Machine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing - Conference Paper

Aug 3, 2026

Manufacturing operations increasingly rely on machine learning to strengthen production performance, with condition monitoring and fault detection serving as a central use case. This paper reviews how ML methods are applied in manufacturing environments to support product quality, reduce downtime, and improve operational efficiency. It emphasizes the role of data preprocessing, feature engineering, and model selection for building dependable monitoring systems. The discussion covers case studies, deployment challenges, and future trends such as edge computing, digital twins, and advanced analytics for real-time predictive and prescriptive maintenance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Abstract

Data preprocessing and feature engineering

Model selection and reliability

Challenges and future trends

Brought to you by INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA Back Machine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing ICETAS 2024-9th IEEE International Conference on Engineering Technologies and Applied Sciences • Conference Paper • 2024 • DOI: 10.1109/ICETAS62372.2024.11120241 Mahmoud, Amena a ; Talpur, Kazim Raza b ; Shah, Asadullah c ; Saini, Shilpa d ; Juneja, Sapna e ; +1 author a Kafrelsheikh University, Faculty of Computers and Information, Kafrelsheikh, Egypt Show all information View PDF Full text Export Save to list Document Impact Cited by (0) References (19) Similar documents Abstract The manufacturing industry has witnessed a surge in the adoption of machine learning (ML) techniques to enhance various aspects of production processes. One critical application of ML in manufacturing is condition monitoring and fault detection, which play a pivotal role in ensuring product quality, minimizing downtime, and maximizing operational eﬃciency. This paper presents a comprehensive review of the use of machine learning for condition monitoring and fault detection in manufacturing environments. It also discusses the importance of data preprocessing, feature engineering, and model selection in developing robust and reliable ML-based condition monitoring systems. Furthermore, the paper addresses the case studies, challenges and future trends associated with deploying ML-driven condition monitoring, such as data quality, model interpretability, and integration with existing manufacturing systems. It also highlights emerging trends and future research directions in this domain, including the integration of edge computing, digital twins, and advanced analytics for real-time, predictive, and prescriptive maintenance strategies. © 2024 IEEE. Author keywords Condition Monitoring; Fault Detection; Machine Learning; Sensor-based Monitoring; Supervised Learning Indexed keywords Engineering controlled terms Engineering education; Learning systems; Maintenance; Predictive analytics; Supervised learning Engineering uncontrolled terms Condition; Critical applications; Faults detection; Machine learning techniques; Machinelearning; Manufacturing industries; Manufacturing IS; Production process; Products quality; Sensor-based monitoring Engineering main heading Condition monitoring © Copyright 2025 Elsevier B.V., All rights reserved. Abstract Author keywords Indexed keywords About Scopus What is Scopus Content coverage Scopus blog Scopus API Privacy matters Language ⽇本語版を表⽰する查看简体中文版本查看繁體中文版本 Просмотр версии на русском языке Customer Service Help Tutorials Contact us Terms and conditions Privacy policy Cookies settings All content on this site: Copyright © 2025 Elsevier B.V. , its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply. We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies .

FAQs

What future trends are discussed for deploying ML-driven monitoring in manufacturing?

The paper points to integration with edge computing, digital twins, and advanced analytics to enable real-time predictive and prescriptive maintenance strategies.

Aug 3, 2026

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