AI-Assisted Predictive Maintenance
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.
More about the service
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