AI-Powered Predictive Maintenance for Industrial Systems

• Understanding of industrial equipment and maintenance workflows
• Basic familiarity with AI and machine learning concepts
• Experience with data collection and monitoring systems

Audience

• Maintenance engineers
• Reliability teams
• Operations managers

AI-powered predictive maintenance applies machine learning and data analytics to forecast equipment failures and optimize maintenance schedules. It transforms reactive maintenance models into proactive strategies, enabling better uptime, cost reduction, and asset longevity.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement AI-driven predictive maintenance solutions in industrial environments.

By the end of this training, participants will be able to:

• Understand how predictive maintenance differs from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-powered analysis.
• Apply machine learning models to detect anomalies and predict failures.
• Implement end-to-end workflows from sensor data to actionable insights.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises and case studies.
• Live demonstration and practical data workflows.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Predictive Maintenance

• What is predictive maintenance?
• Reactive vs. preventive vs. predictive approaches
• Real-world ROI and industry case studies

Data Collection and Preparation

• Sensors, IoT, and data logging in industrial environments
• Data cleaning and structuring for analysis
• Time series data and failure labeling

Machine Learning for Predictive Maintenance

• Overview of machine learning models (regression, classification, anomaly detection)
• Choosing the right model for equipment failure prediction
• Model training, validation, and performance metrics

Building the Predictive Workflow

• End-to-end pipeline: data ingestion, analysis, and alerts
• Using cloud platforms or edge computing for real-time analysis
• Integration with existing CMMS or ERP systems

Failure Mode and Health Index Modeling

• Predicting specific failure modes
• Calculating Remaining Useful Life (RUL)
• Developing asset health dashboards

Visualization and Alerting Systems

• Visualizing predictions and trends
• Setting thresholds and creating alerts
• Designing actionable insights for operators

Best Practices and Risk Management

• Overcoming data quality issues
• Ethics and explainability in industrial AI systems
• Change management and adoption across teams

Summary and Next Steps