木建筑与环境工程, 2012, 34(1): 85-90.
[7] 王海涛, 陈友明. VAV空气处理机组故障检测与诊断方法及应用[J]. 北京工业大学学报, 2014, 40(2): 290-295.
[8] House, J.M., Vaezi-Nejad, H. and Whitcomb, J.M. (2001) An Expert Rule Set for Fault Detection in AIR-handling Units. ASHRAE Transactions.
[9] Dey, D. and Dong, B. (2016) A Probabilistic Approach to Diagnose Faults of Air Handling Units in Buildings. Energy and Buildings, 130, 177-187. [Google Scholar] [CrossRef] 
[10] Wang, H., Feng, D. and Liu, K. (2021) Fault Detection and Diagnosis for Multiple Faults of VAV Terminals Using Self-Adaptive Model and Layered Random Forest. Building and Environment, 193, Article 107667. [Google Scholar] [CrossRef] 
[11] Wang, H., Chen, Y., Chan, C.W.H. and Qin, J. (2011) A Robust Fault Detection and Diagnosis Strategy for Pressure-Independent VAV Terminals of Real Office Buildings. Energy and Buildings, 43, 1774-1783. [Google Scholar] [CrossRef] 
[12] 杨思钰, 杨学宾, 李鑫海, 等. 变风量空调阀门卡死故障检测阈值在线训练策略[J]. 东华大学学报(自然科学版), 2023, 49(4): 123-129.
[13] Li, C.J. and Lee, H. (2005) Gear Fatigue Crack Prognosis Using Embedded Model, Gear Dynamic Model and Fracture Mechanics. Mechanical Systems and Signal Processing, 19, 836-846. [Google Scholar] [CrossRef] 
[14] 中华人民共和国国家技术监督局. GB50189-2015公共建筑节能标准[S]. 中国: 建筑工业出版社, 2005.
[15] Huang, T., Zhang, Q., Tang, X., Zhao, S. and Lu, X. (2021) A Novel Fault Diagnosis Method Based on CNN and LSTM and Its Application in Fault Diagnosis for Complex Systems. Artificial Intelligence Review, 55, 1289-1315. [Google Scholar] [CrossRef] 
[16] 飞思科技产品研发中心. 神经网络理论与MATLAB7实现[M]. 北京: 电子工业出版社, 2005.

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