Proceedings of the 13th International Youth Conference in the series of “Youth for India @2047, AI Disruption and Opportunities: Preparing Youth for Global Challenges (IYC 2026)

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An integrated Prognostics Driven framework for Defect, and Health analysis in Industrial Motors

Authors
Paras Kumar Koayrh1, Praveen Saraswat1, 3, Ravi Kant2, Vaibhav Sharma1, Rajeev Agrawal1, *
1Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, India
2National Engineering Industries Limited, Jaipur, India
3Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India
*Corresponding author. Email: ragrawal.mech@mnit.ac.in
Corresponding Author
Rajeev Agrawal
Available Online 15 May 2026.
DOI
10.2991/978-94-6239-676-0_9How to use a DOI?
Keywords
Prognostics and Health Management (PHM); Machine learning; Predictive Maintenance (PdM); Fault Detection; Induction Motor
Abstract

The fourth industrial revolution in today’s world has changed advanced manufacturing. Its fundamental process such as machining, welding, and additive manufacturing (AM) are altered by synthesis of artificial intelligence (AI) and machine learning (ML). The quality control (QC) for product, focusing on part-level defect detection, and “Predictive maintenance” (PdM), focusing on asset-level health of machines are often treated as distinct functional challenges. With the help of integration framework between predictive maintenance and industry 4.0 technology, Industries are benefited with improving the operational efficiency, shorter production time and optimized resources.

In this research study, A predictive maintenance framework has been developed for a coolant supply motor during its useful life to accurately detect early defects and predict remaining useful life to prevent unscheduled downtime and condition-based maintenance. The machine learning model employs a dual modal approach using a support vector machine (SVM) for boundary-based classification and a random forest (RF) for ensemble-based fault detection. It can be concluded that study will be beneficial to the industry professionals to provides a holistic solution for anomaly detection and remaining useful life estimation.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 13th International Youth Conference in the series of “Youth for India @2047, AI Disruption and Opportunities: Preparing Youth for Global Challenges (IYC 2026)
Series
Advances in Intelligent Systems Research
Publication Date
15 May 2026
ISBN
978-94-6239-676-0
ISSN
1951-6851
DOI
10.2991/978-94-6239-676-0_9How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Paras Kumar Koayrh
AU  - Praveen Saraswat
AU  - Ravi Kant
AU  - Vaibhav Sharma
AU  - Rajeev Agrawal
PY  - 2026
DA  - 2026/05/15
TI  - An integrated Prognostics Driven framework for Defect, and Health analysis in Industrial Motors
BT  - Proceedings of the 13th International Youth Conference in the series of “Youth for India @2047, AI Disruption and Opportunities: Preparing Youth for Global Challenges (IYC 2026)
PB  - Atlantis Press
SP  - 136
EP  - 146
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-676-0_9
DO  - 10.2991/978-94-6239-676-0_9
ID  - Koayrh2026
ER  -