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Title: :  FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING
PaperId: :  23112
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23112
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
DIVIYA KBANNARI AMMAN INSTITUTE OF TECHNOLOGY
PAVITHRA PBANNARI AMMAN INSTITUTE OF TECHNOLOGY
RITHANYA ABANNARI AMMAN INSTITUTE OF TECHNOLOGY
PARTHASARATHI PBANNARI AMMAN INSTITUTE OF TECHNOLOGY

Abstract

COMPUTER ENGINEERING
image processing, machine learning, deep learning, Inception V3 algorithm.
The work explores a state-of-the-art approach to detecting severe faults using machine learning. Using the power of pattern recognition in machine learning algorithms, we propose an automatic system for image tail string analysis. The system is trained on an extensive dataset carefully labeled with different fault classifications. This allows the model to detect and classify potential errors in unseen tow images during deployment. This method offers significant advantages over traditional techniques by providing an objective, automated and continuously learning solution to stern line inspection. This can change the way hard line integrity is evaluated in many industries. The method automates the inspection process by analyzing images of harsh lines to detect defects. Machine learning algorithms excel at pattern recognition, making them ideal for this task. The proposed method involves training a model on a dataset of stern line images classified by different fault types. Once the model is trained, it can analyze new images and effectively classify them, and detect potential errors in the towline. This data-driven approach has several advantages over traditional methods, including better accuracy, efficiency and the ability to continuously learn and improve over time. This approach could revolutionize return line control in many industries. Algorithm V3 is a deep Convolutional neural network architecture developed by Google. Due to the effective use of Convolutional filters and bootstrap modules, it achieves high performance in various image classification tasks. Seed modules stack multiple Convolutional layers with filters of different sizes in parallel, allowing the network to capture different features of the image. This hierarchical approach allows Inception V3 to learn complex representations of image data, resulting in better error detection accuracy in tail string analysis.

Citations

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IJARIIE DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. "FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2650-2657
MLA DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. "FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2650-2657.
APA DIVIYA K, PAVITHRA P, RITHANYA A, & PARTHASARATHI P. (2024). FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2650-2657.
Chicago DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. "FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2650-2657.
Oxford DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. 'FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2650-2657. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/FAULT_DETECTION_METHOD_FOR_TAIL_ROPE_USING_MACHINE_LEARNING_ijariie23112.pdf (Accessed : 09 August 2024).
Harvard DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. (2024) 'FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2650-2657IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/FAULT_DETECTION_METHOD_FOR_TAIL_ROPE_USING_MACHINE_LEARNING_ijariie23112.pdf (Accessed : 09 August 2024)
IEEE DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P, "FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2650-2657, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/FAULT_DETECTION_METHOD_FOR_TAIL_ROPE_USING_MACHINE_LEARNING_ijariie23112.pdf [Accessed : 09 August 2024].
Turabian DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. "FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (09 August 2024).
Vancouver DIVIYA K, PAVITHRA P, RITHANYA A, and PARTHASARATHI P. FAULT DETECTION METHOD FOR TAIL ROPE USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 09 August 2024]; 10(2) : 2650-2657. Available from: https://ijariie.com/AdminUploadPdf/FAULT_DETECTION_METHOD_FOR_TAIL_ROPE_USING_MACHINE_LEARNING_ijariie23112.pdf
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