Edge Computing & Predictive Maintenance for Gas Compressors

This whitepaper discusses the challenges HOERBIGER faced when setting up a predictive maintenance solution and how TTTech Industrial supported them in defining a technical solution that uses our edge computing platform Nerve.

Large gas compressors are used worldwide in applications like hydrogen delivery, gas pipelines, and liquified natural gas (LNG) transport in vessels and for chemical processes. The compressors are a fundamental part of the process in these applications. They are expensive equipment and subject to considerable wear, but having a redundant compressor on site to ensure availability is usually not feasible.

A predictive maintenance solution can help to plan maintenance efforts and to detect wear or faults early and thus avoid costly unplanned downtimes for repairs. Such a solution needs to encompass thousands of compressors of different types and with a wide range of individual configurations, e.g. for scaled rollouts, offline operation, and air-gapped system configuration.

Nerve supports the efficient, scaled rollout of individually configured edge devices for predictive maintenance in gas compressors.

Nerve is an edge hosting, management, and data acquisition platform that provides a basis for configuration and operation at scale. It includes a feature called Nerve DNA that offers an easy-to-use method to configure a multitude of edge devices both online and offline. This allows the customer to focus on his core know-how in creating predictive maintenance algorithms for his use case

In particular, Nerve’s rich feature set provides solutions for the following critical challenges when setting up a predictive maintenance solution for gas compressors:

High-speed data acquisition with local analytics
Individually configurable network settings, data ingestion, processing, and visualization
Configuration in the factory and on-premises, air-gapped configuration

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