Industry
Energy
Work area
Predictive Maintenance
Optional
Proof of Concept in 3 Months
Project duration
6–9 months
Initial situation
Network operators are faced with aging infrastructure, including equipment, transformer stations, and switchgear that is decades old in some cases.
Maintenance is often carried out at fixed intervals, regardless of the actual condition of the components.
This leads either to unnecessary maintenance costs or to unexpected failures that compromise supply security and operational efficiency.
At the same time, the demands on grid stability, reliability, and cost transparency are increasing—a smart, data-driven approach is needed.
Objective
The aim of the project is to introduce an AI-supported maintenance strategy that analyzes condition data from network components in real time, forecasts failure probabilities, and plans maintenance measures in advance.
This helps to avoid disruptions, use resources in a more targeted manner, and significantly reduce operating costs.
Description of the Use Case
The combination of IoT sensors, cloud data analysis, and machine learning models creates a system that continuously monitors the status of critical network components and detects anomalies at an early stage.
The solution shifts the focus from reactive or cyclical maintenance to a proactive, data-driven maintenance strategy.
Key components of the project:
- Data collection & sensor integration: Attachment of sensors to transformers, cables, and switchgear to record temperature, vibration, current flow, and humidity.
- Cloud-based data platform: Storage and processing of recorded sensor data in real time.
- AI/ML analytics: Development of predictive models to detect deviations and predict failure probabilities.
- Alerts & recommendations for action: Automatic notification of critical values and recommendation of targeted maintenance measures.
- Integration into operational processes: Connection to asset management systems and existing maintenance processes.
Value Proposition
- Reduced downtime: Early detection of faults prevents network interruptions.
- Cost efficiency: Optimized maintenance intervals and targeted use of resources.
- Longer service life: Condition-based maintenance extends the service life of expensive equipment.
- Safety & compliance: Complete documentation of all maintenance activities.
- Sustainability: More efficient use of resources through targeted maintenance instead of blanket replacement measures.
Required Services
- Project management & control: Setting up project structure, scheduling, stakeholder communication.
- IoT & sensor technology: Selection and integration of suitable measurement technology and communication protocols.
- Data platform & AI models: Setting up the data infrastructure and developing prediction models.
- Integration & automation: Connection to maintenance and ERP systems.
- Training & change management: Training of operating personnel and support during process conversion.
Benefits
In short:
C4 Energy enables grid operators to switch from reactive to predictive maintenance.
AI-supported condition analyses enable early detection of faults, reduce costs, and extend the service life of critical infrastructure — a key component for tomorrow’s grid security.
Future-proofing your company
