From sensor data to automated maintenance in 30 seconds.
Monday: 3 prompts for failure prediction. Tuesday: automated agent workflows. Wednesday: team collaboration patterns. Thursday: complete production architecture. IoT sensors, ML prediction, agent orchestration, CMMS integration, and edge computing for 10,000+ machines.
Key Assumptions
- •Monitor 10-10,000 industrial machines (CNC, pumps, conveyors, robots)
- •Sensor data: vibration, temperature, pressure, current draw (100+ metrics per machine)
- •Historical failure data available (min 6 months for ML training)
- •CMMS system exists (SAP PM, IBM Maximo, or similar)
- •Edge devices available for local processing (optional but recommended)
- •Network connectivity: 99%+ uptime, latency <100ms to cloud
- •Compliance: ISO 55000 (asset management), OSHA safety standards
System Requirements
Functional
- Real-time sensor data ingestion (100Hz-10kHz per sensor)
- ML-based failure prediction (7-14 day horizon)
- Anomaly detection with <5 min latency
- Automated work order generation in CMMS
- Root cause analysis using LLMs
- Maintenance scheduling optimization
- Historical trend analysis and reporting
Non-Functional (SLOs)
💰 Cost Targets: {"per_machine_per_month_usd":15,"per_prediction_usd":0.05,"total_infrastructure_monthly_usd":2000}
Agent Layer
planner
L4Decomposes maintenance tasks, selects specialized agents, coordinates workflow
🔧 Task prioritizer, Agent registry lookup, Workflow orchestrator
⚡ Recovery: Retry with exponential backoff, Fallback to manual queue if all agents fail, Alert ops team after 3 failures
prediction
L3Runs ML models to predict failures, estimates time-to-failure, confidence scores
🔧 Feature store API, ML inference service, Model registry, Drift detector
⚡ Recovery: Use cached predictions if inference fails, Fallback to rule-based heuristics, Flag low-confidence predictions for review
diagnosis
L3Root cause analysis using LLMs, retrieves similar past failures, generates explanations
🔧 LLM API (GPT-4/Claude), Vector DB (RAG retrieval), Parts catalog API, Maintenance manual search
⚡ Recovery: Retry with different LLM if timeout, Use template responses if LLM unavailable, Human review for complex cases
scheduling
L3Optimizes maintenance schedules, creates work orders, assigns technicians
🔧 CMMS API, Scheduling optimizer, Inventory system, Notification service
⚡ Recovery: Queue work orders if CMMS down, Manual assignment if optimizer fails, Alert supervisor for critical failures
evaluator
L2Validates predictions, checks data quality, measures agent performance
🔧 Metrics store, Statistical tests, Drift detector, Alerting service
⚡ Recovery: Continue monitoring even if alerts fail, Log all evaluations for offline analysis
guardrail
L4Safety checks, prevents dangerous predictions, enforces business rules
🔧 Policy engine, Safety rule checker, Audit logger
⚡ Recovery: Fail-safe: block all actions if guardrail down, Human approval required for critical decisions
ML Layer
Feature Store
Update: Real-time (streaming) + batch (hourly aggregates)
- • Vibration RMS (rolling 1min, 5min, 1hr windows)
- • Temperature delta (current vs baseline)
- • Current draw deviation
- • Pressure variance
- • Cycle count
- • Time since last maintenance
- • Bearing age (days)
- • Lubrication status
- • Load factor (% of rated capacity)
- • Environmental (humidity, ambient temp)
Model Registry
Strategy: Semantic versioning (major.minor.patch), Git-backed, immutable artifacts
- • BearingFailureClassifier
- • MotorAnomalyDetector
- • PumpDegradationRegressor
Observability
Metrics
- 📊 predictions_per_hour
- 📊 ml_inference_latency_p95_ms
- 📊 false_positive_rate_percent
- 📊 work_orders_created_per_day
- 📊 cmms_api_latency_p95_ms
- 📊 agent_task_success_rate
- 📊 edge_gateway_uptime_percent
- 📊 sensor_data_quality_score
- 📊 model_accuracy_rolling_7d
- 📊 cost_per_prediction_usd
Dashboards
- 📈 ops_dashboard
- 📈 ml_performance_dashboard
- 📈 agent_health_dashboard
- 📈 cmms_integration_dashboard
- 📈 cost_analytics_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Serverless-first (Lambda/Cloud Run)
- • Managed services (RDS, ElastiCache, IoT Hub)
- • Single region (us-east-1 or eu-west-1)
- • Shared LLM API keys
- • Basic RBAC (3 roles)
- • Email notifications only
→ Deploy in 2-4 weeks
→ Cost: $500-2K/mo for 100-1K machines
→ Manual model retraining (monthly)
→ Best for: Pilot projects, single-facility deployments
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS/GKE/AKS)
- • Private networking (VPC peering, PrivateLink)
- • Multi-region active-active
- • BYO KMS/HSM for encryption
- • SSO/SAML integration (Okta, Azure AD)
- • Dedicated ML infrastructure (GPU clusters)
- • Multi-tenant data isolation (separate DBs per customer)
- • Custom CMMS integrations
- • Advanced monitoring (Datadog, Dynatrace)
→ Deploy in 3-6 months
→ Cost: $25K-100K/mo for 10K+ machines
→ Automated model retraining (daily)
→ 99.9% SLA with financial penalties
→ Data residency (EU/US/APAC regions)
→ Best for: Multi-site manufacturing, global deployments
📈 Migration: Start with startup variant for pilot (1-2 sites). Migrate to enterprise when: (1) >1K machines, (2) Multi-site deployment, (3) Regulatory requirements (data residency, SSO). Migration path: (a) Deploy K8s in parallel, (b) Dual-write to both systems, (c) Gradual traffic shift, (d) Decommission serverless after validation.
Risks & Mitigations
⚠️ Sensor data quality degradation
High✓ Mitigation: Data quality checks at edge + cloud. Flag low-quality readings. Alert technician to check sensor. Skip prediction if <70% quality score.
⚠️ Model drift (machine behavior changes)
Medium✓ Mitigation: Weekly drift detection (KL divergence). Automatic retraining triggered if accuracy <80% for 3 days. A/B testing before full rollout.
⚠️ False positives (unnecessary maintenance)
Medium✓ Mitigation: Target <5% false positive rate. Guardrail Agent blocks low-confidence predictions. Human review for high-cost repairs (>$10K).
⚠️ CMMS integration failure
Low✓ Mitigation: Queue work orders with retry (exponential backoff, up to 1hr). Alert supervisor if CMMS down >1hr. Manual entry as fallback.
⚠️ LLM hallucinations (fake part numbers, wrong diagnosis)
Medium✓ Mitigation: 4-layer validation: confidence scores, parts catalog cross-check, logical consistency, human review. 0.5% hallucination rate, 100% caught.
⚠️ Edge gateway offline (network outage)
Medium✓ Mitigation: Local data buffering (24hr). Cloud-based fallback (higher latency). Auto-reconnect + sync when online.
⚠️ Regulatory compliance (data residency, audit)
Low✓ Mitigation: Multi-region deployment with data residency controls. 7-year audit logs (immutable storage). Annual compliance audits (ISO 55000).
Evolution Roadmap
Phase 1: MVP (0-3 months)
Weeks 1-12- → Deploy edge gateways on 10-50 machines
- → Implement basic anomaly detection (rule-based + Isolation Forest)
- → Manual work order creation (no CMMS integration)
- → Single-model prediction (XGBoost for bearings)
- → Email notifications only
Phase 2: Agent Orchestration (3-6 months)
Weeks 13-24- → Scale to 100-500 machines
- → Implement multi-agent system (Planner, Prediction, Diagnosis, Scheduling)
- → CMMS integration (SAP PM or Maximo)
- → LLM-based root cause analysis (RAG)
- → Automated work order creation
- → A/B testing framework for models
Phase 3: Enterprise Scale (6-12 months)
Weeks 25-52- → Scale to 1,000-10,000 machines
- → Multi-region deployment (US + EU + APAC)
- → Multi-model ensemble (XGBoost + LSTM + Isolation Forest)
- → Continuous learning (automatic retraining)
- → Advanced scheduling optimization (constraint solver)
- → Enterprise features (SSO, RBAC, data residency, audit)
Complete Systems Architecture
9-layer architecture from edge devices to cloud orchestration
Sequence Diagram - Failure Prediction Flow
Predictive Maintenance - Agent Orchestration
6 ComponentsPredictive Maintenance - External Integrations
10 ComponentsData Flow - Sensor to Work Order
30-second end-to-end flow from anomaly detection to CMMS
Scaling Patterns
Key Integrations
SAP Plant Maintenance (PM)
IBM Maximo
IoT Sensor Network
ERP System (Inventory)
Notification Services
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Edge gateway offline | Cloud-based anomaly detection (higher latency) | Degraded (10s vs 1s detection) | 99.5% |
| ML inference service down | Use cached predictions (last 24hr) + rule-based heuristics | Reduced accuracy (70% vs 85%) | 99.9% |
| LLM API timeout (diagnosis) | Template-based root cause (top 10 common failures) | Generic diagnosis | 99.0% |
| CMMS API unavailable | Queue work orders, retry with exponential backoff (up to 1hr) | Delayed work order creation | 99.5% |
| Sensor data quality degraded | Flag low-quality readings, use last-known-good values | Lower prediction confidence | 100% (never predict on bad data) |
| Database unavailable | Read from replica (read-only mode) | No new predictions, existing data accessible | 99.99% |
| Model drift detected | Rollback to previous model version | Slightly lower accuracy (revert to v-1) | 99.9% |
Multi-Agent Architecture
6 specialized agents collaborate autonomously with human oversight
┌──────────────────────────────────────────────┐
│ ORCHESTRATOR (LangGraph) │
└────────────────┬─────────────────────────────┘
│
┌──────────┼──────────┬─────────┬────────┐
│ │ │ │ │
┌───▼───┐ ┌──▼───┐ ┌───▼────┐ ┌─▼──┐ ┌──▼────┐
│Planner│ │Predict│ │Diagnosis│ │Sched│ │Guardrail│
│Agent │ │Agent │ │ Agent │ │Agent│ │ Agent │
└───┬───┘ └──┬───┘ └───┬────┘ └─┬──┘ └──┬────┘
│ │ │ │ │
└─────────┴──────────┴────────┴────────┘
│
┌───────┴────────┐
│ │
┌──▼─────┐ ┌────▼─────┐
│ ML │ │ CMMS │
│ Layer │ │ Adapter │
└────────┘ └──────────┘Agent Collaboration Flow
Reactive Agent
Reflexive Agent
Deliberative Agent
Orchestrator Agent
Levels of Autonomy
Advanced ML/AI Patterns
Production ML engineering for predictive maintenance