Date: February 5, 2027
Time: 9:00 a.m. – 1:00 p.m.
Duration: 4 Clock Hours (One 4-Hour Session)
Location: State Fair Community College | Sedalia Campus | in-person
Instructor: Dr. Richard Byfield
Registration Deadline: January 29, 2027
Cost: $299 per participant
Category: Technology and Professional Development
Training Description
Unexpected equipment failures can disrupt production, increase maintenance costs, and create costly downtime. Introduction to AI-Driven Predictive Maintenance for Manufacturing introduces participants to how artificial intelligence and equipment data can be used to identify potential problems before failures occur.
Through instructor-led demonstrations, real-world manufacturing examples, and guided activities, participants will explore the differences between reactive, preventive, predictive, and prescriptive maintenance strategies. The course will examine how AI can analyze equipment data—including vibration, temperature, pressure, electrical current, and operating conditions—to recognize patterns, detect abnormalities, and provide early indications of potential equipment issues.
Participants will also learn how to identify equipment that may be a strong candidate for predictive maintenance, interpret basic maintenance indicators and alerts, and evaluate the operational and financial considerations associated with implementation.
By the end of the course, participants will have a foundational understanding of AI-driven predictive maintenance and a practical framework for identifying opportunities and planning an initial predictive maintenance project within their facility.
Learning Objectives
Upon completion of this training, participants will be able to:
- Explain the differences between reactive, preventive, predictive, and prescriptive maintenance strategies.
- Describe how artificial intelligence and machine data can support predictive maintenance.
- Identify common equipment data used to monitor machine health and performance.
- Recognize sensors and data sources used to detect changes in vibration, temperature, pressure, electrical current, and operating conditions.
- Identify equipment and processes that may be strong candidates for predictive maintenance.
- Interpret basic predictive maintenance outputs, including equipment health indicators, anomalies, trends, and maintenance alerts.
- Evaluate the potential benefits and challenges of implementing AI-driven predictive maintenance.
- Consider data availability, technology requirements, workforce readiness, cost savings, and downtime reduction when evaluating opportunities.
- Develop a preliminary strategy for introducing predictive maintenance within a manufacturing facility.
Training Outline
Understanding Maintenance Strategies
- Reactive maintenance: responding after failure
- Preventive maintenance: maintaining on a schedule
- Predictive maintenance: using equipment condition and data
- Prescriptive maintenance: recommending corrective actions
- Comparing the benefits and limitations of each approach
- Understanding where AI fits into modern maintenance strategies
How AI Supports Predictive Maintenance
- Understanding AI and machine learning in maintenance
- Using historical and real-time equipment data
- Recognizing equipment patterns and trends
- Detecting abnormal operating conditions
- Forecasting potential equipment failures
- Turning equipment data into actionable maintenance information
Sensors, Equipment & Maintenance Data
- Understanding condition-monitoring sensors
- Vibration monitoring
- Temperature monitoring
- Pressure and flow data
- Electrical current and power data
- Machine operating and performance data
- Maintenance history and equipment records
- Understanding the importance of reliable, accurate data
Identifying Predictive Maintenance Opportunities
- Identifying critical equipment and assets
- Evaluating equipment failure history
- Understanding the operational impact of downtime
- Identifying recurring maintenance issues
- Determining whether sufficient data is available
- Prioritizing equipment for predictive maintenance
- Selecting an appropriate pilot opportunity
Understanding Predictive Maintenance Results
- Reading equipment health indicators
- Understanding trends and condition changes
- Recognizing anomalies and abnormal behavior
- Understanding maintenance alerts
- Evaluating AI-generated recommendations
- Determining when additional investigation is needed
- Maintaining human oversight in maintenance decisions
Planning for Implementation
- Evaluating current maintenance practices
- Identifying technology and sensor requirements
- Assessing available equipment and maintenance data
- Engaging maintenance, operations, engineering, and IT teams
- Evaluating costs and potential return on investment
- Measuring downtime reduction and maintenance improvements
- Developing a preliminary predictive maintenance implementation strategy
Materials Provided
Who Should Attend
This training is designed for maintenance professionals, maintenance supervisors, engineers, reliability professionals, operations managers, production supervisors, continuous improvement teams, manufacturing leaders, and other employees responsible for equipment performance and reliability.
It is especially valuable for manufacturers interested in reducing unplanned downtime, improving equipment reliability, and exploring how AI and equipment data can support more proactive maintenance strategies.