From Scheduled Downtime to Data-Driven Uptime
Preventive maintenance has long been the backbone of reliability engineering, aiming to reduce equipment failures and unplanned downtime. However, traditional calendar-based approaches often lead to over-maintenance, unexpected breakdowns, and inefficient use of resources.
Today’s manufacturers are facing critical challenges:
Lack of real-time health visibility for legacy or cost-sensitive equipment
Over-reliance on operator intuition, especially in high-mix environments
Limited integration between maintenance routines and production analytics
These issues increase both operational risk and maintenance cost.
At SMARC, we are developing AI-powered preventive maintenance frameworks that combine lightweight data acquisition, signal interpretation, and behavioral pattern recognition to detect early signs of machine degradation. Our focus is on scalable intelligence, making predictive insights accessible without overhauling existing infrastructure.
We are currently validating models across multiple domains, from machining to molding environments, always guided by the principles of cost-efficiency, robustness, and industrial interpretability.
If you are exploring smart maintenance strategies or seeking to reduce downtime through intelligent insights, please don’t hesitate to reach out.
