Predictive maintenance using sensor data and machine learning
Table Of Contents
- 1.Introduction
- 1.1Background and Motivation
- 1.2Objectives and Scope
- 2.Predictive Maintenance Fundamentals
- 2.1Importance of Predictive Maintenance
- 2.2Predictive Maintenance Approaches
- 3.Sensor Data Acquisition and Preprocessing
- 3.1Sensor Data Sources and Types
- 3.2Data Cleaning and Feature Engineering
- 4.Machine Learning Models for Predictive Maintenance
- 4.1Anomaly Detection and Failure Prediction
- 4.2Prognostics and Remaining Useful Life (RUL) Estimation
- 5.Model Training and Validation
- 5.1Training Data Selection and Splitting
- 5.2Model Performance Evaluation
Thesis Abstract
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.
Thesis Overview
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