Predictive Maintenance & Plant Intelligence
ML models predict weekly equipment health from sensor, PLC and maintenance data — and a natural-language assistant lets teams query the plant directly.
The challenge
Plants relied on reactive or periodic maintenance, so failures surfaced too late:
- Unexpected machine breakdowns
- Disrupted production schedules
- Poorly utilised maintenance resources
- Slow root-cause analysis
- Rising downtime costs
Teams were forced to react after failures had already occurred.
How it works
Sense, predict, explain, act
Every signal the plant produces flows into models that rank risk — and every prediction arrives with its evidence:
- 01
Ingest PLC data, sensor telemetry, machine logs, breakdown history, maintenance records and operator feedback
- 02
Run failure-prediction, downtime-prediction, risk-scoring and failure-mode classification models
- 03
Publish weekly health alerts and at-risk machine rankings to the maintenance dashboard
- 04
Surface cell-level risk analysis and downtime forecasts for planners
- 05
Answer team questions through the AI plant assistant, in natural language
What we built
Key capabilities
Explainable predictions
Every flag ships with the evidence: failure counts vs plant average, lifetime failure rate, days since last failure, missing preventive records.
AI plant assistant
“Which machines need inspection this week?” “Why is ABC1234B critical?” — engineers query the plant conversationally.
Recommended actions
Each prediction includes concrete next steps — inspect sensors, verify PLC signals, check servo health, review PM schedules.
Cell-level risk view
Downtime risk rolled up by cell and line, so planners see where the plant is most exposed.
Sample prediction
Machine ABC1234C — flagged CRITICAL
- Risk score
- 0.88
- Predicted failure
- Equipment failure
- Confidence
- 98%
- Expected downtime
- 2h 30m
- Evidence
- 209 failures in 90 days vs plant avg of 18 · 97% lifetime failure rate vs 45% factory average · last failure 1 day ago · no preventive record
Business impact
What it changed
Downtime reduction
Maintenance is scheduled before breakdowns, not after them.
Higher equipment availability
More uptime translates directly into plant productivity.
Resource optimisation
Crews are deployed against ranked risk, not routine calendars.
Reduced production loss
Issues are fixed before throughput drops, not after.
Knowledge retention
The AI institutionalises maintenance expertise instead of losing it to attrition.
Faster decisions
Anyone can query plant health conversationally — no report queue.
“Trust is the product: predictions teams act on because every one of them can explain itself.”
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