Data Science for Predictive Maintenance in Manufacturing

To equip PhD scholars and academicians with advanced skills in data science and AI techniques to predict equipment failure in manufacturing, reducing downtime and costs. The course emphasizes predictive maintenance using data-driven approaches to improve operational efficiency.

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.

Data scientists, AI professionals, manufacturing engineers, and academic researchers.