Fault Diagnosis Based on Fusion of Residuals and Data for Chillers DOI Open Access
Zhanwei Wang, Boyang Liang, Jingjing Guo

и другие.

Processes, Год журнала: 2023, Номер 11(8), С. 2323 - 2323

Опубликована: Авг. 2, 2023

Feature data refer to direct measurements of specific features, while feature residuals represent the deviations between these and their corresponding benchmark values. Both types information offer unique insights into system’s behavior. However, conventional diagnostic systems often struggle effectively integrate utilize both concurrently. To address this limitation improve performance, a hybrid method based on Bayesian network (BN) is proposed. This enables parallel fusion within unified model, comprehensive framework for developing also given. In BN, symptom layer consists residual nodes representing measured data. By applying proposed two chillers comparing it with state-of-the-art existing methods, we demonstrate its effectiveness superiority. The results highlight that not only accommodates absence either type but leverages them enhance performance. Compared using single node, achieves maximum improvement 24.5% in accuracy, significant enhancements F-measure observed refrigerant leakage fault (34.5%) excessive lubricant (32.8%), respectively.

Язык: Английский

In situ model fusion for building digital twinning DOI
Sungmin Yoon, Jabeom Koo

Building and Environment, Год журнала: 2023, Номер 243, С. 110652 - 110652

Опубликована: Июль 21, 2023

Язык: Английский

Процитировано

23

A novel in-situ sensor calibration method for building thermal systems based on virtual samples and autoencoder DOI
Zhe Sun, Qiwei Yao,

Huaqiang Jin

и другие.

Energy, Год журнала: 2024, Номер 297, С. 131314 - 131314

Опубликована: Апрель 15, 2024

Язык: Английский

Процитировано

9

An enhanced feature extraction based long short-term memory neural network for wind power forecasting via considering the missing data reconstruction DOI Creative Commons
Zheng Xin,

Xingran Liu,

Hanyuan Zhang

и другие.

Energy Reports, Год журнала: 2023, Номер 11, С. 97 - 114

Опубликована: Ноя. 27, 2023

Wind power forecasting plays a significant role in regulating the peak and frequency of system, which can improve wind receiving capacity. Despite plenty methods have been proposed to fortify accuracy forecasting, existing models do not consider reconstruction missing data extract spatiotemporal features from data. To address these issues, this study proposes an improved long short-term memory (LSTM) network based method reconstruct capture In order model, multiple imputation technique (MIT) is first developed fill up samples with reconstructed by analyzing correlation among variables raw Secondly, exploit spatial temporal reduce low computation complexity, new parallel convolutional involving dilated convolution causal established for extraction. Finally, further performance, LSTM applied long-term trends reveal internal relations derived features. The experimental results on benchmark dataset both demonstrate that obtain better performance.

Язык: Английский

Процитировано

17

Diagnosis of single and multiple-source faults of chiller sensors using EWEEMD-ICKNN by time sequence denoising and non-Gaussian distribution feature extraction DOI
Boyan Zhang, Peng Wang, Guangyu Liu

и другие.

Energy and Buildings, Год журнала: 2023, Номер 298, С. 113572 - 113572

Опубликована: Сен. 19, 2023

Язык: Английский

Процитировано

15

Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy DOI
Guannan Li, Zixi Wang, Jiajia Gao

и другие.

Applied Thermal Engineering, Год журнала: 2024, Номер 241, С. 122357 - 122357

Опубликована: Янв. 5, 2024

Язык: Английский

Процитировано

6

Fault detection, diagnosis and calibration of heating, ventilation and air conditioning sensors by combining principal component analysis and improved bayesian inference DOI
Guannan Li, Chenglong Xiong, Jiajia Gao

и другие.

Journal of Building Engineering, Год журнала: 2023, Номер 82, С. 108230 - 108230

Опубликована: Дек. 4, 2023

Язык: Английский

Процитировано

12

Analysis of sensor offset characteristics in building energy systems based on redundant sensors: A case study on variable air volume system DOI

Jiteng Li,

Peng Wang, Yu Li

и другие.

Energy and Buildings, Год журнала: 2024, Номер 306, С. 113957 - 113957

Опубликована: Янв. 28, 2024

Язык: Английский

Процитировано

5

A deep learning-based Bayesian framework for high-resolution calibration of building energy models DOI
Gang Yi Jiang, Yixing Chen, Zhe Wang

и другие.

Energy and Buildings, Год журнала: 2024, Номер 323, С. 114755 - 114755

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

5

Self-correction method for sensor faulty heat pump system based on machine learning DOI Creative Commons
Zhe Sun, Qiwei Yao

Results in Engineering, Год журнала: 2024, Номер 22, С. 102170 - 102170

Опубликована: Апрель 24, 2024

Sensor faults are a common type of failure in heat pump systems, which can seriously affect the normal operation systems. Self-correction sensor fault system is crucial. State-of-the-art correction methods based on data-driven and physical models face challenges, such as need for co-located sensors, accurate models, large amount labeled data, greatly limiting their applicability. This paper proposes using machine learning self-correction. Firstly, data self-correction strategy convolutional autoencoder introduced. Furthermore, an artificial sample generation proposed to address scarcity training model. The results demonstrate that method effectively self-corrects both single multiple faults. Simultaneously, thermal diagnosis evaluations reveal over 90% accuracy corrected with maximum diagnostic improvement 53.5%. study shows number parameters crucial effective correction, underscoring over-constraint essential successful

Язык: Английский

Процитировано

4

Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES–CFD Co-Simulation DOI Creative Commons
Luhan Wang, Guannan Li, Jiajia Gao

и другие.

Sensors, Год журнала: 2023, Номер 23(13), С. 5958 - 5958

Опубликована: Июнь 27, 2023

Due to energy constraints and people’s increasing requirements for indoor thermal comfort, improving efficiency while ensuring comfort has become the focus of research in design operation HVAC systems. This study took office rooms with few people occupying them Wuhan as object. The EnergyPlus-Fluent co-simulation method was used impact 12 forms air distribution on environment air-conditioner consumption. results indicate that 3 m/s supply velocity 45° angle are more suitable case model this study. paper provides a reference environments offices them.

Язык: Английский

Процитировано

9