Machine health and reactive maintenance practices present significant challenges, resulting in trillions of dollars in lost productivity, the highest percentage of industrial injuries, and substantial energy and emissions waste. Surprisingly, only about 20% of machine degradation is due to normal wear and tear, with the remaining 80% stemming from factors like operating conditions, imperfect maintenance, and system design.

This presentation explores how advanced AI-driven predictive maintenance can address these challenges by leveraging the right data, analysis, and actions to reduce downtime and eliminate waste.

Key topics include:

• Root Causes and Solutions: Insights into the sources of machine degradation and the actionable opportunities for improvement, emphasizing the importance of predictive maintenance practices.

• The Right Data: Strategies for collecting comprehensive, high-fidelity data necessary for accurate diagnostics and analysis, including the need for frequent data sampling, intermittent sampling, and a single pane of glass of 3rd party and legacy sensors.

• The Right Analysis: Techniques for simplifying and organizing data into actionable insights using AI and machine learning; automating the identification, diagnosis, and resolution of issues through prioritization and workflows.

• The Right Action: Ensuring that insights are delivered to the right person, at the right time, and in the right context to empower workers to take effective action; improving maintenance quality, optimizing machine health, and eliminating unplanned events.

Benefits of Predictive Maintenance:

• Increased Safety: Proactively identifying potential equipment failures to prevent accidents and ensure a safer working environment.

• Reduction in Energy Consumption: Optimizing equipment performance to reduce energy use and support eco-friendly operations.

• Understanding Process Anomalies: Using continuous data to correlate process changes with machine health indicators, allowing for timely adjustments and improved operating practices.

• Identifying Improper Maintenance: Detecting and correcting maintenance errors in real-time to enhance equipment care and longevity.

By the end of this session, attendees will gain a comprehensive understanding of how AI-driven predictive maintenance can transform their approach to machine health. They will be equipped with the knowledge and tools to implement these advanced techniques, leading to safer, more efficient, and sustainable operations.