Turbnetic.ai
Electric Jet Engine Intelligence — GE Frame 5 / 6 / 7 · LM-Series · eVTOL

Stop Turbine Failures Before They Start

Real-time AI diagnostics for gas turbines, hybrid-electric propulsion, and eVTOL powertrains.
Detect combustion instability, blade fouling, and bearing degradation weeks before failure — zero hardware changes.

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0wk Early Detection Lead Time
0.2% Anomaly Detection Precision
$50K Avg. Prevented Failure Cost
0 hardware Changes Required
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Exterior View
Scroll to zoom inside the engine
8 Critical Sensor Streams
T1/P1 Inlet temp & pressure
T3/P3 Compressor exit
EGT Exhaust gas temp
T4 Turbine inlet temp
BRG1/2 Vibration signatures
FF Fuel flow rate
ML Engine
Multivariate Anomaly detection
RUL Remaining useful life
Self-learning Per-asset models
<200ms Inference latency
Capabilities

What the Platform Does

Raw Sensor Data
OPC-UA · PI · REST
AI Inference
<200ms · LSTM · IsoForest
Actionable Alert
CMMS · Dashboard · SMS
Core Engine · LSTM + Isolation Forest

Multivariate Anomaly Detection

Isolation Forest and LSTM autoencoder models trained on GE Frame 5/6/7 and LM-series patterns. Detects subtle cross-parameter deviations invisible to threshold-based alarms.

  • 8+ simultaneous sensor streams correlated in latent space
  • Anomaly score updates every 200 ms from raw sensor feeds
  • Zero false positives during validated baseline cruise conditions
Frame 5 · 6 · 7LM2500 · LM6000LSTM Autoencoder
99.2% Detection Precision
Turbnetic
Plant Alpha/GT-04 · Frame 7/Anomaly Monitor
1H6H24H7D
LIVE
Nominal Elevated Anomalous Score threshold 0.70 8 CHANNELS · 200 ms REFRESH
⚠ ANOMALY SCORE 0.87 MODEL LSTM-AE v2.3 EPOCH 14:32:07Z ● PIPELINE HEALTHY
Predictive Engine · Bayesian Models

Predicted Remaining Useful Life

Bayesian degradation models output a RUL estimate with 90% confidence intervals for each asset. Maintenance windows ranked by criticality — never face another surprise forced outage.

  • Per-asset baseline auto-calibrated during commissioning week
  • Confidence intervals tighten as more operational data accumulates
  • Integrates with CMMS for automatic work order generation
Bayesian RULMonte Carlo CICMMS Integration
23 wk Avg. Early Warning Lead Time
Turbnetic
Fleet/3 Assets/Predictive RUL
4W13W52W2Y
UPDATED 14:32Z
GT-04 GT-07 GT-11 Forecast RUL % · 90% CI BAND
▲ 1 CRITICAL · GT-04 48d HORIZON 52 WEEKS MODEL Bayesian RUL ● CMMS SYNCED
Combustion Analytics · Spectral Analysis

Combustion Instability Detection

Spectral analysis of dynamic pressure and cross-correlation with fuel-air ratio to flag lean blowout precursors and thermoacoustic oscillation onset — hours before flame-out risk.

  • 200 Hz band ΔP monitoring with harmonic decomposition
  • Dual-channel combustor inlet and exit pressure correlation
  • Auto load-reduction recommendation at configurable thresholds
FFT Spectral200 Hz BandLean Blowout
<8 hr Onset-to-Alarm Lead Time
Turbnetic
Plant Alpha/GT-04/Combustion Dynamics
0.1s0.2s0.5s
LIVE
CAN-A ΔP CAN-B ΔP 200 Hz band FFT · 0–400 Hz · 2 kHz SAMPLE
⚠ OSCILLATION ONSET RMS 2.4 kPa PEAK 198 Hz CHANNELS CAN-A + CAN-B
Performance Map · Flett Method

Blade Fouling & Degradation

Continuous compressor performance monitoring using modified Flett efficiency map methods to detect fouling accumulation and stage stall margin erosion weeks before it becomes critical.

  • Real-time operating point tracked against clean baseline map
  • Stall margin erosion quantified as percentage of design margin
  • Wash schedule optimizer cuts annual heat rate loss by up to 3%
Compressor MapStall MarginWash Optimizer
3% Heat Rate Improvement
Turbnetic
Plant Alpha/GT-07/Compressor Performance
7D30D90D1Y
UPDATED 14:30Z
Isentropic η Commissioning baseline Wash event η % · HEAT-RATE DEVIATION
η 93.2% ↓ 3.1 pp HEAT RATE +2.4% OP HOURS 7,200 h ● OFFLINE WASH ADVISED
Vibration Analytics · Envelope Analysis

Bearing Degradation Tracking

Envelope analysis and kurtosis trend monitoring on raw vibration time-series. Detects inner/outer race defects and roller element spalling weeks ahead of threshold breach.

  • BPFO / BPFI / BSF harmonic tracking updated in real time
  • Kurtosis trend isolates defect signal from structural noise floor
  • Spectral waterfall captures defect frequency growth history
BPFO / BPFI / BSFKurtosisWaterfall FFT
6 wk Avg. Detection Lead Time
Turbnetic
Plant Alpha/GT-04 · BRG-3/Vibration Spectrum
100Hz200Hz500Hz
LIVE
Spectral density BPFO 84 Hz 2×BPFO ENVELOPE · g RMS · 0–200 Hz
BPFO 4.2 g ↑ KURTOSIS 8.4 (thr 5.0) ISO 10816 ZONE C DEFECT OUTER RACE
Adaptive Learning · Federated ML

Continuous Per-Asset Retraining

Every turbine builds its own operational baseline. Models retrain nightly on each plant's history, accounting for fuel quality, ambient conditions, and load profile — a dataset moat that strengthens over time.

  • Federated architecture — raw sensor data never leaves your plant
  • AutoML selects optimal model architecture per asset class
  • Model drift detection auto-triggers targeted retraining cycles
Federated LearningAutoMLNightly Cadence
Nightly Per-Asset Retraining Cadence
Turbnetic
MLOps/GT-04 baseline/Model Training
50100147EPOCHS
RUN 02:14Z
Train accuracy Val accuracy Confusion matrix 12 ASSETS · NIGHTLY CADENCE
✓ CONVERGED · epoch 147 ACCURACY 99.2% (+0.3%) VAL LOSS 0.018 ● AUTO-DEPLOYED
Plugin-Based Hardware · Zero Cloud Dependency

Edge AI Device

A ruggedized DIN-rail edge module ships pre-loaded with your turbine's trained model. Runs full LSTM inference on-device — no internet required. Snaps into existing control cabinet wiring in under 30 minutes.

  • Plugin architecture — swappable model cards per asset class
  • Operates fully air-gapped; syncs model updates over secure OTA
  • Hardened for −40°C to 85°C, IP67, Class I Div 2 rated
  • Local alert relay: 4×DI / 4×DO + Modbus RTU output
DIN-Rail MountAir-GappedOTA UpdatesModbus RTU
<30 min Field Installation Time
Turbnetic
Plant Alpha/GT-04 · Cabinet A/Edge Node
1H6H24H7D
ONLINE
Sensor ingest Edge inference Cloud · CMMS sync LSTM v2.3 · OFFLINE-CAPABLE
✓ ONLINE · model v2.3 UPTIME 2,847 h INFERENCES 1.2M LAST SYNC 04:12Z
SCROLL TO EXPLORE
The $50B Problem

Unplanned Turbine Downtime
Is Destroying Your Margins

$50B
Annual cost of unplanned downtime in global power generation
72hrs
Average time to diagnose a combustion instability event post-failure
3-5%
Heat rate efficiency loss from undetected blade fouling per year
68%
Of major failures show warning signatures 2+ weeks prior in sensor data

Your historians are already capturing this data. You're just not acting on it fast enough.

Maintenance Agent AI

2,000 Pages of Manual.
One Precise Action.

Detecting the anomaly is half the job. Turbnetic.ai agents cross-reference the full maintenance manual and send the mechanic an exact, step-by-step directive — dashboard and pager, instantly.

01

Sensor Signal

Stage 3 discharge temp trending 8°C above baseline. Bearing #2 vibration rising 0.3 mm/s per day.

→
02

AI Anomaly Model

Pattern matches early-stage blade fouling. Confidence 94.2%. Lead time to degraded performance: 11–14 days.

→
03

Agent Orchestration

Cross-references manual §12.4.2, §7.1, Appendix C. Builds the response procedure with safety interlocks.

→
04

Mechanic Receives

One clear directive on the pager app — what, where, how, in what order. No manual lookup. No guesswork.

→
05

Fleet Learning Loop

Mechanic confirms the fix. The model retrains. Detection improves for every turbine on the platform.

Turbnetic Pager now
Unit GT-07 · Stage 3 Fouling · Priority 2
"Inspect compressor leading edge, Stage 3. Measure tip clearance at 3 points per §12.4.2. If >0.032 in, schedule offline wash within 72 hrs."
AcknowledgeOpen Procedure
Day 1

A new hire responds correctly to any turbine alert from their first shift — the institutional knowledge lives in the platform, not in one retiring engineer's head.

Zero lookup

Blade degradation, combustion instability, vibration, fouling — every directive arrives with the exact manual section, measurement procedure, and escalation threshold.

1 operator

One person can manage and dispatch maintenance across a 50+ turbine facility from a single fleet dashboard.

Zero-Friction Integration

Plug Into Your Existing
Plant Infrastructure

No new hardware. No rip-and-replace. API connectors for every major industrial historian and control system — live in days.

OSIsoft PI System
Native PI Web API connector with tag-level subscription streaming
OPC-DA · AF · PI Vision
GE APM / Asset360
Bidirectional APM integration for alert write-back and case management
REST · GraphQL · Asset Model
OPC-UA Server
Standard IEC 62541 OPC-UA subscription model for real-time historian bypass
IEC 62541 · Unified Architecture
DCS / SCADA
Honeywell Experion, ABB Symphony, Emerson DeltaV, Siemens SPPA-T3000
Modbus · DNP3 · IEC 61850
CMMS Systems
Automatic work order generation into SAP PM, IBM Maximo, and Infor EAM
SAP PM · Maximo · Infor
Custom REST API
Open webhook and REST API for bespoke integrations and data lake pipelines
REST · Kafka · InfluxDB
Proven Results

Built for the World's Most
Demanding Turbine Fleets

0wk
Average early detection lead time
Before failure — not before alarm
0.2%
Anomaly detection precision
Across Frame 7 and LM6000 fleets
$0K
Average value per prevented failure
Parts + labor + lost generation
0hr
Time to first live data connection
Via OPC-UA or PI Web API
0%
Heat rate improvement via fouling ops
Optimizing compressor wash schedules
<0ms
End-to-end inference latency
From sensor to scored alert
Edge Intelligence

Starts on Turbofans.
Built for Every Critical Machine.

Our compression pipeline distills the full cloud model into a tiny, hardware-optimized version that runs anywhere — offshore platforms, pipeline stations, air-gapped defense installations. No cloud. No connectivity. Same detection quality.

CLOUD

Teacher Model

Large ensemble trained on millions of fleet-hours. Full accuracy, continuously updated.

~500MB · GPU · Cloud
→
OPTIMIZATION PIPELINE

Compression Engine

Knowledge distillation · 8-bit quantization · pruning · hardware-aware architecture search.

Automated · per-chip tuning
→
EDGE

Student Model

Runs on a $50 compute module inside a ruggedized device. Fully offline, encrypted sync when available.

<8MB · <50ms · <5W
Expansion path
Gas Turbines now → Jet Engines · DoD Unmanned Platforms → Marine · Pipeline · Industrial Compressors → Grid & Electricity Demand Intelligence

One AI core. We prove it where failure costs the most — utility-scale gas turbines — then carry the same model, agents, and edge pipeline to every sensor-dense machine that can't afford to fail.

Platform

Operator Intelligence Dashboard

Built for plant engineers — not data scientists. Actionable, prioritized, and explained in plain language.

Interactive demo — click on turbines, nav icons, and alerts below

Turbnetic.ai — Fleet Live
app.turbnetic.ai/plant/alpha/live
LIVE
Plant Alpha › Fleet Overview
2 Alerts
14:32:07 UTC
2
Active Anomalies
+1 today
847h
Lowest Fleet RUL
GT-07
$1.4M
Failures Prevented
YTD
Jun 12
Next Scheduled
GT-01 overhaul
Fleet Status LIVE Click a row to inspect
GT-07 Frame 7
Combustion anomaly · 87% conf.
C 71HI
847
hrs
GT-01 Frame 5
Fouling stage 5 · schedule wash
D 48HI
312
hrs
GT-04 LM6000
Normal operations
A 96HI
2,340
hrs
GT-02 Frame 6
Normal operations
B 92HI
1,920
hrs
HIGH
P1UNACK
Combustion Instability Precursor · GT-07
GT07.COMB.T4_AVG OPC-UA
T4↑ cross-correlated with ΔP dynamic pressure. 87% confidence. Detected 3h 14m ago.
~23 days to fault CI: 18–31 days WO-2026-0418 · PLANNED Create WO →
EGT · GT-07 °C 614°C ↑ +14°C
THRESHOLD
−24h−18h−12h−6hnow
847 HRS REMAINING
AssetGT-07 Frame 7
Confidence87%
ActionInspect in 23d
Active Anomalies 4 FOUND
HIGH
GT-07
Combustion instability
87%
MED
GT-01
HPC fouling stage 5
91%
LOW
GT-04
Bearing vib. slight uptick
72%
INFO
GT-02
LPT efficiency −0.4%
68%
HIGH Combustion Instability Precursor
GT-07 · Frame 7 · Plant Alpha
Root causeT4 temp rising above trend. Cross-correlated with ΔP oscillation at 200 Hz band.
Confidence87% — High
Predicted fault~23 days (CI: 18–31d)
RecommendedBorescope inspection · hot section
Create WO → Snooze 24h
Fleet Health Score Last 30 days
GT-04
96%
GT-02
93%
GT-07
71%
GT-01
44%
94.1%
Fleet Avg Efficiency
↑ 1.3%
vs Last Month
$1.4M
Savings YTD
2
Anomalies Active
EGT Fleet Trend · 7-Day GT-07 ↑
Upcoming Maintenance 5 EVENTS
AssetWork TypeScheduledStatusWO #
GT-07 Borescope · Hot section Jun 4, 2026 Action Required Create →
GT-01 Offline compressor wash Jun 8, 2026 Scheduled WO-1847
GT-01 Major overhaul (C-inspection) Jun 12, 2026 Planned WO-1821
GT-04 Annual borescope inspection Aug 19, 2026 Planned WO-1903
GT-02 Fuel nozzle inspection Sep 3, 2026 Planned WO-1911
9:41
ANOMALY ALERT now
Critical: GT-07 Combustion
EGT +14°C above trend. 87% confidence. Estimated 23 days to fault.
GT-07 Remaining Useful Life
847 HOURS REMAINING
23dPredicted Fault
87%Confidence
4Fleet OK
MAINTENANCE 2h ago
GT-01 Wash Recommended
Stage 5 efficiency −2.1%. Schedule offline wash within 8 days.
Why Now

AI Created the Demand.
AI Keeps It Running.

Data centers will draw over 1,000 TWh a year — Japan's entire consumption — and US data center power demand is projected to grow 160% by 2030. Gas turbines are the only dispatchable generation fast enough to follow that load. They can no longer afford to fail.

AI infrastructure needs electricity
↓
Gas turbines supply it — and must never fail
↓
Turbnetic.ai keeps them running
↓
Reliable power enables more AI
Get Started

Your First Anomaly Alert
in Under 4 Hours

Connect to your PI historian or SCADA system, import your tag list, and receive your first scored anomaly alert — without writing a single line of code.

No commitment. Live demo on your actual plant data within 24 hours.