Article
Energy & Fuels
Syed Ali Ammar Taqvi, Haslinda Zabiri, Fahim Uddin, Muhammad Naqvi, Lemma Dendena Tufa, Majida Kazmi, Saddaf Rubab, Salman Raza Naqvi, Abdulhalim Shah Maulud
Summary: This study presents a multiple kernel support vector machine (MK-SVM) algorithm for diagnosing simultaneous faults in a distillation column, achieving a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK-SVM-based classification demonstrates a high F1 score of >97% for all combinations of faults, showing superior fault diagnosis performance compared to other machine-learning algorithms for single, multiple, and simultaneous faults.
ENERGY SCIENCE & ENGINEERING
(2022)
Article
Thermodynamics
Zhengxiong Ren, Hua Han, Xiaoyu Cui, Hailong Lu, Mingwen Luo
Summary: Data-driven fault detection and diagnosis (FDD) for chillers relies on a large quantity of labelled data, but under data-scarce scenarios, a novel data pulling (DP) strategy is proposed to address the issue. This strategy can extract high-confidence level data and improve the accuracy of fault detection.
Article
Computer Science, Information Systems
Kenichi Yatsugi, Shrinathan Esakimuthu Pandarakone, Yukio Mizuno, Hisahide Nakamura
Summary: Induction motors are crucial components in many industries, requiring proper maintenance and fault detection to prevent serious damage and industry shutdown. Bearing faults, broken rotor bar faults, and short-circuit insulation faults are common in induction motors, and their early detection and classification have received significant attention. However, there are limited studies on the detection and classification of these faults in the initial stage using common diagnosis methods.
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Shih-Lin Lin
Summary: This study applied the medium Gaussian support vector machine method to machine learning, improving the reliability and accuracy of motor bearing fault estimation, detection, and identification. Additionally, the classification results of motor datasets using different machine learning algorithms were summarized and analyzed.
Article
Construction & Building Technology
K. F. Fong, C. K. Lee, M. K. H. Leung, Y. J. Sun, Guangya Zhu, Seung Hyo Baek, X. J. Luo, Tim Ka Kui Lo, Hetty Sin Ying Leung
Summary: A hybrid multiple sensor fault detection, diagnosis, and reconstruction (HMSFDDR) algorithm was developed for chiller plants to address the characteristics of secondary sensors not involved in system control. Machine learning and pattern recognition were utilized to predict primary sensor faults based on weekly performance curves. By reconstructing primary sensor signals, secondary sensor faults were estimated using mass and energy balance. The algorithm achieved a maximum effectiveness of 75% when applied to various logged plant data and compared with on-site checking results. Off-site sensor testing further validated the usefulness of the proposed HMSFDDR algorithm.
JOURNAL OF BUILDING PERFORMANCE SIMULATION
(2023)
Article
Engineering, Electrical & Electronic
Bing Sun, Xiaofeng Liu
Summary: Wheelset bearing is a critical component in high-speed trains for safe and efficient operation. However, the Support Vector Machine (SVM) method for bearing health monitoring can lead to overfitting when outliers are present in the training dataset. In order to address this issue, an improved Significance SVM (SSVM) is proposed that assigns significant coefficients to samples in the model training process, giving less attention to outlier samples. The experiments on HST bearing vibration dataset demonstrate the effectiveness and stability of the proposed method under different noise levels.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Hongru Zhang, Jiaxiang Sun, Kaining Hou, Qingquan Li, Hongshun Liu
Summary: An IVSVM model combining improved information entropy and vague support vector machine is introduced for transformer fault diagnosis, improving accuracy by weighting training data and optimizing SVM sub-interface with vague set.
Article
Engineering, Multidisciplinary
Jianqun Zhang, Qing Zhang, Xianrong Qin, Yuantao Sun
Summary: This paper proposes a intelligent fault diagnosis methodology for rotating machinery by combining optimized support vector data description and optimized support vector machine. It explores different entropy-based indicators for feature extraction to improve the accuracy of fault detection and fault identification, which is beneficial to practical application.
Article
Chemistry, Analytical
Min-Chan Kim, Jong-Hyun Lee, Dong-Hun Wang, In-Soo Lee
Summary: In this study, an induction motor simulator was constructed to collect vibration datasets for three states. Support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models were used for fault diagnosis. Experimental results demonstrate the suitability of this technique for diagnosing faults in induction motors.
Article
Engineering, Multidisciplinary
Abderrazak Arabi, Mouloud Ayad, Nacerdine Bourouba, Mourad Benziane, Issam Griche, Sherif S. M. Ghoneim, Enas Ali, Mahmoud Elsisi, Ramy N. R. Ghaly
Summary: This paper presents an accurate approach for detecting and classifying parametric or soft faults in analog integrated circuits using machine learning algorithms. Features extracted from the real and imaginary frequency responses of output voltage and supply current are used to train machine learning classifiers, with the quadratic discriminant classifier achieving the highest average accuracy. The proposed approach has shown higher classification accuracy compared to other research works.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Energy & Fuels
Hongwen Dou, Radu Zmeureanu
Summary: This paper presents the development and use of benchmarking grey-box models for the detection and diagnosis of multiple-dependent faults (MDFDD) of a water-cooled centrifugal chiller. The models are developed using data recorded by a Building Automation System (BAS) from a central cooling plant of an institutional building. The paper introduces a forward residual-based fault detection model and a rule-based backward approach for fault diagnosis. The proposed MDFDD model is tested with artificial faults inserted into the measurement data file, and the results showcase its potential for application.
Article
Nuclear Science & Technology
Wenzhe Yin, Hong Xia, Xueying Huang, Jiyu Zhang, Miyombo Ernest Miyombo
Summary: This study proposes a fault diagnosis method based on adaptive feature extraction and multiple support vector machines to overcome the limitations of traditional intelligent fault diagnostics in nuclear power plants. The method adaptively extracts fault features using a deep residual neural network and identifies the fault using multiple support vector machines, demonstrating better diagnostic performance.
PROGRESS IN NUCLEAR ENERGY
(2023)
Article
Telecommunications
Biswa Ranjan Senapati, Pabitra Mohan Khilar, Rakesh Ranjan Swain
Summary: Vehicular Ad-hoc NETwork (VANET) is a rapidly growing research area with diverse applications, and the failure of the vehicle communication unit can lead to the loss of multiple applications. This paper proposes a composite fault diagnosis methodology for detecting and classifying OBU faults in VANET, utilizing statistical methods for detecting and categorizing both hard and soft faults.
VEHICULAR COMMUNICATIONS
(2021)
Article
Thermodynamics
Donghyuk Kim, Sukkyung Kang, Jaisuk Yoo, Dong-Kwon Kim, Baek Youn
Summary: The study developed quantitative fault detection models for air conditioner defects based on refrigeration cycle simulation data, which can predict the values of defective parameters and improve air conditioner efficiency.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2021)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)