Predicting Turbocharger Failures in Mining Haul Trucks
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Authors
Kalyoncuoglu, Nuri
Date
2024-04-30
Type
thesis
Language
eng
Keyword
Mining Haul Trucks , Turbochargers , Machine Learning , Predictive Maintenance , Anomaly Detection
Alternative Title
Abstract
This thesis presents an innovative approach for predicting turbocharger failures in mining haul trucks, leveraging the power of machine learning algorithms to improve predictive maintenance strategies within the mining industry. The haul trucks have a critical role in mining operations, and a turbocharger failure within the haul truck can cause significant costs. This research aims to minimize downtime, improve safety, and enhance operational efficiency.
By incorporating machine learning models, this thesis introduces a novel predictive maintenance framework to monitor equipment conditions in real-time and identifies patterns that precede failures by analyzing sensor data from turbochargers, enabling preemptive maintenance actions. The study thoroughly examines turbocharger technology, common failure causes, and the role of various components. It also provides a detailed review of machine learning techniques applied to predictive maintenance such as ensemble learning, support vector machines, neural networks, and advanced anomaly detection methods.
Two case studies form the core of the analysis, demonstrating how supervised and unsupervised learning models are applied for predicting failures and detecting anomalies. These models make use of sensor data to forecast the behavior of turbocharger systems, detecting both anomalies and potential failures. This approach allows for timely maintenance decisions, reducing unnecessary maintenance operations and preventing catastrophic failures.
The thesis makes several contributions to the field, such as the use of machine learning models for predicting turbocharger failures, the development of new metrics for monitoring turbocharger health, and the identification of key sensors that affect turbocharger performance.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International