Workshop Objectives:

• Implement data science techniques to predict equipment failures.
• Build machine learning models for predictive maintenance.
• Integrate IoT data for real-time equipment monitoring.
• Analyze manufacturing data to optimize maintenance schedules.
• Gain hands-on experience with real-world datasets in predictive maintenance.

What you will learn?

Day 1: Introduction to Predictive Maintenance
Duration: 1 Hour
Objective: Provide a foundational understanding of predictive maintenance within the manufacturing sector.
Session Details:

• Overview of Predictive Maintenance: Definition, importance, and how it compares to reactive and preventative maintenance.
• Data Collection in Manufacturing: Types of data collected from manufacturing equipment that are crucial for predictive analysis.
• Introduction to Data Science Tools: Brief on tools and software that will be used throughout the course (e.g., Python, R, specific libraries for machine learning).
• Hands-On Activity: Setting up the data science environment and exploring initial datasets.

Day 2: Machine Learning for Equipment Failure Prediction
Duration: 1 Hour
Objective: Dive into machine learning techniques that enable the prediction of equipment failures.
Session Details:

• Feature Engineering: Identifying and engineering features from manufacturing data that are predictive of equipment health.
• Building Predictive Models: Introduction to key machine learning algorithms used in predictive maintenance (e.g., regression analysis, decision trees, neural networks).
• Model Training and Validation: Steps to train models and validate their accuracy using real or simulated data.
• Hands-On Activity: Participants will build a simple predictive model using a provided dataset.

Day 3: Implementing and Optimizing Predictive Maintenance Models
Duration: 1 Hour
Objective: Learn to implement, deploy, and optimize predictive maintenance models in a manufacturing setting.
Session Details:

• Model Deployment: How to deploy models into production environments safely and efficiently.
• Performance Monitoring and Optimization: Techniques for monitoring model performance over time and optimizing them for better accuracy and reliability.
• Case Studies: Discussion of successful predictive maintenance implementations in the industry.
• Hands-On Activity: Using their models, participants will simulate deploying them in a production-like environment and monitor their outputs.