Data-Driven Predictive Maintenance for Industrial Equipment
Table Of Contents
<p>1. Introduction<br> 1.1 Importance of Predictive Maintenance in Industrial Settings<br> 1.2 Objectives of the Project<br>2. Sensor Data Collection and Preprocessing<br> 2.1 Types of Sensors and Data Acquisition<br> 2.2 Data Cleaning and Preprocessing Techniques<br>3. Feature Engineering for Predictive Maintenance<br> 3.1 Time-Series Analysis of Sensor Data<br> 3.2 Feature Selection and Extraction<br>4. Machine Learning Models for Fault Detection<br> 4.1 Anomaly Detection Algorithms<br> 4.2 Classification Models for Fault Diagnosis<br>5. Remaining Useful Life Prediction<br> 5.1 Regression Models for RUL Estimation<br> 5.2 Prognostics and Health Management (PHM) Techniques<br></p>
Thesis Abstract
<p> Predictive maintenance has emerged as a critical strategy for minimizing downtime and optimizing the performance of industrial equipment. This project aims to develop a data-driven predictive maintenance framework for industrial machinery using machine learning and sensor data analysis. The project will focus on the collection and preprocessing of sensor data, feature engineering, and the application of machine learning algorithms for fault detection and remaining useful life prediction. Additionally, the project will evaluate the economic and operational benefits of implementing predictive maintenance in industrial settings. <br></p>Thesis Overview
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