A Predictive Maintenance IIoT solution

Client: FIBRO GmbHmanufactures metal stamping products. The Company produces standard parts and rotary tables.

Challenge: FIBRO wanted to leverage analytics and  IOT to develop a platform that could help maintain quality, performance and efficiency of rotary tables by avoiding unforeseen malfunction. The platform was required to collect data from various sensors. Once the data is collected, predictive maintenance algorithms identify detection of critical areas including:

  • Wear of bearings
  • Unbalanced load
  • Low consumables
  • Overload
  • Crashes

Solution: SSI spent time to understand FIBRO’s business operations and architected a platform that includes sensors to collect data, a data hub, a DAQ Board  and a web-based analytics app. Data collected from all the sensors is fed into the Sensor Box which transmits the sensor raw data in real time to the DAQ Board where it is analyzed and stored after every complete rotation. This data is analyzed and captured by the web-based analytics application that provides the end user the ability to track critical information and health checks.

Results: FIBRO clients can now easily monitor and maintain the health of their rotary tables through predictive analytics and avoid costs that might occur due to damages.

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