End User
SWW
Description
The AI-Assisted Predictive Maintenance service transforms distribution asset management from reactive/time-based to condition-based maintenance. By fusing historical operational data, chemical diagnostics (DGA), and high-frequency sensor streams (vibration, thermography), the service calculates a normalised Asset Health Index (AHI) and predicts the Remaining Useful Life (RUL) of critical infrastructure including power transformers and switchgear.
Core Capabilities
Monitoring & Anomaly Detection
Predictive & Prescriptive Analytics
Business Need
DSOs face an ageing asset base increasingly stressed by electrification and renewables. Fixed maintenance intervals are inefficient — generating unnecessary work on healthy assets while risking rapidly degrading ones, leading to unplanned outages and high emergency replacement costs. The service provides early warnings of failure and risk-based prioritisation of maintenance activities, reducing unplanned downtime and operational expenditure.
Key Performance Indicators
True Positive Rate (Precision): proportion of High Risk alerts confirmed by physical inspection
Warning Lead Time: average interval between AI alert and potential functional failure
SAIDI Reduction: correlation between service deployment and reduced unplanned outage duration
OPEX Savings: reduction in routine time-based inspections replaced by condition-based interventions
Data Provided
Asset Health Index (0=critical, 100=new), Predicted RUL in days, Risk Class (Low/Medium/High)
Diagnostic Flags identifying specific issues (e.g., "Cooling System Efficiency Low", "Partial Discharge Detected")
Daily aggregated scores plus near-real-time anomaly alerts from high-frequency sensors
Inputs: SCADA load/temperature history, DGA reports (gas concentrations, moisture, furan), vibration/ultrasonic/thermal sensors, maintenance logs
TEF
TEF DSO