Study of Partially Labeled Learning Approaches Applied to Electrical Equipment Analysis

Registration ID: IJNRD_313305

Published ID: IJNRD2604137

: https://doi.org/10.56975/ijnrd.v11i4.313305

About Hard Copy and Transparent Peer Review Report

Dr.K.Rajashekar, VANKUDOTHU GEETHA, DASARI VARSHITH, SOMASANI NAGARAJU

Semi-Supervised Learning, Electrical Machine Monitoring, Fault Diagnosis, Predictive Maintenance, Industrial Data Analytics.

The fast growth of industrial automation and the growing complexity of electrical machines have made it very important to have smart systems for monitoring, diagnosing problems, and predicting when maintenance will be needed. People have used traditional supervised machine learning methods a lot to look at operational data from electrical machines. This makes it possible to find faults and improve performance. But these methods need a lot of labelled data, which can be expensive, take a long time, and be hard to get in real-world industrial settings. On the other hand, a lot of operational data is still not labelled, not used enough, and could be useful for making strong predictive models. Semi-Supervised Machine Learning (SSML) solves this problem by using the best parts of both labelled and unlabelled data to make models more accurate, flexible, and able to apply to new situations. This study examines the incorporation of SSML into the oversight and upkeep of electrical machinery, with the objective of improving operational reliability, minimising unanticipated downtime, and optimising maintenance timetables. We look at important SSML algorithms, such as self-training, co-training, generative models, and graph-based methods. Self-training uses predictions that are very likely to be correct on data that isn't labelled to make the model better over time. Co-training, on the other hand, uses multiple complementary views of data to help the model learn from datasets that are only partially labelled. Generative models, like variational autoencoders and generative adversarial networks, make up plausible operational scenarios and add to training datasets. This helps with the lack of labelled fault data. Graph-based methods use the connections and similarities between machine data points to spread labels quickly and find complex operational dependencies. The research examines the practical implementation of these SSML methodologies for fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Through in-depth analysis, it shows how SSML can find small problems, spot possible failures before they get worse, and guess how much longer important parts will last. A comparative evaluation of the methods elucidates their distinct advantages and disadvantages, alongside their appropriateness for various operational scenarios. For example, self-training works well when there aren't many labelled examples but they are reliable. On the other hand, graph-based techniques work best when there are a lot of correlations between sensors. The research also looks at important problems that come up when using SSML in factories, like dealing with noisy and unbalanced data, choosing the best algorithm for a given machine condition, and combining real-time data streams with predictive models. There are ways to deal with these problems, such as data preprocessing techniques, confidence-based label propagation, and incremental model retraining. The study also stresses the need for scalable architectures and real-time processing capabilities to make sure that predictive insights are delivered quickly, which allows for proactive maintenance actions. The research not only looks at the methods used, but also gives a detailed framework that brings together sensor data collection, data preprocessing, SSML-based predictive modelling, and dashboard-based monitoring. This framework gives engineers and operators useful information about the health of machines, which helps them plan operations better, lower maintenance costs, and make machines last longer. The results show that SSML could be a good way to connect traditional supervised learning methods with real-world industrial needs. It is a cost-effective and efficient way to use both labelled and unlabelled data. Finally, the study talks about how SSML can be used in more general ways in industry. Companies can go from reactive maintenance to predictive and preventive strategies by combining advanced machine learning techniques with industrial operational workflows. This cuts down on downtime, makes things safer, and boosts overall productivity. The study finds that SSML is not only a useful tool for monitoring electrical machines, but it is also the basis for the future of smart, data-driven industrial systems. There are also chances for more progress in edge computing, real-time analytics, and the integration of explainable AI.

Published Paper   E-Certificate

Dr.K.Rajashekar, VANKUDOTHU GEETHA, DASARI VARSHITH, & SOMASANI NAGARAJU (April-2026). Study of Partially Labeled Learning Approaches Applied to Electrical Equipment Analysis. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), b274-b280. https://doi.org/10.56975/ijnrd.v11i4.313305

Volume 11 Issue 4, April-2026

Pages : b274-b280

Paper Reg. ID: IJNRD_313305

Published Paper Id: IJNRD2604137

Research Area: Other area not in list

Author Type: Indian Author

Country: warangal, telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604137.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604137

Crossref DOI: https://doi.org/10.56975/ijnrd.v11i4.313305

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

UGC CARE JOURNAL PUBLICATION | ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International UGC CARE JOURNAL PUBLICATION, Low Cost, Scholarly Open Access, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

© 2026 - Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License. and The Open Definition. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). 🛡️ Disclaimer: The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.

Call For Paper - Volume 11 | Issue 4 | April 2026

IJNRD is a Scholarly Open Access, Peer-Reviewed, Refereed, and UGC CARE Journal Publication with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost, and Transparent Peer Review Journal Publication that adheres to the New UGC CARE Transparent Peer-Reviewed Journal Policy and aligns with Scopus Journal Publication standards to ensure the highest level of research quality and credibility.

IJNRD offers comprehensive Journal Publication Services including indexing in all major databases and metadata repositories, Digital Object Identifier (Crossref DOI) assignment for each published article with additional fees, citation generation tools, and full Open Access visibility to enhance global research reach and citation impact.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse academic and professional fields. The journal promotes global knowledge exchange among researchers, developers, academicians, engineers, and practitioners, serving as a trusted platform for innovative, peer-reviewed journal publication and scientific collaboration.

Indexing Coverage: Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many other recognized academic repositories.

Transparent Peer Review Journal Publication: IJNRD operates a strict double-blind peer review system managed by 3000+ expert reviewers, ensuring ethical, unbiased, and high-quality review for every research paper.

For Indian Authors : Get a transparent peer review report from Scholar9.com for just ₹1000. View Sample Report

For Foreign Authors : A detailed peer review report is available through Scholar9.com for $20 USD. View Sample Report

Transparent Peer Review Journal Publication

⭐ Transparent Peer Review | 🕵️‍♂️ Double-Blind | 👨‍🏫 3000+ Expert Reviewers | 🇮🇳 Report for India Author ₹1000 | 🌐 Report for Foreign Author $20 | 📄 Sample Reports on Scholar9.com | 🌍 High Credibility | ⚖️ Ethical & Unbiased Evaluation

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Recently, the UGC discontinued the UGC-CARE Journal List and introduced new parameters that allow publication in Transparent Peer-Reviewed (Refereed) Journals. IJNRD is Transparent Peer Review Journal Valid As per New UGC Notification.

You can now publish your research paper in IJNRD.ORG. IJNRD is a Transparent Peer-Reviewed Open Access (Refereed Journal), UGC and UGC CARE Approved, Crossref DOI, Multidisciplinary, Impact Factor calculate by Google Scholar. As an International, open-access, and online journal, Publishing with us ensures wider reach, academic credibility, and enhanced recognition for your work.

For more details, refer to the official notice: UGC Public Notice

⭐ Low Cost ₹1570 | 📚 UGC CARE Approved | 🔍 Peer-Reviewed | 🌐 Open Access | 🔗 Crossref DOI & Global Indexing | 📊 Google Scholar Impact Factor | 🧪 Multidisciplinary

Submit Paper Online  Call for Paper  About IJNRD UGC CARE Approval

Important Dates for Current issue

Paper Submission Open For: April 2026

Current Issue: Volume 11 | Issue 4 | April 2026

Impact Factor: 8.76

Last Date for Paper Submission: Till 30-Apr-2026

Notification of Review Result: Transparent peer review process - your paper is evaluated by experts, and you receive acceptance or rejection updates via email and SMS.

Publication of Paper: Once all documents are submitted, your paper is published without delay, and you can instantly download your certificate and confirmation letter online.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an international open-access journal offering Low Cost Journal Publication, transparent Peer Review Journal Publication, Crossref DOI, and multidisciplinary research visibility under UGC CARE Approved Journal Publication.

Subject Category: Research Area

Approval, Licenses and Indexing: More Details

Call For Paper - Volume 11 | Issue 4 | April 2026

IJNRD.org offers low-cost journal publication starting at ₹1570 with UGC CARE Approved, refereed, peer-reviewed, open-access publishing. This multidisciplinary monthly journal, available in both online and print formats, features a strong Google Scholar-based impact factor of 8.76, Transparent Peer Review, CrossRef DOI, global indexing, fast publication, and complete metadata for maximum research visibility and citation impact across multidisciplinary domains.

Volume 11 | Issue 4 | April 2026 | IJNRD Transparent Peer Review Certificate | Submit Paper Online till 30-Apr-2026

⭐ UGC CARE Approved Refereed Journal | 🔍 Transparent Peer Review | 🌐 Open Access Publishing | 💰 Low-Cost ₹1570 | 🔗 CrossRef DOI & Global Indexing | 📊 Google Scholar Impact Factor 8.76 | 🧪 Multidisciplinary | Online & Print

Submit Paper Online