Nerve Use Cases

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Condition Monitoring

Condition monitoring keeps track of key indicators for machine performance (vibration, temperature etc.) in order to identify any changes which may be indicative of a developing fault. Nerve provides a rich set of functions to implement a condition monitoring and predictive maintenance solution.

Find out more:

  • In our case study with Felss, we show how Nerve provides a platform for Felss to offer its customers secure data connectivity on the machine, in the factory, and to the cloud.
  • In our technical article with Felss, we go into more detail about how they use Nerve, among others in their current project for tool wear detection.

Use Nerve for Condition Monitoring

  • Read sensor data via fieldbuses (Profinet, EtherCAT) using the Soft PLC module
  • Read data from PLCs or other sources (S7, Modbus, OPC UA) using the integrated Data Gateway and store it in a local database
  • Optionally, run algorithms on the data using NodeRed or Python based containers created by the Nerve SDK
  • Use the integrated Grafana visualization tool to display the data locally and create alarms using rules
Nerve Features

Remote Service

Remote Service includes all service and maintenance work that is carried out on machines and production lines without the service technician being on site. Nerve integrates a full featured remote service subsystem and allows software updates online and offline.

Find out more:

Use Nerve for Remote Service

  • Remotely access all devices in your machine using an integrated Tunnel (supports any Ethernet based protocol)
  • Directly display a remote desktop (VNC and RDP) in your browser without the need for installing additional software
  • Access devices inside the machine, use the Edge Node as jump host
  • Collect logs centrally for all your devices
  • Choose between continuously online or being online only during remote maintenance
  • Keep your edge software and applications up to date
Nerve Features

Machine Learning

Machine Learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Nerve can be used as a platform for collecting data to feed training models as well as a distribution method for deployment of Machine Learning algorithms and custom code.

Find out more:

  • In our case study with craftworks, we show how the combination of Nerve with craftworks’ navio unites modular edge computing with seamless deployment of AI models into one ready-to-use solution.

Use Nerve for Machine Learning

  • Use Nerve Data Services to collect training data
  • Create and train your models using your preferred Machine Learning toolkit
  • Deploy models and configurations using Nerve and integrate into a DevOps flow using the API
  • Connect your trained models to live data using the Data Services SDK
  • Visualize results using the built-in Grafana dashboards
Nerve Features

Digital Twin

A digital twin is a representation of a physical object, process or service. The twin can be a digital replica of an object in the physical world such as a CNC mill or a turbine. Nerve provides secure access to machine data which acts as the foundation for a digital twin.

Find out more:

Use Nerve for Digital Twin

  • Collect data from the machine and model them in OPC UA for higher level services
  • Read sensors using fieldbuses (Profinet, EtherCAT) using the integrated Soft PLC
  • Secure your data using OPC UA security features
Nerve Features