Article
Construction & Building Technology
Fanyong Cheng, Wenjian Cai, Xin Zhang, Huanyue Liao, Can Cui
Summary: This paper introduces a novel fault detection and diagnosis method using multiscale convolutional neural networks for Air Handling Unit in HVAC system. By utilizing three different scale kernels and an end-to-end learning strategy, the proposed method can effectively extract discriminative features to improve diagnostic performance. The comparison results show that the proposed MCNNs-based FDD method outperforms other commonly used methods.
ENERGY AND BUILDINGS
(2021)
Article
Computer Science, Artificial Intelligence
Chengdong Li, Yulong Yu, Linyuan Shang, Hanyuan Zhang, Yongqing Jiang
Summary: This paper proposes an AHU fault diagnosis model based on probabilistic slow feature analysis (PSFA) and attention residual network (AResNet) to improve the accuracy of fault diagnosis. The proposed model is built using the PSFA method and AResNet, and experiments are conducted on the experimental data with different noise levels. The results show that the proposed PSFA-AResNet model outperforms other popular methods in fault diagnosis performance under three different noise levels.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Construction & Building Technology
Hanyuan Zhang, Chengdong Li, Qinglai Wei, Yunchu Zhang
Summary: This paper introduces a novel SFA algorithm STBDSFA based on sparse feature representation to enhance the effectiveness of dynamic AHU system FDD. The algorithm shows high fault detection and diagnosis rates on experimental datasets.
ENERGY AND BUILDINGS
(2022)
Article
Green & Sustainable Science & Technology
Tingting Li, Mengqiu Deng, Yang Zhao, Xuejun Zhang, Chaobo Zhang
Summary: A proactive AHU fault isolation method is proposed in this study to introduce dynamic disturbances and generate additional diagnostic information for isolating serious faults. Proactive fault isolation rules are developed based on the additional diagnostic information, which have been evaluated on a simulated air-conditioning system to effectively isolate the serious faults of AHUs.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Construction & Building Technology
Zhiqiang Liu, Zhenlin Huang, Jiaqiang Wang, Chang Yue, Sungmin Yoon
Summary: This study introduced a novel fault detection, diagnosis, and self-calibration method based on Bayesian inference and virtual sensing to estimate various faults, including sensor and component faults. The method effectively recognized system operating state and identified fault positions, reducing deviation rate by up to 98.0% in most fault scenarios.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Yulong Yu, Hanyuan Zhang, Wei Peng, Ruiqi Wang, Chengdong Li
Summary: This paper proposes an images based deep learning model for fault diagnosis of AHU. The method extracts and ranks features using kernel slow feature analysis, transforms them into two-dimensional grayscale images, and uses convolutional neural networks (CNNs) for fault diagnosis.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Woo-Seung Yun, Won-Hwa Hong, Hyuncheol Seo
Summary: The study proposed a data-driven maintenance scheme for fault detection and diagnosis of AHUs in HVAC systems, aiming to improve system reliability and consider undefined states. Experimental results showed high performance in distinguishing between undefined and defined data, with higher FDD performance for defined states and facilitating maintenance in building HVAC systems.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Construction & Building Technology
Hanyuan Zhang, Chengdong Li, Ding Li, Yunchu Zhang, Wei Peng
Summary: This paper presents an enhanced kernel slow feature analysis (SFA) based fault detection and diagnosis (FDD) scheme for nonlinear AHU systems, utilizing novel algorithms such as threeway data based kernel SFA (TBKSFA) and kernel discriminant SFA (KDSFA) to improve performance in capturing dynamic characteristics and identifying fault patterns. Experimental results show significant improvements compared to other popular methods.
ENERGY AND BUILDINGS
(2021)
Article
Chemistry, Analytical
Huanyue Liao, Wenjian Cai, Fanyong Cheng, Swapnil Dubey, Pudupadi Balachander Rajesh
Summary: An online data-driven diagnosis method combining rule-based method and CNNs was proposed for fault diagnosis of AHU in HVAC systems, achieving an accurate identification of fault types with a 99.15% accuracy in offline testing and fast online detection. Experimental results validate the performance improvement of the proposed RACNN method.
Article
Construction & Building Technology
Bingjie Wu, Wenjian Cai, Haoran Chen, Xin Zhang
Summary: A novel simultaneous fault diagnosis model, CC-RF, is proposed and validated with on-site experiments, achieving high accuracy and performance in diagnosing both single and simultaneous faults. The model is proven to be scalable with reasonable training time and shows good competence in online analysis.
ENERGY AND BUILDINGS
(2021)
Article
Automation & Control Systems
Ke Yan, Xinke Chen, Xiaokang Zhou, Zheng Yan, Jianhua Ma
Summary: Physics theory integrated machine learning models enhance the interpretability and performance of AI techniques for real-world industrial applications, such as AHU FDD. Traditional machine learning-based FDD models have high classification accuracy with sufficient training data but lack physical interpretation. This article presents a physical model integrated WGAN model for AHU FDD with insufficient training data. The proposed solution significantly improves model interpretability and outperforms existing methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Construction & Building Technology
Cheng Fan, Xuyuan Liu, Peng Xue, Jiayuan Wang
Summary: This study proposes a novel semi-supervised FDD method using neural networks, which adopts the self-training strategy for semi-supervised learning and has been tested for fault diagnosis and unseen fault detection. Statistical characterization of key learning parameters has been conducted through data experiments, showing that the method can effectively enhance model generalization performance by utilizing large amounts of unlabeled data.
ENERGY AND BUILDINGS
(2021)
Article
Automation & Control Systems
Wei-Ting Yang, Marco S. Reis, Valeria Borodin, Michel Juge, Agnes Roussy
Summary: Process monitoring is critical in manufacturing industries. This paper proposes an interpretable unsupervised machine learning model based on Bayesian Networks (BN) for fault detection and diagnosis. The model combines data-driven induction with domain knowledge and displays causal interactions in a graphical form. The proposed fault detection scheme consists of two levels of monitoring and uses local indices for fault diagnosis.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Engineering, Environmental
Nan Liu, Minggang Hu, Ji Wang, Yujia Ren, Wende Tian
Summary: Risks in chemical plants can be categorized as Black Swan events and Gray Rhino events. To address the challenge of root cause diagnosis in Gray Rhino events, a strong relevant mechanism Bayesian network (SRMBN) method is proposed for fault detection and diagnosis. The SRMBN is constructed through structure learning and parameter learning, and utilizes Bayesian interval estimated index and Bayesian contribution index for fault detection and diagnosis.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Automation & Control Systems
Viet Tra, Manar Amayri, Nizar Bouguila
Summary: Incomplete data is a common problem in data-driven energy and building solutions. This paper proposes a new framework called DV-MPPCA to address the fault detection problem in buildings with incomplete data. The framework combines a variational autoencoder (VAE) for data compression and a mixture of principal component analyzers (MPPCA) for density estimation. Experimental results demonstrate the exceptional performance of DV-MPPCA, even with high missing rates.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Hong Tang, Shengwei Wang
Summary: This study develops a novel model-based predictive dispatch strategy for hybrid building energy systems to maximize economic benefits in electricity markets by considering the correlation between multiple flexibility resources and grid control signals, which can lower electricity costs in the electricity market.
Article
Energy & Fuels
Wenzhuo Li, Shengwei Wang
Summary: This paper proposes a fully distributed optimal control approach for HVAC systems to be implemented in IoT-enabled building automation networks. It utilizes the Incremental Cost Consensus (ICC) algorithm and the average consensus algorithm to optimize the individual rooms' outdoor air volume and estimate the outdoor air volume mismatch. Through tests and comparisons, it is found that the proposed approach with the fully connected topology outperforms existing hierarchical distributed approaches, demonstrating higher robustness, lower computation complexity, and higher optimization efficiency.
Article
Thermodynamics
Shaobo Sun, Shengwei Wang, Kui Shan
Summary: This study quantifies the measurement uncertainties of water flow meters in multiple water-cooled chiller systems using a Bayesian approach, proposing a quantification strategy that performs well in quantifying both systematic and random uncertainties with acceptable accuracy. The strategy can be used to optimize the control of chiller systems and improve their reliability.
APPLIED THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Hangxin Li, Shengwei Wang
Summary: Constructing nearly and net zero-energy buildings presents the challenge of achieving energy goals post-occupancy, with traditional design methods showing high risk of failure. New ZEB design standards now focus on post-occupancy performance evaluation, posing additional challenges and calling for effective design methods.
BUILDING SIMULATION
(2022)
Article
Thermodynamics
Hangxin Li, Shengwei Wang
Summary: Model predictive control (MPC) method is superior in enhancing system performance, but is hindered by forecast uncertainties in operation. Different methods, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC), have been proposed to mitigate these uncertainties. However, there is limited knowledge about their year-round performance and relative performance, especially in the utilization of building flexibility-resources.
Article
Thermodynamics
J. I. A. J. I. N. G. HUANG, H. Y. U. N. S. O. O. YOON, O. J. A. S. PRADHAN, T. E. R. E. S. A. WU, J. I. N. WEN, Z. H. E. N. G. O'NEILL, K. A. S. I. M. S. E. L. C. U. K. Candan
Summary: This study developed a data-driven method for constructing AFDD baseline based on information entropy. Evaluation results showed that the proposed method has similar or better accuracy in fault detection compared to traditional methods.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2022)
Article
Energy & Fuels
Shaobo Sun, Kui Shan, Shengwei Wang
Summary: This study proposed an online robust sequencing control strategy for chiller plants under low-quality and uncertain flow measurements, which effectively reduced the impacts of flow measurement uncertainties and improved the performance of chiller plants. The uncertainty processing model accurately quantified the measurement uncertainties of water flow rates, leading to significant reductions in root-mean-square error of cooling loads, total switching number of chillers, and cumulative unmet cooling load. The proposed control strategy showed the ability to tolerate flow measurement uncertainties.
Article
Construction & Building Technology
Xiuming Li, Sida Lin, Kui Shan, Zongwei Han, Shengwei Wang
Summary: This study proposes a self-organization method based on the Semi-tensor product to solve logic control problems in distributed building automation systems. The method is demonstrated through simulation and experiment, showing its effectiveness in generating control logic through self-organization.
JOURNAL OF BUILDING ENGINEERING
(2022)
Editorial Material
Thermodynamics
Zheng O'Neill, Jin Wen
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2022)
Article
Energy & Fuels
Yimin Chen, Jin Wen, Ojas Pradhan, L. James Lo, Teresa Wu
Summary: Fault detection and diagnosis (FDD) technologies are crucial for ensuring satisfactory building performance. This study proposes a novel method based on discrete Bayesian Network (DisBN) for diagnosing cross-level faults in HVAC systems. Experimental results demonstrate the effectiveness of the proposed method.
Article
Computer Science, Information Systems
Jiajing Huang, Hyunsoo Yoon, Teresa Wu, Kasim Selcuk Candan, Ojas Pradhan, Jin Wen, Zheng O'Neill
Summary: Sampling is a technique used to select a representative subset of data that captures the characteristics of the entire dataset. This study proposes a model-free metric called Eigen-Entropy (EE) based on information entropy for multivariate datasets. EE can measure the composition of the dataset, such as its heterogeneity or homogeneity, and support sampling decisions. Two use cases demonstrate the utility of EE in improving classification performance for imbalanced datasets and enhancing fault detection in building systems.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Zhuang Zheng, Shengwei Wang, Wenzhuo Li, Xiaowei Luo
Summary: This paper proposes a novel voltage control strategy that regulates the on/off states of AC clusters to address voltage issues caused by high PV penetrations. The strategy includes temperature priority-based on/off control, real-time optimal demand response resources dispatch, distributed sensing of ACs, and flexibility capacity estimation. The strategy is validated to be effective and scalable, and is incorporated into a hierarchical control framework for smart grid voltage control.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Review
Energy & Fuels
Zhelun Chen, Zheng O'Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin, Shohei Miyata, Seungjae Lee, Chou Shen, Roberto Chiosa, Marco Savino Piscitelli, Alfonso Capozzoli, Franz Hengel, Alexander Kuehrer, Marco Pritoni, Wei Liu, John Clauss, Yimin Chen, Terry Herr
Summary: This paper reviews and summarizes the literature on data-driven fault detection and diagnostics (FDD) for building HVAC systems, focusing on the process, systems studied, and evaluation metrics. It identifies challenges such as real-building deployment, performance evaluation, scalability, interpretability, cyber security, data privacy, and user experience that data-driven FDD methods still face despite promising performance reported in the literature.
Article
Construction & Building Technology
Wenxuan Zhao, Hangxin Li, Shengwei Wang
Summary: This study proposes a novel outdoor air ventilation strategy for high-tech cleanrooms, which enables maximum energy savings and efficiency. The strategy determines the optimal outdoor air volume by theoretically calculating the energy differential. Results show that the traditional fully coupled AHU system can achieve annual free cooling hours ranging from 662 to 2,537 in 31 major Chinese cities. Moreover, the proposed strategy achieves 8% energy savings in transition months and significant electricity and primary energy savings in a year.
BUILDING AND ENVIRONMENT
(2023)
Article
Multidisciplinary Sciences
Jessica Granderson, Guanjing Lin, Yimin Chen, Armando Casillas, Jin Wen, Zhelun Chen, Piljae Im, Sen Huang, Jiazhen Ling
Summary: This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. The dataset covers the most common HVAC systems and configurations in commercial buildings, across different climates, fault types, and fault severities. The data set is a significant expansion of that first published by the lead authors in 2020, and includes both typical and less typical measurements encountered in existing buildings.
Article
Thermodynamics
Hai Zhao, Puzhen Gao, Xiaochang Li, Ruifeng Tian, Hongyang Wei, Sichao Tan
Summary: This study numerically investigates the interaction between flow-induced vibration and forced convection heat transfer in a tube bundle. The results show that the impact of flow-induced vibration on heat transfer varies in different flow velocity regions.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Rohit Chintala, Jon Winkler, Sugirdhalakshmi Ramaraj, Xin Jin
Summary: The current state of fault detection and diagnosis for residential air-conditioning systems is expensive and not suitable for widespread implementation. This paper proposes a cost-effective solution by introducing an automated fault detection algorithm as a screening step before more expensive tests can be conducted. The algorithm uses home thermostats and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
A. Azimi, N. Basiri, M. Eslami
Summary: This paper presents a novel optimization algorithm for improving the water-film cooling system of photovoltaic panels, resulting in a significant increase in net energy generation.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Duc-Thuan Phung, Chin-Hsiang Cheng
Summary: In this study, a novel CFDMD model is used to analyze and investigate the behavior of thermal-lag engines (TLE). The study shows that the CFDMD model effectively captures the thermodynamic behavior of the working gas and the dynamic behavior of the engine mechanism. Additionally, the study explores the temporal evolution of engine speed and the influence of various parameters on shaft power and brake thermal efficiency. The research also reveals the existence of a thermal-lag phenomenon in TLE.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Haiying Yang, Yinjie Shen, Lin Li, Yichen Pan, Ping Yang
Summary: The purpose of this article is to find a measure to improve the interfacial thermal transfer of graphene/silicon heterojunction. Through molecular dynamics simulation, it is found that surface modification can significantly reduce the thermal resistance, thereby improving the thermal conductivity of the graphene/silicon interface.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Qiong Wu, Yancheng Wang, Haonan Zhou, Xingye Qiu, Deqing Mei
Summary: This article introduces a visible methanol steam reforming microreactor, which uses an optical crystal as an observation window and measures the reaction temperature in real-time using infrared thermography. The results show that under lower oxygen to carbon ratio conditions, the microreactor has a higher heating rate and a stable gradient in temperature distribution.
APPLIED THERMAL ENGINEERING
(2024)
Review
Thermodynamics
Giulia Manco, Umberto Tesio, Elisa Guelpa, Vittorio Verda
Summary: In the past decade, there has been a growing interest in studying energy systems for the combined management of power vectors. Most of the published works focus on finding the optimal design and operations of Multi Energy Systems (MES). However, for newcomers to this field, understanding how to achieve the desired optimization details while controlling computational expenses can be challenging and time-consuming. This paper presents a novel approach to analyzing the existing literature on MES, with the aim of guiding practical development of MES optimization. Through the discussion of six case studies, the authors provide a mathematical formulation as a reference for building the model and emphasize the impact of different aspects on the problem nature and solver selection. In addition, the paper also discusses the different approaches used in the literature for incorporating thermal networks and storage in the optimization of multi-energy systems.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Xuepeng Yuan, Caiman Yan, Yunxian Huang, Yong Tang, Shiwei Zhang, Gong Chen
Summary: In this study, a multi-scale microgroove wick (MSMGW) was developed by laser irradiation, which demonstrated superior capillary performance. The surface morphology and performance of the wick were affected by laser scan pitch, laser power, repetition frequency, and scanning speed. The MSMGW showed optimal capillary performance in alumina material and DI water as the working fluid.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Maofei Mei, Feng Hu, Chong Han
Summary: This paper proposes an effective local search method based on detection of droplet boundaries for understanding the dynamic process of droplet growth during dropwise condensation. The method is validated by comparing with experimental data. The present simulation provides an effective approach to more accurately predict the nucleation site density in future studies.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Rahul Kumar Sharma, Ashish Kumar, Dibakar Rakshit
Summary: The study explores the use of phase change materials (PCM) as a retrofit with Heating Ventilation and Air-conditioning systems (HVAC) to reduce energy consumption and improve air quality. By incorporating PCM with specific thickness and fin configurations, significant energy savings can be achieved in comparison to standard HVAC systems utilizing R134a. This research provides policymakers with energy-efficient and sustainable solutions for HVAC systems to combat climate change.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Zhenhua Ren, Xiangjin Meng, Xingang Qi, Hui Jin, Yunan Chen, Bin Chen, Liejin Guo
Summary: This paper investigates the heat transfer mechanism and factors influencing thermal radiation in the process of supercritical water gasification (SCWG) of coal, and proposes a comprehensive numerical model to simulate the process. Experimental validation results show that thermal radiation accounts for a significant proportion of the total heat exchange in the reactor and a large amount of radiant energy exists in the important spectral range of supercritical water. Enhancing radiative heat transfer can effectively increase the temperature of the reaction medium and the gasification rate.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Mauro Abela, Mauro Mameli, Sauro Filippeschi, Brent S. Taft
Summary: Pulsating Heat Pipes (PHP) are passive two-phase heat transfer devices with a simple structure and high heat transfer capabilities. The actual unpredictability of their dynamic behavior during startup and thermal crisis hinders their large-scale application. An experimental apparatus is designed to investigate these phenomena systematically. The results show that increasing the number of evaporator sections and condenser temperature improves the performance of PHP. The condenser temperature also affects the initial liquid phase distribution and startup time.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Ke Gan, Ruilian Li, Yi Zheng, Hui Xu, Ying Gao, Jiajie Qian, Ziming Wei, Bin Kong, Hong Zhang
Summary: A 3-dimensional enhanced heat pipe radiator has been developed to improve heat dissipation and temperature uniformity in cooling high-power electronic components. Experimental results show that the radiator has superior heat transfer performance compared to a conventional aluminum fin radiator under different heating powers and wind speed conditions.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Xinyi Zhang, Shuzhong Wang, Daihui Jiang, Zhiqiang Wu
Summary: This study focuses on recovering waste heat from blast furnace slag using dry centrifugal pelletizing technology. A comprehensive two-dimensional model was developed to analyze heat transfer dynamics and investigate factors influencing heat exchange efficiency. The findings have important implications for optimizing waste heat recovery and ensuring safe operations.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Xincheng Wu, An Zou, Qiang Zhang, Zhaoguang Wang
Summary: The boosting heat generation rate of high-performance processors is challenging traditional cooling techniques. This study proposes a combined design of active jet intermittency and passive surface modification to enhance heat transfer.
APPLIED THERMAL ENGINEERING
(2024)
