Predictive Maintenance
Reducing industrial machinery downtime with IoT and AI.
The Challenge
A heavy manufacturing client was experiencing frequent, unplanned downtime due to critical machinery failures. Their maintenance schedule was based on fixed intervals, which either serviced machines too early (costly) or too late (catastrophic failure). They needed a way to anticipate failures before they happened.
Our Solution
We deployed a network of IoT sensors to collect real-time data on vibration, temperature, and power consumption from key machinery. This data was streamed to a cloud platform where our machine learning model, trained on historical failure data, analyzes patterns to predict the remaining useful life (RUL) of components. Maintenance teams receive alerts to schedule repairs proactively.
Key Outcomes
- Reduced unplanned downtime by 50% within the first year.
- Cut annual maintenance costs by 20% by optimizing service schedules.
- Extended the operational lifespan of critical assets.
- Improved worker safety by preventing catastrophic equipment failures.