SKF Moves Turbine Inspection into the Cloud

Sweden’s SKF has developed an app-based turbine maintenance support system designed to improve inspection efficiency and reduce costs for frontline maintenance teams, while providing operators with real time data on turbine performance and reliability. 

The SKF Enlight mobile and cloud based data collection combines the intuitive ease-of-use of an iOS or Android app, running on a standard mobile device, with the power to access customised workflows for specific tasks, and the ability to collect data from a wide range of sources and sensors. Behind the scenes, the DataCollect app connects wirelessly to the SKF cloud. All inspection data is uploaded and stored for review and analysis.

Wind energy companies can take their paper-based maintenance processes online with SKF Enlight, creating data collection forms that guide staff step-by-step through standard inspection activities, aided by images and online manuals. The fully customisable forms can be set up to suit operator standard operating procedures, including items such as mandatory safety checks to protect personnel.

In use, the system is able to connect directly to a wide variety of sensors, allowing photographs and video to be collected and uploaded together with data on temperature, humidity, vibration and a host of other operating parameters. SKF Enlight’s smart forms also adapt based on the data collected, allowing different inspection protocols depending on turbine operating hours, for example, or immediately suggesting corrective actions when out-of-tolerance readings are found.

“The user-friendly SKF Enlight system allows our wind energy customers to engage more of their staff in inspection and maintenance activities,” said Hannes Leopoldseder, SKF Global Sales Manager Wind Operations and Maintenance.

“The system is easily scalable as companies expand their infrastructure, and safely storing inspection data in the cloud in a secure, standardised way has significant advantages for companies looking for a high level, real time view of equipment performance.”