AI for Predictive Maintenance: Prevent Downtime, Boost Equipment Life

Master how to use AI for early detection of equipment issues, optimize maintenance schedules, and avoid costly downtimes. This course equips maintenance teams with tools to transform reactive repair into predictive strategy.

Course Description:

This 2-day course introduces plant maintenance professionals to the power of AI in anticipating equipment failures and optimizing asset performance. Participants will explore how AI models process sensor data, logs, and operational metrics to predict breakdowns, prioritize interventions, and shift from reactive to predictive maintenance strategies.

With real use cases across discrete and process manufacturing environments, this course helps teams reduce unplanned downtime, improve spare part planning, and lower maintenance costs—without needing to build models or code systems from scratch.

Who Should Attend:

• Maintenance Managers and Reliability Engineers
• Plant Engineers and Equipment Technicians
• TPM/CBM Program Leaders
• Asset Management Professionals
• Factory Managers overseeing production downtime

Course Objectives:

Knowledge Acquisition:

• Understand AI concepts used in predictive maintenance
• Learn how anomaly detection and time-series forecasting apply to equipment behavior
• Explore data sources: SCADA, PLCs, IoT sensors, CMMS logs
• Recognize limitations and best practices in deploying AI maintenance systems

Skills Development:

• Interpret AI-generated maintenance predictions and alerts
• Identify critical equipment for AI monitoring based on risk and cost
• Use dashboards and heatmaps to prioritize asset inspections
• Evaluate patterns in downtime, wear, and failure events

Practical Application:

• Simulate failure detection using vibration, thermal, or voltage data
• Build a basic predictive maintenance schedule from mock AI insights
• Calculate potential cost savings from avoided downtime
• Design a simple pilot plan to introduce AI to a high-value asset line

What will I Learn From it:

• Catch early failure indicators before breakdowns occur
• Extend asset life through intelligent scheduling
• Reduce emergency repair costs and unplanned downtime
• Use AI reports to guide inspections and prioritize high-risk equipment
• Make a strong business case for expanding predictive capabilities

Training Methodology

• Tool Simulations: AI dashboards and anomaly detection previews
• Scenario-Based Exercises: Work with mock equipment failure data
• Interactive Planning: Build a realistic pilot plan with peer feedback
• Case-Driven Instruction: Learn from successful (and failed) real-world examples
• Capstone Presentation: Final team strategy pitch for predictive readiness

Requirement/
pre-requisites:

• Background in maintenance, asset management, or reliability engineering
• Basic familiarity with asset failure modes and maintenance cycles
• No programming or data science knowledge required
• Laptop with Excel or browser access for exercises