Change search
Refine search result
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Arora, Siddhant
    et al.
    School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai Campus, Vandalur Kelambakkam Road, Chennai 600127, India.
    Venkatesh, Sridharan Naveen
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sugumaran, Vaithiyanathan
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai Campus, Vandalur Kelambakkam Road, Chennai 600127, India.
    Prabhakaranpillai Sreelatha, Anoop
    Sustainable Mobility Automobile Research Technology (SMART) Center, Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India.
    Mahamuni, Vetri Selvi
    Department of Project Management, Mettu University, P.O. Box: 318, Metu, Ethiopia.
    Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals2024In: Journal of Engineering, ISSN 2314-4904, E-ISSN 2314-4912, article id 1321775Article in journal (Refereed)
    Abstract [en]

    Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

    Download full text (pdf)
    fulltext
  • 2.
    Karthikeyan, H. Leela
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur, Kelambakkam Road, Chennai, 600127, India.
    Venkatesh, Naveen
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur, Kelambakkam Road, Chennai, 600127, India.
    Balaji, P. Arun
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur, Kelambakkam Road, Chennai, 600127, India.
    Vaithiyanathan, Sugumaran
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur, Kelambakkam Road, Chennai, 600127, India.
    Diagnosing Faults in Suspension System Using Machine Learning and Feature Fusion Strategy2024In: Arabian Journal for Science and Engineering, ISSN 2193-567XArticle in journal (Refereed)
    The full text will be freely available from 2026-03-31 09:01
  • 3.
    Nair, Shruti
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, India.
    Venkatesh, Naveen
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Chakrapani, Ganjikunta
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, India.
    Vaithiyanathan, Sugumaran
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, India.
    Automotive Clutch Fault Diagnosis Through Feature Fusion and Lazy Family of Classifiers2024In: Journal of Vibration Engineering & Technologies, ISSN 2523-3920Article in journal (Refereed)
  • 4.
    Parihar, Hrithik
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur Kelambakkam Road, Chennai-600127, India.
    Venkatesh, Naveen
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur Kelambakkam Road, Chennai-600127, India.
    Anoop, P S
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur Kelambakkam Road, Chennai-600127, India; Sustainable Mobility Automobile Research Technology (SMART) Center,Dept. of Electronics and Communication Engineering,Amrita Vishwa Vidyapeetham, Amritapuri, India.
    Sugumaran, V
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology Chennai Campus, Vandalur Kelambakkam Road, Chennai-600127, India.
    Application of feature fusion strategy for monitoring the condition of nitrogen filled tires using tree family of classifiers2024In: Physica Scripta, ISSN 0031-8949, E-ISSN 1402-4896, Vol. 99, no 3, article id 035210Article in journal (Refereed)
    The full text will be freely available from 2026-02-14 15:09
  • 5.
    Prasshanth, C.V.
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Venkatesh, Naveen
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sugumaran, V.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Aghaei, Mohammadreza
    Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway; Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg, Germany.
    Enhancing photovoltaic module fault diagnosis: Leveraging unmanned aerial vehicles and autoencoders in machine learning2024In: Sustainable Energy Technologies and Assessments, ISSN 2213-1388, E-ISSN 2213-1396, Vol. 64, article id 103674Article in journal (Refereed)
    Abstract [en]

    Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future.

    Download full text (pdf)
    fulltext
  • 6.
    Rattan, Avantika
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Venkatesh S, Naveen
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Sugumaran, V.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Anoop, P. S.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India; Sustainable Mobility Automobile Research Technology (SMART) Center, Dept. of Electronics and Communication Engineering, AmritaVishwa Vidyapeetham, Amritapuri, India.
    Monitoring the condition of nitrogen-filled tires using weightless neural networks2024In: Automatika: Journal for Control, Measurement, Electronics, Computing and Communications, ISSN 0005-1144, E-ISSN 1848-3380, Vol. 65, no 2, p. 523-537Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 7.
    Vasan, Vinod
    et al.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India.
    Venkatesh, Naveen
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India.
    Feroskhan, M.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India.
    Vaithiyanathan, Sugumaran
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India.
    Subramanian, Balaji
    Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
    Tsai, Pei-Chien
    Department of Medical and Applied Chemistry, Kaohsiung Medical University (KMU), Kaohsiung City 807, Taiwan; Department of Computational Biology, Institute of Bioinformatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 602105, India.
    Lin, Yuan-Chung
    Institute of Environmental Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan; Center for Emerging Contaminants Research, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
    Lay, Chyi-How
    Master's Program of Green Energy Science and Technology, Feng Chia University, Taichung City 407102, Taiwan.
    Wang, Chin-Tsan
    Department of Mechanical and Electro-Mechanical Engineering, National I-Lan University, I Lan, Taiwan.
    Ponnusamy, Vinoth Kumar
    Department of Medical and Applied Chemistry, Kaohsiung Medical University (KMU), Kaohsiung City 807, Taiwan; Center for Emerging Contaminants Research, National Sun Yat-sen University, Kaohsiung 804, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung City 807, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital (KMUH), Kaohsiung City 807, Taiwan; Department of Chemistry, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
    Biogas production and its utilization in internal combustion engines - A review2024In: Process Safety and Environmental Protection, ISSN 0957-5820, E-ISSN 1744-3598, Vol. 186, p. 518-539Article, review/survey (Refereed)
    The full text will be freely available from 2026-04-30 15:56
  • 8.
    Venkatesh, Naveen
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Sugumaran, V.
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Subramanian, Balaji
    Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India.
    Josephin, J.S. Femilda
    Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkiye; Department of Autotronics, Institute of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India.
    Varuvel, Edwin Geo
    Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkiye.
    A comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning features2024In: Sustainable Energy Technologies and Assessments, ISSN 2213-1388, E-ISSN 2213-1396, Vol. 64, article id 103713Article in journal (Refereed)
    Abstract [en]

    Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naïve Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.

    The full text will be freely available from 2027-03-02 17:59
  • 9.
    Venkatesh, Naveen
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Vaithiyanathan, Sugumaran
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Aghaei, Mohammadreza
    Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Ålesund 6009, Norway; Department of Sustainable Systems Engineering (INATECH), University of Freiburg, Freiburg 79110, Germany.
    Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features2024In: Energy Reports, E-ISSN 2352-4847, Vol. 11, p. 3889-3901Article in journal (Refereed)
    Abstract [en]

    This study proposes a novel approach utilizing a voting-based ensemble technique to diagnose visible faults in photovoltaic (PV) modules from aerial images captured by unmanned aerial vehicles (UAVs), leveraging AlexNet features. The proposed method focuses on classifying commonly occurring visual faults such as glass breakage, snail trails, burn marks, delamination and discoloration. Two voting-based ensemble models, a two-class ensemble (combining support vector machines and k-nearest neighbor) and a three-class ensemble (integrating support vector machines, J48, and k-nearest neighbor) were developed and evaluated against individual machine learning classifiers. Results indicate that the two-class ensemble outperforms the three-class ensemble and other individual classifiers, achieving an accuracy of 98.30%. This approach not only enhances fault diagnosis accuracy but also reduces inspection costs and instrument monitoring efforts contributing to the sustainable and efficient operation of PV systems.

    Download full text (pdf)
    fulltext
  • 10.
    Venkatesh S, Naveen
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sripada, Divya
    Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, India.
    V, Sugumaran
    School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India.
    Aghaei, Mohammadreza
    Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009, Ålesund, Norway; Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110, Freiburg, Germany.
    Detection of visual faults in photovoltaic modules using a stacking ensemble approach2024In: Heliyon, E-ISSN 2405-8440, Vol. 10, no 6, article id e27894Article in journal (Refereed)
    Abstract [en]

    Faults in photovoltaic (PV) modules may occur due to various environmental and physical factors. To prevent faults and minimize investment losses, fault diagnosis is crucial to ensure uninterrupted power production, extended operational lifespan, and a high level of safety in PV modules. Recent advancements in inspection techniques and instrumentation have significantly reduced the cost and time required for inspections. A novel stacking-based ensemble approach was performed in the present study for the accurate classification of PV module visible faults. The present study utilizes AlexNet (a pre-trained network) to extract image features from the aerial images of PV modules with the aid of MATLAB software. Furthermore, J48 algorithm was applied to perform the feature selection task to determine the most relevant features. The features derived as output from the J48 algorithm were passed onto train eight base classifiers namely, Naïve Bayes, logistic regression (LR), J48, random forest (RF), multilayer perceptron (MLP), logistic model tree (LMT), support vector machines (SVM) and k-nearest neighbors (kNN). The best performing five classifiers on the front run with higher classification accuracies were selected to formulate three categories of stacking ensemble groups as follows: (i) three-class ensemble (SVM, kNN, and LMT), (ii) four-class ensemble (SVM, kNN, LMT, and RF), and (iii) five-class ensemble (SVM, kNN, LMT, RF, and MLP). A comparison in the performance of the aforementioned stacked ensembles was evaluated with different meta classifiers. The obtained results infer that the four-class stacking ensemble model (SVM, kNN, LMT, and RF) with RF as the predictor achieved the highest possible classification accuracy of 99.04%.

    Download full text (pdf)
    fulltext
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf