Introduction

Predictive maintenance (PdM) is revolutionizing the way organizations manage their assets and operations by leveraging advanced technologies and data-driven approaches to anticipate failures and optimize maintenance schedules. This 5-day advanced training course focuses on equipping participants with the skills and tools to implement and manage predictive maintenance strategies that minimize downtime, reduce costs, and enhance asset reliability. With a forward-looking approach, the course highlights the integration of IoT, AI, and data analytics to meet the challenges of modern facilities management.

Objectives

By the end of this course, participants will:

1. Understand the principles and benefits of predictive maintenance.
2. Learn how to implement and manage PdM technologies and frameworks.
3. Gain expertise in analyzing data to predict and prevent asset failures.
4. Explore the integration of IoT, AI, and CMMS for advanced maintenance strategies.
5. Develop actionable plans for transitioning from reactive or preventive maintenance to predictive models.

Who Should Attend?

This course is ideal for:

• Facilities Managers and Maintenance Engineers.
• Operations and Asset Managers.
• CMMS Administrators and IT Professionals supporting maintenance programs.
• Reliability Engineers and Data Analysts in maintenance roles.
• Professionals involved in asset management and maintenance optimization.

Course Outline

Day 1: Introduction to Predictive Maintenance

• Understanding Maintenance Strategies
  • Reactive, preventive, and predictive maintenance compared.
  • Key benefits of PdM: Cost reduction, efficiency, and reliability.
• Principles of Predictive Maintenance
  • Predicting failures based on data and condition monitoring.
  • Key technologies enabling PdM: IoT, sensors, and analytics.
• Workshop: Assessing current maintenance practices and identifying PdM opportunities.

Day 2: Data Collection and Condition Monitoring

• Sensor Technologies for PdM
  • Overview of vibration, thermal, ultrasonic, and acoustic sensors.
  • Choosing the right sensors for various asset types.
• Condition Monitoring Techniques
  • Monitoring critical parameters: Temperature, pressure, vibration, and more.
  • Real-time vs. periodic data collection.
• Interactive Session: Designing a sensor network for a facility’s critical assets.

Day 3: Data Analytics and Machine Learning for PdM

• Analyzing Data for Predictive Insights
  • Understanding failure modes and patterns through historical data.
  • Using statistical and AI-based models for prediction.
• Machine Learning and AI in PdM
  • Training machine learning models for anomaly detection and prediction.
  • Overview of tools and platforms for PdM analytics.
• Case Study: Using machine learning to predict failures in a manufacturing facility.

Day 4: Integration of PdM into Maintenance Systems

• IoT and CMMS Integration
  • Connecting IoT devices with CMMS and EAM platforms.
  • Automating work orders based on predictive alerts.
• Implementing PdM Strategies
  • Planning the transition from preventive to predictive maintenance.
  • Overcoming challenges: Data silos, change management, and ROI justification.
• Workshop: Creating an implementation roadmap for a predictive maintenance program.

Day 5: Sustainability, Risk Management, and Future Trends

• Sustainability through Predictive Maintenance
  • Reducing energy consumption and extending asset life.
  • Aligning PdM with ESG (Environmental, Social, and Governance) goals.
• Risk Management in PdM
  • Identifying and mitigating risks in PdM adoption.
  • Building resilience through predictive insights.
• Future of Predictive Maintenance
  • Emerging trends: Digital twins, autonomous maintenance, and Industry 4.0.
  • Preparing for smart cities and next-generation asset management.
• Conclusion and Certification.