Signal to Service: From Diagnosis to Prioritised Action

Service decisions are rarely based solely on data or rules. Organisations with inconsistent processes work with multiple data sources at once, while expert knowledge remains distributed across individual team members and service reports.

Monitoring and analytics tools receive sensor data and detect signals. However, they do not answer the crucial question: Which service action is technically correct and economically worthwhile? Rising cost pressures make this gap increasingly apparent.

In a critical machine component, vibration increases over several operating cycles while the temperature remains stable.

1. Data Capture: Sensor data on vibration and temperature is continuously collected.

2. Diagnose: The Signal to Service Agent identifies the pattern as early-stage bearing degradation or imbalance.

3. Risk Assessment: If the trend remains unchanged, the threshold will be reached within a few weeks. Downtime costs and spare parts availability are included in the assessment.

4. Handover: The AI agent recommends an inspection during the next maintenance window and close monitoring until then. It hands the case over to the service team with this rationale.

Result: The service team receives a scheduled, evidence-based recommendation rather than a non-specific alert and can plan the inspection accordingly.

Illustrative visualisation generated from sensor and CRM data.