Grid Asset Failure Prediction
Predictive maintenance solution for distribution networks that forecasts substation asset health and prioritizes transformer failure risk to reduce outages, extend equipment life, and optimize maintenance planning.
The Problem
“Predict transformer and substation asset failure risk before outages occur”
Organizations face these key challenges:
Aging transformer fleet with 40+ year old assets
Reactive maintenance after faults or alarms
Fragmented data across SCADA, CMMS, inspections, and asset registries
Inefficient time-based maintenance practices
Impact When Solved
The Shift
Human Does
- •Review asset registers, inspection reports, alarms, and maintenance history to assess transformer condition
- •Apply age, load, and rule-based thresholds to rank substation assets for inspection or repair
- •Decide maintenance priorities and schedule field crews using fixed intervals and engineer judgment
- •Respond to faults and outages with reactive work orders and emergency repair planning
Automation
- •Aggregate basic condition indicators into static health dashboards
- •Flag threshold breaches from SCADA alarms and test results
- •Produce simple condition index rankings based on predefined rules
Human Does
- •Approve maintenance priorities and intervention plans for the highest-risk assets
- •Review AI-identified risk drivers and validate actions for critical transformers and substations
- •Handle exceptions where data is incomplete, operating context has changed, or field findings conflict with predictions
AI Handles
- •Continuously forecast asset health and transformer failure risk across the fleet
- •Fuse telemetry, maintenance records, inspections, alarms, and environmental factors into dynamic risk scores
- •Rank assets by urgency and generate prioritized maintenance and inspection recommendations
- •Monitor health changes over time and alert teams to emerging degradation before faults occur
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve maintenance priorities for critical transformers or substations without review by the maintenance planner or substation asset manager [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Grid Asset Failure Prediction implementations:
Key Players
Companies actively working on Grid Asset Failure Prediction solutions: