Program Overview
This program gives maintenance teams a focused, hands-on introduction to Intelligent Automation in Maintenance (AIM), covering AI/ML, IIoT sensors, robotics, digital twins, and automated decision-making. Led by a senior expert with 25+ years of experience, it blends essential concepts with real industrial examples in predictive maintenance, machine health monitoring, RUL prediction, and automated inspections. Participants learn to design sensor setups, interpret machine data, integrate OT–IT systems, and apply automation to reduce failures and improve uptime. Practical exercises and a capstone case enable teams to apply AIM tools directly to their maintenance environment.
Features
- Apply AI, IIoT and automation to modernize maintenance processes and shift from reactive to predictive & prescriptive workflows
- Interpret machine condition data (vibration, acoustic, thermal, electrical) and detect anomalies using practical AIM toolkits
- Design autonomous inspection, robotics-assisted maintenance, and digital twin–based fault prediction concepts
- Build integrated maintenance workflows linking detection → diagnosis → work order automation → action closure for measurable uptime improvement
Target audiences
- Maintenance & Reliability Teams
- Industry 4.0 & Smart Maintenance Teams
- Automation, Robotics & OT–IT Integration Teams
- Plant Engineering, Utilities & Operations Teams
Curriculum
- 5 Sections
- 35 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- 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
Instructor
