Pilot network fault prediction and automatic control

Electricity and telecommunications network operators predict faults and control their networks automatically

Future prospectStrictly regulatedMonitoring sensor and machine dataEnergy supply, Telecommunications

Fits: IT & software development · Production, engineering & maintenance

Overloads, equipment failures and bottlenecks in the electricity or telecommunications network could be detected before customers notice them, and the network would partly reroute itself. Predictions for individual pieces of equipment are already running at some network operators; automatic control by AI is only in pilot projects and research networks.

Is this for you?

  • Your company wants to become a pilot partner or prepare early for AI-supported network control.
  • You have measurement data from the network control system, local substations or network elements, a fault history and a control centre that can support shadow operation.
  • Network control, IT security and management are prepared to back a limited pilot with stop criteria and know the obligations for high-risk systems.
First step today

Do not put network plans, topologies, vulnerabilities or control system data into an AI assistant. Instead, use the template below to have the state of trials compiled with sources, and check which projects actually control the network automatically and which only give recommendations to the control centre.

How to go about it

  1. Define the pilot area and goal
  2. Combine measurement data and fault history
  3. Have the system learn normal operation in shadow operation
  4. Define warning and intervention limits
  5. Bring suggestions into the control centre
  6. Evaluate the pilot and decide

Templates to copy

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Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

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Same approach, other use cases

8 more use cases follow the pattern “Monitoring sensor and machine data”.

Detect battery degradation in your EV fleet earlyDetect damage to tracks and points earlyDetect falls in residents’ rooms without a cameraHave AI monitor process data and suggest setpointsMonitor cows with sensors and barn camerasPredict machine failures from vibration and temperatureDetect leaks in the water network earlyPilot electronic noses for quality testing
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EV-02 · Legal status 08.10.2026 · Safety component in critical infrastructure: digital infrastructure, road traffic, water, gas, heating, electricity (Annex III No. 2)