Scheduling maintenance before problems manifested. A leading European conveyor belt manufacturer and maintenance and servicing provider wanted to switch to predictive maintenance for elevators to identify problems before they occurred.
Situation: Creating value from large volume of information for predictive maintenance.
The client wanted to transition from reactive maintenance of its conveyor belts to predictive and preventive maintenance. Client was looking for a partner who can help them make sense of the large volumes of data it received from the sensors, and provide rich, real-time insights to schedule maintenance before problems cropped up.
Contact us to learn how we help businesses to reduce the likelihood of equipment failures but predicting and monitoring the performance of equipment.
Sensor data analytics
We utilized harnessed data from each sensor, created time series modeling and machine learning algorithm. We used Fourier and Support Vector machine algorithm for transformation of data, followed by predictive analysis to generate alerts in the form of maintenance alerts, instructions and recommendations.
We can help you analyze machine-generated information to identify issues and deploy countermeasures to prevent failures that could otherwise result in catastrophic outcomes. Request a free proposal to know more.
Improved the life time value of assets.
On the basis of our insights, the client was able to anticipate and quickly resolve maintenance issues for 75% of the conveyor belts it serviced, and ensure better maintenance management, which improved the life of assets by 4 years on average.