Predictive Maintenance AI
For Manufacturing Plants
Predict failures on motors, pumps, compressors, conveyors and CNC machines before they happen. Integrated with SCADA, historian, SAP PM and CMMS.
Quick Answer
NeoBram builds predictive maintenance AI for manufacturing plants that forecasts equipment failures on motors, pumps, compressors and CNC machines using vibration, current and SCADA data, integrated with SAP PM, Maximo and your CMMS to auto-create work orders before failure.
Why This Matters
Stop Reactive Maintenance. Predict Failures Before They Happen.
Unplanned downtime costs the average manufacturer $260,000 per hour. Most plants still run reactive or time-based maintenance, replacing parts that still have life and missing the failures that actually stop production.
Modern predictive maintenance combines high-frequency vibration and current data, historian context, and machine learning models that learn each asset's failure signatures. Done right, it cuts unplanned downtime 30-50% and extends asset life 20-40%.
We deploy production-grade PdM on your plant - not a slide deck. Edge inference on PLC or industrial PC, models trained on your historian data, and bidirectional integration with SAP PM, Maximo or IBM Maximo so work orders are created automatically when failure risk crosses a threshold.
Our Tech Stack
Production-Grade Tools We Deploy
Data acquisition
ML & analytics
Edge & deployment
Workflow integration
Architecture Deep-Dive
How We Build It
Vibration & current signature analysis
FFT-based feature extraction from vibration and motor-current data to detect bearing wear, misalignment, imbalance and cavitation before they cause failure.
- Triaxial vibration at 10-25 kHz from IFM / SKF / Banner sensors
- Motor current signature analysis (MCSA) for rotor and stator faults
- Envelope spectrum analysis for bearing defect frequencies
- Edge feature extraction to minimise bandwidth to historian
Failure prediction & remaining useful life
Per-asset ML models that learn normal operating signatures and predict failure probability and remaining useful life with calibrated confidence intervals.
- Autoencoder and LSTM anomaly models for each asset class
- Survival analysis for remaining useful life estimates
- Failure-mode classification (bearing, seal, coupling, lubrication)
- Calibrated probability scores - not just alerts
CMMS work-order automation
Bidirectional integration with SAP PM, Maximo or your CMMS so predicted failures create work orders automatically with recommended parts, procedures and technician skills.
- Auto-create notifications and work orders in SAP PM / Maximo
- Recommended spare parts pulled from BOM
- Skill and crew assignment based on failure mode
- Closed-loop feedback: technician outcome retrains the model
Plant-wide reliability dashboards
Site-level visibility into asset health, MTBF, MTTR and avoided downtime so plant managers and reliability engineers can prioritise capital and labour.
- Asset criticality matrix with live health scores
- MTBF / MTTR trending by asset class and line
- Avoided downtime $ tracking against baseline
- Drill-down from plant to line to asset to sensor
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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