Reduced unscheduled maintenance costs of machines through predictive analytics using sensor data
Reduced unscheduled maintenance costs of machines through predictive analytics using sensor data
2 PagesA global leader in industrial automation struggled with costly, unexpected equipment breakdowns across its hydroforming presses due to the difficulty of analyzing complex signals from over eight hundred sensors. To solve this challenge, Sigmoid deployed an advanced predictive analytics solution powered by specialized machine learning software. Sigmoid’s intelligent platform processed massive volumes of sensor data to accurately identify critical failure indicators and predict maintenance needs before breakdowns occurred. By eliminating guesswork and replacing rigid scheduled maintenance with proactive insights, Sigmoid's robust software empowered the client to drastically reduce unscheduled downtime, optimize operational efficiency, and minimize maintenance costs.
