Predictive maintenance using sensor data and machine learning
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives and Scope<br>2. Predictive Maintenance Fundamentals<br> 2.1 Importance of Predictive Maintenance<br> 2.2 Predictive Maintenance Approaches<br>3. Sensor Data Acquisition and Preprocessing<br> 3.1 Sensor Data Sources and Types<br> 3.2 Data Cleaning and Feature Engineering<br>4. Machine Learning Models for Predictive Maintenance<br> 4.1 Anomaly Detection and Failure Prediction<br> 4.2 Prognostics and Remaining Useful Life (RUL) Estimation<br>5. Model Training and Validation<br> 5.1 Training Data Selection and Splitting<br> 5.2 Model Performance Evaluation<br></p>
Thesis Abstract
<p> Predictive maintenance has gained significant attention in industrial settings for minimizing downtime and optimizing equipment reliability. This project aims to develop a predictive maintenance system using sensor data and machine learning algorithms. The system will leverage historical sensor data to predict equipment failures and maintenance needs, enabling proactive maintenance interventions. By implementing predictive maintenance, organizations can reduce operational costs and enhance overall equipment effectiveness. <br></p>Thesis Overview
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