Curriculum
- 5 Sections
- 35 Lessons
- 1 Day
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- Foundations of Intelligent Automation in Maintenance6
- 1.1Evolution of maintenance: from reactive → preventive → predictive → intelligent/automated
- 1.2Sensing, data fusion, analytics, automated diagnostics, decision automation
- 1.3Asset hierarchy, criticality ranking, and failure mode patterns
- 1.4Role of IIoT devices, vibration sensors, thermal imaging, oil particle counters, and energy monitors
- 1.5Structured data pipelines: acquisition → preprocessing → event detection → automated triggers
- 1.6Asset Health Index (AHI) and automated threshold modelling
- Real-World Maintenance Challenges and Failure Drivers8
- 2.1Inconsistent manual inspections causing missed early-warning signals
- 2.2Failures due to poor condition monitoring coverage across rotating & static equipment
- 2.3Delayed diagnosis due to rely on isolated data (vibration-only or temperature-only)
- 2.4Over-maintenance driven by calendar-based schedules
- 2.5High downtime due to unplanned failures in motors, pumps, gearboxes, conveyors, chillers, compressors
- 2.6Fragmented CMMS usage, leading to weak history logs and poor decision support
- 2.7Supplier delays in spares due to late detection of component degradation
- 2.8Workforce skill gaps in interpreting multi-sensor condition data
- Intelligent Automation Frameworks8
- 3.1Automated Condition Monitoring Loops: Integrated vibration–thermal–acoustic–electrical data
- 3.2Smart Diagnostic Engines: Algorithms classifying misalignment, imbalance, bearing wear, lubrication failure
- 3.3Automated Work Order Generation: Sensor thresholds triggering maintenance tasks in CMMS/EAM
- 3.4Predictive Maintenance Models (PdM 2.0): ML-based anomaly detection, RUL (Remaining Useful Life) estimation
- 3.5Digital Twin for Rotating Equipment: Real-time simulation for what-if analysis
- 3.6Closed-Loop Reliability Improvement: Feedback from RCA fed into automated rules
- 3.7Energy & Load-Based Maintenance Optimisation: reducing unnecessary interventions
- 3.8Maintenance–Production Sync: Automated alerts to scheduling teams for planned downtime windows
- AIM Deployment Architecture & Technology Roadmap8
- 4.1Asset criticality study & prioritisation
- 4.2Sensor selection: Vibration, ultrasonic, thermal, current signature, oil analysis
- 4.3Edge gateway configuration and communication protocols
- 4.4Integration with CMMS/EAM/SCADA
- 4.5Data architecture: Historian, cloud/edge analytics, M2M connectivity
- 4.6Governance: Alert thresholds, data cleansing, calibration cycles
- 4.7Change management for technicians & supervisors
- 4.8Building internal capability for AI-enabled maintenance
- Hands-On Exercises, Diagnostics & Automation Simulation5
Sensing, data fusion, analytics, automated diagnostics, decision automation
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