Description

1. Learning Outcomes

• Understand concepts of predictive maintenance, condition monitoring, and failure prediction.
• Analyze sensor data, equipment logs, and performance indicators for patterns and anomalies.
• Apply machine learning techniques such as regression, classification, and time-series modeling.
• Implement anomaly detection methods to identify irregular behavior.
• Develop early warning alerts and health-score systems for equipment.
• Build simple predictive maintenance models and evaluate their performance.

2. Program Highlights & Key Features

• Real-world case studies from manufacturing, energy, and industrial systems.
• Hands-on training with sample datasets and predictive modeling tools.
• Sessions on sensor analytics, feature engineering, and real-time monitoring.
• Practical exercises on building anomaly detection workflows.
• Coverage of Industry 4.0 concepts and modern predictive maintenance technologies.