Predictive Maintenance

Executive Summary

Component failures are inevitable—but surprises are optional. Predictive maintenance turns noisy telemetry into early warnings and planned interventions. AI agents continuously estimate health and remaining useful life, surface actionable risks, and coordinate parts and schedules through automated workflows. The product and engineering team keeps humans in control: safety-critical steps require approval, and every decision is logged. Results: fewer emergencies, lower service cost, and higher asset availability.

The problem today

Most organizations oscillate between reactive fixes and rigid calendar-based maintenance. Reactive repairs drive overtime, rush shipping, and missed SLAs; over-maintenance wastes parts and labor. Data is fragmented across SCADA/BMS, PLCs, historian databases, tickets, and spreadsheets, so patterns go unnoticed. Spare parts aren’t staged, and technicians arrive without the right context.

The AI-led flow

1. Ingest & normalize: High-frequency sensor data, operating hours, events/alarms, ambient conditions, and operator notes are normalized to a canonical asset schema.
2. Detect & forecast: Anomaly detection flags off-nominal behavior; survival/RUL models forecast failure windows with confidence bounds and reason codes.
3. Triage & recommendations: A policy engine classifies severity, proposes interventions (inspect, lubricate, replace, recalibrate), and estimates impact on uptime, cost, and safety.
4. Parts & scheduling: For high-confidence cases, the workflow creates a CMMS work order, checks parts inventory/lead times, reserves kits, and proposes the lowest-impact maintenance window.
5. Technician brief: A generated job pack includes last alarms, trend charts, likely root causes, SOPs, and safety notes.
6. Human-in-the-loop: Supervisors approve actions, adjust windows, or escalate. Safety interlocks and permits-to-work remain mandatory.
7. M&V & learning: Post-maintenance outcomes close the loop; models retrain on successes and false alarms to improve precision.

Privacy-by-design, compliance-aligned: Minimal operational data, role-based access, region-bound processing (e.g., EU), immutable audit logs, and explicit control over automated actions. Decision support, not replacement of safety or engineering judgment.

Pilot scope (30–60 days)

• Scope: One asset class (e.g., pumps, motors, HVAC units, conveyors) across 1–2 sites.
• Interfaces: Read from historian/IoT; write to CMMS/EAM for work orders and inventory reservations.
• Success criteria: Unplanned downtime hours (pilot scope), % planned vs. unplanned work, alarm precision/recall, parts stockouts on critical work orders, and MTTR/MTBF movement.

Hypothesis metrics (illustrative, not guaranteed):

• Unplanned downtime −10–25% in pilot scope.
• Shift to planned maintenance +15–30% of work orders.
• Emergency callouts −15–30%; parts stockouts on critical orders −30–50%.

Quick ROI math (scenario):
If a line’s downtime costs €5,000/hour and averages 10 hours/month, a conservative 20% reduction saves 2 hours/month → €10,000/month (≈ €120k/year). Add reduced overtime and rush freight (e.g., €30–50k/year) for total savings in the €150–170k/year range; typical operating costs of the system are a fraction of recovered value.

Risks & mitigations

• Data quality & sensor drift: Signal validation, calibration checks, and confidence thresholds; suppress automation when confidence is low.
• False positives/negatives: Dual gating (anomaly + trend), human review for medium-confidence cases, and active learning on feedback.
• Integration & change management: Start read-only; enable automated work orders only after a shadow period and SOP alignment.
• Safety & compliance: Enforce permits, LOTO procedures, and escalation paths; never bypass interlocks.

From pilot to scale

Expand asset classes and sites; incorporate computer vision for belts and leaks; add spares optimization with probabilistic demand; integrate with production planning to schedule around takt time. Over time, reliability moves from firefighting to continuous improvement.

Expected impact (illustrative):

• Lower repair and emergency service costs through prevention.
• Reduced downtime and improved service quality for customers.
• Extended lifespan of infrastructure components.
• Fewer customer complaints and lower churn due to higher reliability.
• ROI through cost savings and uptime improvements within months.