Mon - Fr
/
9AM - 18PM
Project Duration
March 2022 – June 2024 (27 months)
Team
22 employees led by Chief Engineer: Robert Mason
Estimated Cost
$2.3 million
The Predictive Maintenance System leverages IoT sensors, machine learning, and big data analytics to predict equipment failures before they occur. This solution minimizes downtime and reduces maintenance costs by moving from a reactive to a proactive maintenance strategy.
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Analyze operational data to identify failure points (3 months
Build architecture for sensor integration and data analytics (5 months)
Deploy IoT sensors across machinery (7 months)
Analyze historical data for prediction models (6 months)
Optimize predictions and minimize false alerts (4 months)
Launch system and provide real-time monitoring (2 months)
Unplanned equipment failures disrupt production, increasing downtime and maintenance costs. Traditional preventive maintenance often leads to unnecessary repairs and resource waste.
Contributes to sustainable production by reducing waste, conserving energy, and minimizing disruptions in industries such as food processing, transportation, and manufacturing.
With predictive maintenance, companies can extend equipment life, reduce repair costs, and optimize production schedules, boosting overall efficiency.
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