Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive methodologies
- Real-world ROI and industry case studies
Data Collection and Preparation
- Sensors, IoT, and data logging in industrial contexts
- Cleaning and structuring data for analysis
- Handling time series data and labeling failures
Machine Learning for Predictive Maintenance
- Overview of machine learning models (regression, classification, anomaly detection)
- Selecting appropriate models for predicting equipment failures
- Model training, validation, and performance evaluation
Developing the Predictive Workflow
- End-to-end pipeline: data ingestion, analysis, and alerting
- Utilizing cloud platforms or edge computing for real-time analysis
- Integrating with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Predicting specific failure modes
- Calculating Remaining Useful Life (RUL)
- Creating asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and trends
- Establishing thresholds and generating alerts
- Designing actionable insights for operators
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and explainability in industrial AI systems
- Change management and team adoption strategies
Summary and Next Steps
Requirements
- Knowledge of industrial equipment and maintenance processes
- Basic understanding of AI and machine learning principles
- Experience with data collection and monitoring systems
Target Audience
- Maintenance engineers
- Reliability engineering teams
- Operations managers
14 Hours
