Create living virtual replicas of every asset in your facility. IoT sensor data feeds real-time digital twins powered by AI that predict failures weeks in advance, simulate maintenance scenarios, forecast remaining useful life, and auto-generate work orders – transforming reactive maintenance into predictive intelligence.
Critical Asset Twins
From IoT sensor ingestion to automated maintenance actions, iFactory’s Digital Twin AI creates a closed-loop predictive maintenance ecosystem for every asset in your facility.
IoT sensors feed real-time vibration, temperature, and pressure data.
Build dynamic virtual replicas reflecting live asset behavior.
AI forecasts failures and calculates remaining useful life.
Test what-if scenarios without risking real equipment.
Auto-generate work orders and optimize PM schedules.
Connect vibration, temperature, pressure, flow, current, and acoustic sensors across your entire facility. iFactory’s sensor-agnostic platform ingests data from any IoT device, PLC, or SCADA system – feeding continuous telemetry into digital twin models with sub-second latency for complete asset visibility.
Works with any IoT sensor, PLC, SCADA, or BMS system.
Vibration, temperature, pressure, flow – sub-second data.
Local processing for zero-latency in connectivity dead zones.
AI infers missing data points from correlated parameters.
CNC Lathe #7 • Live Sensor Feed
Machine learning models trained on your historical failure data and real-time sensor telemetry detect anomalies invisible to human operators. iFactory AI identifies degradation patterns, calculates failure probability, and alerts maintenance teams with 90–95% accuracy – weeks before equipment fails.
AI spots deviations from normal operating baselines.
Confidence scores on predicted failure modes.
Remaining Useful Life calculated for every critical component.
Distinguish between different failure types simultaneously.
Failure Mode: Spindle bearing inner race fatigue
Confidence: 92% probability within 18 days
Evidence: Vibration spectrum shift at 4.8x RPM harmonic, temperature trending +0.5°C/day for 12 days
Remaining Useful Life
Use digital twins as a risk-free testing ground. Simulate how assets behave under different operating conditions, maintenance intervals, load changes, and environmental factors. Validate maintenance strategies, optimize PM schedules, and test operational changes – all without touching real equipment.
Simulate failure modes, load changes, and stress tests.
Find the optimal maintenance interval for each asset.
See how production speed changes affect component wear.
Compare repair-vs-replace costs for informed decisions.
Scenario Comparison • CNC Lathe #7 Spindle
When the digital twin detects a predicted failure, iFactory automatically generates a prioritized work order complete with asset ID, failure mode, required parts, recommended procedure, and technician assignment. After repair, the twin validates recovery to baseline and logs the resolution for future model training.
Predictive alerts auto-create structured work orders.
AI identifies required parts and checks inventory.
Twin confirms asset returns to baseline after repair.
Every repair outcome improves future prediction accuracy.
Auto-Generated Work Order
Post-Repair Validation
Every digital twin generates a real-time health score from 0–100 based on all monitored parameters. View your entire fleet’s health at a glance, identify the most at-risk assets instantly, and prioritize maintenance resources where they matter most – across single or multi-site operations.
Composite score from all monitored parameters.
Assets ranked by criticality and failure proximity.
Compare asset health across all facilities on one screen.
Track health degradation curves over weeks and months.
Highest Risk Assets
Track how digital twin predictions translate into real-world savings. Dashboards show prediction accuracy rates, prevented failures, avoided downtime hours, maintenance cost savings, and OEE improvements – proving ROI and justifying continued investment in predictive technology.
Track true positives, false alarms, and model drift.
Measure digital twin’s impact on equipment effectiveness.
Accuracy
PredictionsSaved
Avoided costsUptime
RecoveredOEE
↑ 12%Prevented Failures • Last 12 Months
Twin intelligence flows seamlessly into work orders, assets, inventory, and analytics for a fully connected predictive maintenance ecosystem.
Auto-generated from alerts
Full lifecycle twins
Predictive parts staging
AI-optimized schedules
OEE & ROI reports
Two-way data sync
Whether you operate CNC machines, turbines, HVAC systems, or production lines – iFactory’s Digital Twin AI adapts to your equipment types and operational patterns.
CNC, robotic arms, stamping presses, paint booth twins.
Turbines, generators, transformers, wind farm twins.
Cleanroom HVAC, reactors, filling lines, cold chain twins.
Conveyors, mixers, packaging lines, refrigeration twins.
Pumps, compressors, heat exchangers, reactor vessel twins.
Engine components, landing gear, avionics, composite twins.
HVAC, elevators, BMS, lighting, and chiller plant twins.
Engine health, tire wear, braking, and drivetrain twins.
Predictive alerts prevent failures before production stops.
AI models achieve 90–95% failure prediction accuracy.
Optimized scheduling eliminates unnecessary PM tasks.
Prevented failures and optimized scheduling deliver fast payback.
Everything you need to know about iFactory’s Digital Twin AI capabilities.
A digital twin is a dynamic virtual replica of a physical asset that mirrors its real-time behavior using IoT sensor data. iFactory’s digital twins continuously ingest vibration, temperature, pressure, and other sensor readings, then apply AI and machine learning models to detect anomalies, predict failures, calculate remaining useful life, and recommend optimal maintenance actions – all before problems affect production.
Effective digital twins typically require 10–50 sensor inputs per asset depending on complexity, including vibration, temperature, pressure, flow rate, and electrical parameters. iFactory is sensor-agnostic and works with any IoT sensor brand, PLC, SCADA, or BMS. For assets without physical sensors, AI-powered “virtual sensors” can infer condition data from correlated parameters like amperage and cycle time.
iFactory’s AI models typically achieve 90–95% accuracy for common failure modes after 6–12 months of data collection and model training. Accuracy improves continuously as the system learns from each prediction outcome. The platform tracks true positive rates, false alarm rates, and model drift so you can monitor and validate prediction quality over time.
RUL forecasting estimates how many days or operating hours remain before a component is likely to fail. The digital twin calculates RUL by analyzing degradation patterns from sensor data and comparing them to historical failure curves. This allows maintenance teams to schedule replacements during planned downtime windows rather than reacting to surprise breakdowns.
Yes. iFactory’s digital twin module integrates natively with its CMMS and connects to external ERP systems (SAP, Oracle, Microsoft Dynamics) via APIs. Predictive alerts auto-create work orders in the CMMS, check parts availability in inventory, and feed resolution data back to the twin for continuous model improvement – creating a true closed-loop predictive maintenance ecosystem.
What-if simulation lets you test maintenance strategies, operating changes, and failure scenarios virtually without risking real equipment. For example, you can simulate “what happens if we increase production speed by 15%?” and see the predicted impact on component wear and failure timelines. This helps managers compare repair-vs-replace costs, optimize PM intervals, and make data-driven capital planning decisions.
Start with 2–5 high-value critical assets that have the highest failure costs, complex operating conditions, and sufficient sensor data. Rotating machinery (motors, pumps, compressors), CNC machines, and process-critical equipment typically deliver the fastest ROI. After proving value on pilot assets, expand to the full fleet using the same digital twin infrastructure.
Initial twin deployment on pilot assets takes 2–4 weeks including sensor setup, data ingestion configuration, and baseline model creation. Predictive models need 6–12 months of continuous data to reach 90%+ accuracy. However, anomaly detection and basic health scoring provide value from day one. Full facility-wide deployment with mature predictive models typically takes 6–12 months.
Predict failures weeks in advance, simulate scenarios risk-free, and auto-generate work orders from AI insights. Schedule a free demo.