Article: Visual exploration of fault detection using machine learning and image processing Journal: International Journal of Engineering Systems Modelling and Simulation (IJESMS) 2023 Vol.14 No.1 pp.8 - 15 Abstract: The machine learning CNN method defect detection is highly reliant on the training data; thus, post-classification regularisation may significantly improve the output. The suggested fault detection process may perform well on demanding synthetic and actual information by using a practical synthetic fault system depending on the SEAM model. We further propose the visual exploration be made more reliable regarding fault tolerance. The visual exploration model is made up of three-phase namely, visual identification and mapping, dynamic controller, and terminate criterion. The submap-dependent on visual mapping phase ensures higher mapping manageability, semantic classification dependent on active controller ensures continuous driving, and a new completion assessment technique ensures robust re-localisation under the terminate criterion. To preserve mapping and improve visual tracking, all the components are tightly linked. The proposed model machine learning CNN model is examined, and actual tests show fault-tolerance methods are proven to withstand visual monitoring and mapping failure situations. Inderscience Publishers - linking academia, business and industry through research

You can view the full text of this article for free using the link below.

Title: Visual exploration of fault detection using machine learning and image processing

Authors: D. Vijendra Babu; K. Jyothi; Divyendu Kumar Mishra; Atul Kumar Dwivedi; E. Fantin Irudaya Raj; Shilpa Laddha

Addresses: Department of Electronics and Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, Paiyanoor, 603-104, Tamil Nadu, India ' Department of ECE, College of Engineering Trikaripur, India ' Department of Computer Science and Engineering, Faculty of Engineering, VBS Purvanchal University Jaunpur, Uttar Pradesh, India ' Department of ECE, Bundelkhand Institute of Engineering and Technology, Jhansi, India ' Department of Electrical and Electronics Engineering, Dr. Sivanthi Aditanar College of Engineering, Tamil Nādu, India ' Department of Information Technology, Government College of Engineering, Aurangabad, Maharashtra, India

Abstract: The machine learning CNN method defect detection is highly reliant on the training data; thus, post-classification regularisation may significantly improve the output. The suggested fault detection process may perform well on demanding synthetic and actual information by using a practical synthetic fault system depending on the SEAM model. We further propose the visual exploration be made more reliable regarding fault tolerance. The visual exploration model is made up of three-phase namely, visual identification and mapping, dynamic controller, and terminate criterion. The submap-dependent on visual mapping phase ensures higher mapping manageability, semantic classification dependent on active controller ensures continuous driving, and a new completion assessment technique ensures robust re-localisation under the terminate criterion. To preserve mapping and improve visual tracking, all the components are tightly linked. The proposed model machine learning CNN model is examined, and actual tests show fault-tolerance methods are proven to withstand visual monitoring and mapping failure situations.

Keywords: visual exploration; fault detection; convolutional neural networks; CNNs; image processing.

DOI: 10.1504/IJESMS.2023.127394

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.1, pp.8 - 15

Received: 30 Jul 2021
Accepted: 02 Sep 2021
Published online: 03 Dec 2022 *

Full-text access for editors Full-text access for subscribers Free access Comment on this article

Title: Visual exploration of fault detection using machine learning and image processing

Keep up-to-date