From sensor data to failure prediction in 10 minutes.
Monday: 3 core prompts (sensor analysis, failure prediction, maintenance scheduling). Tuesday: automated agent code. Wednesday: team workflows (plant managers, technicians, data engineers). Thursday: complete technical architecture. Components, data flows, ML pipelines, SCADA integration, and scaling patterns for 10,000+ machines.
Key Assumptions
System Requirements
Functional
- Ingest sensor data from 100-10K machines at 10Hz-1kHz
- Detect anomalies in real-time (<1min latency)
- Predict failures 10-60min in advance with 95%+ accuracy
- Generate maintenance work orders in CMMS automatically
- Provide root cause analysis and recommended actions
- Support multi-site deployments with centralized monitoring
- Historical trend analysis and reporting dashboards
Non-Functional (SLOs)
💰 Cost Targets: {"per_machine_per_month_usd":15,"per_prediction_usd":0.02,"storage_per_machine_per_year_usd":120}
Agent Layer
planner
L4Decompose maintenance tasks, route to specialized agents
🔧 anomaly_detector, failure_classifier, root_cause_analyzer
⚡ Recovery: If anomaly_detector fails → fallback to rule-based thresholds, If CMMS unavailable → queue work order for retry
executor
L3Run anomaly detection and failure prediction models
🔧 fft_transformer, isolation_forest_model, lstm_failure_predictor
⚡ Recovery: If model inference timeout → use last known good prediction, If feature extraction fails → skip prediction, log error
evaluator
L3Validate predictions, filter false positives, assess quality
🔧 false_positive_filter, domain_rule_engine, confidence_calibrator
⚡ Recovery: If validation inconclusive → flag for human review, If rule engine fails → default to conservative (allow prediction)
guardrail
L4Enforce safety limits, policy checks, prevent unsafe actions
🔧 safety_policy_checker, production_impact_estimator, risk_calculator
⚡ Recovery: If policy check fails → block action, alert ops, If risk calc unavailable → default to safest action
root_cause_analyzer
L3Diagnose failure root cause using sensor patterns and maintenance history
🔧 pattern_matcher, causal_inference_model, maintenance_kb_search
⚡ Recovery: If causal model fails → return top 3 likely causes, If KB search timeout → use generic recommendations
orchestrator
L4Coordinate all agents, manage state, handle retries and fallbacks
🔧 all_agents, state_manager, retry_handler
⚡ Recovery: If agent fails → retry 3x with backoff, If multiple failures → degrade gracefully (skip non-critical agents)
ML Layer
Feature Store
Update: Real-time (1min batch) + daily batch recalculation
- • vibration_rms (root mean square)
- • vibration_fft_peaks (frequency domain)
- • temperature_rolling_avg_5min
- • current_draw_variance
- • rpm_deviation_from_baseline
- • pressure_rate_of_change
- • bearing_health_score (composite)
- • time_since_last_maintenance_hours
- • machine_age_days
- • production_load_factor
Model Registry
Strategy: Semantic versioning (major.minor.patch), immutable artifacts in S3
- • anomaly_detector_v3
- • failure_classifier_lstm_v2
- • ttf_predictor_xgboost_v1
- • root_cause_bert_v1
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ Sensor data quality issues (noise, drift, calibration errors)
High✓ Mitigation: Implement data quality checks at edge. Auto-detect sensor drift. Alert maintenance to recalibrate. Use robust feature extraction (e.g., FFT reduces noise impact).
⚠️ False positives causing alert fatigue
Medium✓ Mitigation: Tune confidence thresholds. Implement evaluator agent to filter low-quality predictions. Track false positive rate as KPI. Retrain model monthly.
⚠️ Integration with legacy SCADA systems
High✓ Mitigation: Use OPC UA adapters (industry standard). Fallback: manual data export if real-time integration not possible. Budget 2-3 months for integration.
⚠️ Model drift due to changing operating conditions
High✓ Mitigation: Monitor feature distributions weekly. Retrain model when drift detected (KL divergence >0.15). Implement online learning for gradual adaptation.
⚠️ Regulatory compliance (data residency, audit trails)
Medium✓ Mitigation: Deploy multi-region with data residency controls. Immutable audit logs (7-year retention). Annual SOC2 audit. GDPR compliance for EU sites.
⚠️ Vendor lock-in (cloud provider, CMMS, SCADA)
Medium✓ Mitigation: Use open standards (OPC UA, FHIR). Abstract integrations behind adapters. Store data in portable formats (Parquet). Maintain multi-cloud deployment option.
⚠️ Cost overruns at scale (10K+ machines)
Medium✓ Mitigation: Set cost guardrails ($15/machine/month). Use spot instances for batch training. Compress historical data. Monitor unit economics weekly.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: Pilot (0-3 months)
Phase 2: Production Rollout (3-6 months)
Phase 3: Enterprise Scale (6-12 months)
Complete Systems Architecture
9-layer architecture from edge sensors to executive dashboards
Presentation
4 components
API Gateway
4 components
Agent Layer
5 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Sequence Diagram - Failure Prediction Flow
Automated data flow every hour
Data Flow - Sensor to Work Order
Key Integrations
SCADA Integration
MES Integration
CMMS Integration (SAP PM, Maximo, Fiix)
Historian Integration (OSIsoft PI, Wonderware)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Sensor data stream interrupted | Use last known good values for up to 5 min, then alert | Degraded (no new predictions) | 99.9% |
| Anomaly detection model unavailable | Fall back to rule-based thresholds (vibration >10g, temp >80°C) | Reduced accuracy (90% → 75%) | 99.5% |
| CMMS API down | Queue work orders in local DB, retry every 5 min | Delayed work order creation (eventual consistency) | 99.0% |
| Feature extraction fails (corrupt data) | Skip prediction for that machine, log error, alert data eng | Single machine not monitored temporarily | 99.9% |
| False positive storm (>10% FP rate) | Increase confidence threshold from 0.85 → 0.92, alert ops | Fewer predictions (higher precision, lower recall) | 99.5% |
| Database unavailable | Read from replica (read-only mode), queue writes | No new predictions saved, dashboards show stale data | 99.9% |
| Network partition (site isolated) | Edge inference continues locally, sync when reconnected | No centralized visibility, local predictions only | 99.0% |
RAG vs Fine-Tuning for Root Cause Analysis
Hallucination Detection in Predictions
Evaluation Framework
Dataset Curation
Agentic RAG for Root Cause Diagnosis
Federated Learning for Multi-Site Deployment
Tech Stack Summary
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