Machine learning-based fault diagnosis in engines using IoT based thermodynamic feature vectors

Dr. Kishore Kumar; Tadikonda Jyothilatha; Ankitha Sangani; Thota Priyadarshini

Author(s)

Dr. Kishore Kumar school of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, , Telangana., India

Tadikonda Jyothilatha School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, , Telangana., India

Ankitha Sangani School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, , Telangana., India

Thota Priyadarshini School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, , Telangana., India

Abstract

The integration of Machine Learning (ML) and Internet of Things (IoT) technologies has revolutionized engine fault diagnosis, leveraging real-time data from IoT sensors to enhance predictive maintenance practices. Therefore this project explores the development and implementation of an ML-based fault diagnosis system using thermodynamic feature vectors collected from IoT sensors. These feature vectors, including temperature, pressure, and flow rates, provide a comprehensive overview of engine conditions, enabling the detection and prediction of faults with high accuracy. Traditional diagnostic systems, reliant on manual inspections, rulebased methods, and Diagnostic Trouble Codes (DTC), often fail to handle the complexity and volume of data generated by modern engines. These methods are not only time-consuming and prone to human error but also limited in their ability to provide proactive maintenance solutions. This project addresses these limitations by employing advanced ML algorithms, such as neural networks, decision trees, and support vector machines, to analyze and interpret the high-dimensional data from IoT sensors. The proposed system is designed to provide real-time monitoring and predictive maintenance, significantly reducing downtime and maintenance costs while enhancing engine performance and safety. The ML models are trained on a diverse dataset of thermodynamic features, enabling them to detect subtle anomalies and predict potential faults before they escalate into major issues. Through rigorous testing and validation, the system demonstrated superior diagnostic accuracy and adaptability to varying operational conditions. This project underscores the potential of ML and IoT in transforming engine fault diagnosis, offering a scalable, efficient, and reliable solution for modern industrial and automotive applications. The findings contribute to the advancement of predictive maintenance strategies, fostering greater operational reliability and safety in engine management systems.

Publication Details

Page(s) 784-790

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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Keywords

Internet of Things, Engine Fault Diagnosis, Thermodynamic Feature Vectors

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