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A prognostic framework for rotating machines considering multi-component fault scenarios

IEEE Access 2025年 13卷 91682-91692页

作者： Lycksam, Adam O'Nils, Mattias Qureshi, Faisal Z. Mid Sweden University Department of Computer and Electrical Engineering Sundsvall85170 Sweden SCA Sundsvall85188 Sweden University of Ontario Institute of Technology Faculty of Science OshawaONL1G 0C5 Canada

The importance of preventing failures in rotating machines has led to extensive research on diagnosis and prognosis methods based on vibration data. To achieve a high generalizability for these solutions, they need to handle high discrepancies between the data used for developing the method and the data from the target machine, a lack of historical events from the target machine and multi-component fault scenarios. Currently, state-of-the-art research has successfully found solutions targeting the first two challenges. However, these almost exclusively focus on single-component fault scenarios, meaning it is unclear how a method should be constructed considering a multi-component system. Therefore, this study constructs a framework for multi-component fault scenarios called Rotating Machinery Prognostic Framework (RoMaP) that leverages the advancements in anomaly detection, fault diagnosis and RUL prediction based on vibration data. To evaluate the potential of RoMaP, an instance based on state-of-the-art research was implemented and compared to other methods based on alternative frameworks. The results show that the instance of RoMaP had the best performance across many scenarios expected in an industrial environment, suggesting that it is a suitable approach for monitoring the health of rotating machines. © 2013 IEEE.

关键词： Bearings (machine parts)

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Analysis of Challenges of IoT Adoption for Smart City Application 5th

Analysis of Challenges of IoT Adoption for Smart City Applic...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Das, Rashmi Prava Muduli, Debendra Luhach, Ashish Kumar Department of Computer Science and Engineering C V Raman Global University Bhubaneswar752054 India School of Electrical and Communication Engineering Papua New Guinea University of Technology Lae411 Papua New Guinea

ISBN: (纸本)9789819767311

The Internet of Things (IoT) is an architecture for a network that processes and analyzes sensitive data in order to deliver a variety of services to a large number of users. IoT devices are capable of delivering a wide variety of value-added services in a variety of smart city applications. Nevertheless, there have been a lot of worries voiced concerning data privacy, confidentiality, and security in relation to the IoT, as well as consumer resistance to adopting such innovative technology. This article examines difficulties linked to the expansion of the IoTs and their adoption by users in smart city applications using Step-wise Weight Assessment Ratio Analysis (SWARA) technique. A total of 30 factors that inhibit IoT adoption were analyzed and categorized under 5 major factors. It revealed that, functional and technological barriers offer more resistance to IoT adoption for smart city application in India. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： IoT Optimization Smart city SWARA

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Advanced classified turning traffic volume module at Unsignalized Intersection 9

Advanced classified turning traffic volume module at Unsigna...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kulkarni, Soham Deshpande, Varsha Kshirsagar Bhalerao, Raghavendra Dj Sanghavi College of Engineering Department of Information Technology Mumbai India Rmd Sinhgad College of Engineering Department of Electronics And Telecommunication Pune India Iitram Ahmedabad India Institute of Infrastructure Technology Research And Management Department of Electrical And Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350394474

Intersection study is one of the crucial surveys for advanced traffic management, especially at unsignalized points. In this study, the traditional method for vehicle detection and classification is initially implemented to classify traffic volume at unsignalized intersections. To enhance the accuracy of object detection and counting, a deep learning approach for vehicle classification and counting is designed using the YOLO (You Only Look Once) algorithm and a modified YOLO algorithm. This includes a variation of a statistical approach for the best outlier detection. The approach aims to improve the efficiency of vehicle classification and counting at unsignalized intersections, which is a critical task in intelligent transportation systems. The results verified for four classes of vehicles and indicate that the deep learning approach with an effective outlier detection approach increased the efficiency of vehicle classification and counting by 30 to 40 percent with satisfactory results. © 2024 IEEE.

关键词： Anomaly detection

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

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computers, materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion SDN Wi-Fi

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Modeling Flow Boiling Utilizing Machine Learning Vision Data 23

Modeling Flow Boiling Utilizing Machine Learning Vision Data

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23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： Huang, Cho-Ning Chang, Sanghyeon Suh, Youngjoon Won, Yoonjin Kharangate, Chirag R. Case Western Reserve University Department of Mechanical and Aerospace Engineering ClevelandOH United States University of California Department of Mechanical and Aerospace Engineering IrvineCA United States University of California Department of Electrical Engineering and Computer Science IrvineCA United States

ISBN: (纸本)9798350364330

Flow boiling is a very efficient configuration for meeting the high heat dissipation demands of thermal management systems. However, the lack of a clear understanding of physics affecting two-phase flow has limited its wide implementation across thermal systems. Recently, novel machine learning vision tools have been developed to capture physical feature information during subcooled flow boiling in a rectangular channel with single-sided heating. These features include local and averaged, as well as transient and steady-state statistical data on void fraction, vapor-liquid voids, interfacial behaviors, and liquidsolid wall wetting front areas. In this study, this data is used to model performance parameters in flow boiling. The statistical information relating to void fraction, bubble behaviors, interfacial waviness, and wetting fronts is analyzed and correlated with heat transfer coefficients, and critical heat flux. The data is used in combination with traditional control-volume-based theoretical modeling techniques to capture the relationship between the parameter of interest and the input parameters. The data on channel void fraction and wetting front areas are used to improve theoretical models predicting heat transfer coefficient. The data on interfacial behaviors and wetting front areas is to improve mechanistic model predicting critical heat flux. With this work, a new approach to utilizing machine vision data is proposed and validated. © 2024 IEEE.

关键词： Void fraction

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MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced Quality of Service 10

MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Sadia, Haleema Iqbal, Hafsa Ahmed, Ridha Alaa Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi Pakistan National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350329674

Non-orthogonal multiple access (NOMA) scheme has proved a potential candidate to meet the design challenges of orthogonal multiple access (OMA) schemes. High spectrum efficiency and higher data rates are further achieved by integrating NOMA with multiple input multiple output (MIMO) technology. In this article, we have explored orthogonal space-time block coding (OSTBC) to improve the quality of service (QoS) for the proposed MIMO-NOMA framework. We specifically employed the Alamouti STBC scheme, which provides full diversity for the proposed network. At the transmitter side, OSTBC and power domain multiplexing with superposition coding are utilized, while at the reception side, a low complexity maximum likelihood (ML) detector is used, followed by successive interference cancellation (SIC). Based on bit error rate (BER) evaluations, Monte Carlo simulations are performed to analyze the performance of OSTBC MIMO-NOMA system under various fading channels. The proposed framework is further investigated with a higher modulation scheme and different channel code rates. The investigated Alamouti scheme provides a maximum diversity gain of 2M (M shows the number of receiving antennas) without any need for channel state information at the transmitter end and a less complex ML detector at the receiver end. This research lays the groundwork for designing adaptive modulation and coding methods for future MIMO-NOMA based B5G wireless communication systems. © 2023 IEEE.

关键词： Quality of service

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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A Dual-channel Converter with a Positive Output and a Negative Voltage Output 6

A Dual-channel Converter with a Positive Output and a Negati...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Yeu-Torng, Yau Thanh-Phu, Luu National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Department of Electrical Engineering Taichung Taiwan

ISBN: (纸本)9798350301953

In this paper, a dual-channel converter with a positive output and negative voltage output is proposed. It integrates a positive voltage output converter and a negative voltage output converter, and shares the same switches. The number of active components can be reduced. In addition, the circuit can achieve dual output voltage control with a single controller and PWM drive signal by appropriately designing the ratio of the number of windings of the coupling inductor. A regulated positive voltage output and negative voltage output can be achieved. © 2023 IEEE.

关键词： Power converters

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Texture Analysis of Lung Cell Surface Morphology After Nanoparticle Exposure

Texture Analysis of Lung Cell Surface Morphology After Nanop...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Mohan, Akshatha Jefferis, Taylor Sayes, Christie M. Peeples, Joshua Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Environmental Science Baylor University WacoTX United States

ISBN: (纸本)9781510686045

Nanoparticle exposure induces morphological changes to the human cell surface, necessitating robust methods for quantitative analysis. Scanning electron microscopy (SEM) provides high-resolution imaging of these surfaces, and the results help elucidate possible modes of toxic action. To objectively quantify these changes, we propose a novel texture analysis framework that leverages fractal dimension and lacunarity analysis, combined with a Quantized Co-Occurrence (QCO) operator and Earth Mover's Distance (EMD) to capture both local and global textural features directly from grayscale SEM images. The QCO operator enables the discretization of textural features into quantized levels, facilitating the generation of feature distributions that retain spatial information while summarizing surface variability. Using EMD, we assess the differences in the distributions between classes, providing a robust measure to quantify the structural and morphological differences between untreated cells and those exposed to various nanoparticles. This combined framework enables us to visualize and rank nanoparticle-induced effects in cell surface morphology, revealing key insights into the differential impact of particle sensitizers like nickel oxide (NiO) and irritants like crystalline silica (CS). Our results demonstrate the effectiveness of the proposed framework in highlighting distributional differences with rankings validated against expert knowledge using statistical measures such as Cohen's Kappa. This approach not only advances the objective quantification of cell surface texture changes but also establishes a scalable method for analyzing complex morphological features in biomedical imaging. © 2025 SPIE.

关键词： Fractal dimension

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