Technical requirements for a predictive maintenance solution

Technical requirements for a predictive maintenance solution

Gas compressors are critical components in an energy distribution infrastructure and must operate 24/7. Predictive maintenance is a key functionality that helps ensure availability.

HOERBIGER is a globally active technology company that provides performance-critical products and safety solutions for the oil, gas, automotive, and process industries. For their predictive maintenance solution for gas processors, they needed an IIoT platform to provide the supporting infrastructure at the edge, allowing them to focus on the development of the predictive maintenance algorithm.

The challenge was to find a platform that could provide high-speed data ingestion for the recording of wear data that was capable of both online and offline operation. While gas compressors are usually connected to the Internet to allow service personnel access to data and alarms, some applications require air-gapped environments that have no Internet or network connectivity, usually to reduce the likelihood of cyberattacks. In air-gapped environments, predictive maintenance functionality must be executed on-premises.

In our whitepaper, we show how HOERBIGER uses TTTech Industrial’s edge computing platform Nerve as the supporting infrastructure at the edge, providing data acquisition, processing, and visualization together with cybersecurity functions for their predictive maintenance solution for gas compressors.

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