Unplanned equipment breakdowns are one of the biggest disruptors to manufacturing efficiency. AI4M’s Predictive Maintenance solution uses advanced machine learning models and Condition-Based Monitoring (CBM) to anticipate failures before they happen—enabling a proactive approach to asset maintenance and lifecycle management.
Our system relies on CBM sensors that continuously monitor key mechanical indicators such as vibrations, temperature, current, and sound across motors, pumps, gearboxes, and other rotating equipment. Of particular importance are vibration patterns recorded at motor axes, which act as a fingerprint for machine health.
Machine learning algorithms analyze this rich time-series data to identify:
• Trends that indicate gradual wear (e.g., bearing degradation)
• Anomalies that may point to sudden or abnormal behavior (e.g., misalignment, imbalance,
loose parts)
These insights enable maintenance teams to schedule repairs before breakdowns
occur—minimizing downtime, reducing spare part consumption, and improving overall equipment
effectiveness (OEE).
Benefits of Predictive Maintenance
• Reduces unplanned downtime and production losses
• Improves equipment life through timely intervention
• Optimizes maintenance cost by avoiding unnecessary routine servicing
• Enables data-driven planning for spares and manpower
Real-World Impact
In industries like automotive, chemicals, bottling, and metals, AI4M’s predictive
maintenance solutions have helped clients identify:
• Failing motor bearings weeks in advance
• Pump cavitation due to suction blockage
• Gearbox imbalance caused by shaft misalignment
By replacing reactive firefighting with predictive foresight, our solution transforms maintenance from a cost center into a source of operational resilience.