🧠 General Introduction

As industrial operations evolve toward smarter and more efficient systems, the shift from preventive to predictive maintenance has become a strategic necessity. Preventive maintenance relies on fixed schedules and assumptions, often leading to unnecessary interventions or missed failures.

Predictive maintenance, on the other hand, uses real-time sensor data to monitor equipment health and forecast potential breakdowns before they occur. This data-driven approach enables organizations to reduce downtime, optimize resource usage, and extend asset life.

This workshop is designed to guide participants through the transformation process, from traditional maintenance models to intelligent, predictive strategies. Attendees will learn how to collect and analyze sensor data, apply predictive algorithms, and integrate digital platforms to support decision-making.

The program includes hands-on sessions, case studies, and practical tools to help participants design and implement predictive maintenance plans tailored to their operational needs. Ideal for engineers, managers, and technical teams seeking to enhance reliability and embrace Industry 4.0 technologies.

🎯 Target Audience

• Maintenance and reliability engineers
• Asset and risk management professionals
• Industrial data analysts
• Digital transformation and technology leaders
• Quality and continuous improvement teams
• Academics and researchers in smart maintenance

🎯 Expected Outcomes

• Understand the differences between preventive and predictive maintenance
• Learn how to collect and analyze sensor data
• Acquire skills in forecasting equipment failures
• Apply predictive models in industrial environments
• Design a smart maintenance strategy based on real-time data

🧪 Scientific Topics:

Track 1: Maintenance Concepts and Evolution

Session 1: Preventive vs. Predictive Maintenance

• Definitions and key differences
• Pros and cons of each approach
• When and why to transition

Session 2: Foundations of Predictive Maintenance

• Failure prediction principles
• Role of data in maintenance decisions
• Technologies enabling predictive models

Track 2: Sensor Data Collection and Management

Session 1: Types of Industrial Sensors

• Vibration, temperature, and pressure sensors
• Installation and calibration techniques
• Challenges in harsh environments

Session 2: Data Acquisition and Processing

• Tools for data collection
• Raw data cleaning and formatting
• Linking sensor data to analytics platforms

Track 3: Predictive Modeling and Applications

Session 1: Algorithms for Failure Prediction

• Machine learning and trend analysis
• Statistical and predictive models
• Accuracy and validation techniques

Session 2: Real-World Predictive Maintenance Cases

• Industrial case studies
• Implementation challenges and solutions
• Lessons learned and best practices

Track 4: Digital Integration and Smart Systems

Session 1: Connecting Sensors to CMMS and SCADA

• System integration strategies
• Real-time data visualization
• Alerts and automated reporting

Session 2: Leveraging AI and IoT Technologies

• AI-powered diagnostics
• IoT-enabled equipment monitoring
• Enhancing decision-making with smart tools

Track 5: Strategy Design and Organizational Change

Session 1: Building a Predictive Maintenance Plan

• Assessing current capabilities
• Defining goals and KPIs
• Selecting tools and methodologies

Session 2: Change Management and Implementation

• Training technical teams
• Embedding predictive practices into workflows
• Measuring impact and ROI