Predictive Maintenance

What Is Predictive Maintenance?

Predictive Maintenance is a strategy that uses real-time data from sensors, machine learning models, and historical performance records to predict equipment failures before they occur. Unlike reactive or scheduled maintenance, PdM focuses on condition-based monitoring to optimize repair schedules.

How Predictive Maintenance Works

1. Data Collection: Sensors installed on machinery collect real-time data such as temperature, vibration, and pressure. NYRIS enhances this process by integrating visual data from cameras for additional insights.
2. Data Analysis: Machine learning algorithms analyze the collected data to identify patterns or anomalies that indicate potential failures. NYRIS uses synthetic data generation to train these algorithms for higher accuracy in detecting rare failure scenarios.
3. Actionable Insights: The system generates alerts and recommendations for maintenance actions, which can be integrated into enterprise systems like SAP for automated scheduling and inventory updates.

Industrial Applications

Manufacturing

Predictive Machine Monitoring: NYRIS’s solutions help manufacturers like DMG Mori reduce unplanned downtime by 72%, using AI to predict bearing failures in CNC machines 48 hours in advance.

Automotive

Assembly Line Optimization: Renault employs NYRIS’s predictive maintenance tools to monitor robotic arms on production lines, ensuring consistent quality and reducing repair costs by 30%.

Energy Sector

Power Plant Efficiency: Predictive maintenance identifies wear in turbines or generators, preventing costly breakdowns and optimizing energy output.

Benefits For Your Company

1. Reduced Downtime: Minimize unplanned machine stoppages by up to 65%, ensuring continuous operations.
2. Lower Maintenance Costs: Shift from reactive to predictive repairs, reducing unnecessary part replacements by 30%.
3. Improved Equipment Lifespan: Proactively address wear and tear, extending machinery life by an average of 20%.