Your turbine is whispering before it screams. A 0.3 mil rise in shaft vibration over 14 days. A 4°C creep in bearing temperature that the DCS alarm philosophy lets pass. A governor valve hunting by 2% more than last month. A generator winding running 6°C hotter under the same load. None of these crosses a single trip threshold. Each one, on its own, gets ignored. Together — they are the signature of a forced outage in 21 days. The Turbine and Generator Health Agent reads all five PI tags simultaneously, fits them against a learned envelope of healthy operation, and opens an inspection ticket the moment the trend escapes the model — with 94% confidence and a recommended action your maintenance head can act on immediately. Register for the event to watch the agent flag a real bearing fault on live PI data.
The On-Prem Server Stack That Runs the Health Agent
Shaft vibration sampled at 25.6 kHz from a 600 MW turbine generates 100+ MB per machine per day. Bearing temperature, valve position, current, and winding temperatures add another dense stream. Cloud-based AI cannot keep up — and it cannot live inside your NERC CIP boundary. Three NVIDIA servers form the complete stack: a Jetson AGX Orin edge gateway at the turbine deck for vibration ingestion, an RTX PRO 6000 Blackwell server in the control room for the Health Agent inference, and a DGX Station GB300 Ultra for fleet-wide rollout across plants. All three will be running on stage at the event. Register for the event to see all three servers running live.
CONTROL ROOM · CENTRAL AI BRAIN
RTX PRO 6000 Blackwell Workstation Edition
GPURTX PRO 6000 Blackwell · 96GB VRAM
CPUAMD Ryzen 7 9900X · 12-core
RAM128GB DDR5 6000MHz
Storage2TB NVMe M.2 SSD
PreloadedNVIDIA Omniverse · Health Agent runtime
Best for: Health Agent inference · multi-tag fusion model · digital twin · CMMS integration · sub-10ms response
EDGE GATEWAY · AT THE TURBINE
NVIDIA Jetson AGX Orin Edge AI · PLC Gateway
GPU2048-core Ampere · 2× DLA accelerators
CPU12-core ARM Cortex-A78AE
RAM64GB unified LPDDR5
ProtocolOPC-UA · EtherNet/IP · Modbus TCP
FormIndustrial enclosure · DIN-rail mount
Best for: Vibration sensor ingestion at the turbine deck · 25.6 kHz sampling · <10ms PLC tag sync · feeds the central server
ENTERPRISE FLEET · 25+ TURBINES
NVIDIA DGX Station GB300 Ultra · Enterprise
GPUGrace Blackwell GB300 Ultra superchip
RAM768GB unified memory
Network400GbE · multi-plant federation
Storage30TB NVMe + cold archive
FormRack-mounted · 24/7 production
Best for: 25+ turbine generators across regions · fleet-wide LLM analytics · simulation · cross-plant correlation
100%
On-prem · behind your firewall · NERC CIP friendly
$0/mo
Perpetual license · no recurring fees ever
Air-Gap
Optional · zero internet egress if required
Source
Code & modification rights included
The Five PI Tags This Agent Watches — And What Each One Tells It
The Health Agent does not look at any single tag in isolation. It fuses five live PI streams against a learned envelope of healthy operation specific to your unit. When the multi-sensor pattern leaves the envelope — even if no individual tag has crossed a DCS alarm — the agent opens an inspection ticket. Register for the event to map your tag namespace to the agent live.
Why Multi-Tag Fusion Beats Single-Tag Alarms
A bearing temperature rise alone is ambiguous — could be ambient, could be load, could be lube oil cooler fouling. Combined with a rising vibration harmonic at the same bearing, it is conclusive. Cross-correlated with a governor valve hunt that is loading the rotor unevenly, the agent knows exactly which bearing, exactly what failure mode, and exactly how many days remain. False alarm rate stays under 2% — the signal-to-noise ratio your control room engineers will actually trust.
Three Real Plant Problems — How the Hardware Stack Solves Each One
Three scenarios that happen at every thermal plant. Each one walks through the problem in plain language, then shows exactly how the Jetson edge box, RTX brain, and DGX fleet server work together to solve it. Register for the event to watch all three running on real plant data.
CASE 01
A turbine bearing fails at 3 AM. The plant trips. Nobody saw it coming.
THE PROBLEM
The bearing was getting hotter for two weeks. The shaft was vibrating a little more every day. The governor valve was hunting just slightly. None of those numbers crossed an alarm by themselves — so the control room saw nothing. At 3 AM, the bearing seizes. The unit trips. You lose 18 hours of generation, fly in an emergency repair team, and explain to corporate why a $1.4M outage just happened.
HOW THE HARDWARE SOLVES IT
Jetson AGX Orin
Sits at the turbine. Reads vibration, bearing temperature, and valve position 25,600 times per second. Spots tiny patterns the DCS would never see.
RTX PRO 6000 Brain
Takes all five tag streams from the Jetson. Compares them against 17 historical bearing failures. Says with 94% confidence: "Bearing #4, misalignment plus lube starvation, fails in 18-24 days."
CMMS Work Order
Auto-opens a maintenance ticket. Assigns the right tech. Stages the replacement bearing. Schedules the fix during the next planned weekend outage.
THE RESULT
Bearing replaced on schedule during a planned outage. Standard delivery, normal labor cost, no 3 AM phone calls. The forced outage that would have cost $1.4M simply does not happen. One save pays for the entire hardware stack.
CASE 02
The generator runs 6°C hotter under the same load. Nobody knows why.
THE PROBLEM
Your generator winding is running 6°C hotter than it did six months ago — at the same load, same ambient temperature. That extra heat is silently cutting the insulation life in half. Some day in the next 18 weeks, a winding fault will trip the generator and you will be looking at a $3M+ rewind job. Right now, no alarm is firing. Nothing on the DCS tells you this is coming.
HOW THE HARDWARE SOLVES IT
Jetson AGX Orin
Reads winding temperature plus stator current 24/7 and tags the load and ambient at every reading — so the brain knows what "normal" should be at any given moment.
RTX PRO 6000 Brain
Looks at every reading versus what your generator did last year at the same conditions. Notices the +6°C drift. Cross-checks the current waveform — finds a phase asymmetry signature that points to early insulation aging.
Operator Alert
Sends the maintenance head a clear ticket: "Generator cooling air filter likely fouled. Schedule inspection. If clean, escalate to partial discharge testing." 12 weeks of warning.
THE RESULT
Cooling filter replaced in a 4-hour scheduled stop. Winding temperature drops back to baseline. Insulation life is preserved. The $3M generator rewind that was building up gets pushed out by years. The agent caught it 12 weeks early — your team had time to plan.
CASE 03
You run 8 turbines across 3 plants. Each one fails differently. Nobody connects the dots.
THE PROBLEM
Plant A had a bearing failure last March. Plant B had a similar one in August. Plant C just had a near-miss last week. The same root cause — a specific lube oil supplier change — affected all three. But each plant runs its own systems, talks to corporate separately, and nobody at HQ has the data to spot the pattern. So the fourth turbine fails in October.
HOW THE HARDWARE SOLVES IT
Jetson + RTX (×3 plants)
Each plant runs its own Jetson at the turbine and RTX brain in the control room — staying behind that plant's firewall. Local detection, local tickets, local data.
DGX Station GB300
Sits at corporate HQ. Receives anonymized fault patterns from every plant — never raw operating data. Sees Plant A's March failure looks identical to Plant B's August one. Flags the lube oil supplier change as the common factor.
Fleet Alert
Pushes a fleet-wide warning: "Same fault signature on 3 turbines. Likely lube oil contamination. Inspect bearing #4 lube circuit on all units within 30 days." Plant C, D, E all act before they fail.
THE RESULT
A pattern that took your fleet 18 months to spot manually — the DGX caught in 6 hours. Three forced outages prevented across the fleet. Lube oil supplier swapped. The fleet learns from every plant. Every plant gets smarter.
~$5M+
Combined savings across the three cases on a typical multi-plant fleet — against a one-time hardware cost and zero monthly fees. The hardware pays for itself on the first save. The other two are pure return.
Why This Matters — One Save Pays for the Stack
The economics on a turbine save are not subtle. A single avoided forced outage typically pays for the full hardware-plus-software deployment with margin to spare. The numbers below are conservative averages from operators running predictive vibration AI for 12+ months.
$500K+
Average cost of a single unplanned turbine outage — lost generation, emergency labor, replacement power
94%
Average confidence on Health Agent fault classifications. Operators trust it. They act on it.
72hr+
Earlier failure detection vs DCS alarm philosophy. Often weeks earlier on slow-degradation modes.
<2%
False alarm rate from multi-tag fusion. The signal-to-noise ratio your team will actually trust.
60%
Of developing turbine failure modes caught by vibration plus bearing temperature alone.
6-12wk
Pilot to fully running. No twelve-month consulting engagement. Pre-installed on the server.
May 12 · 5:30 PM EST · Orlando · Hands-On
The Health Agent Will Be Running. Bring Your PI Tag List.
Walk in, hand us your VIBRATION, BEARING_TEMP, GOV_VALVE, CURRENT, and WINDING_TEMP tag names. Watch the agent baseline against your historical data, surface the events your DCS missed, and generate the inspection tickets you wish you had three years ago. Walk out with a quote and an order date. Pilot to fully running in 6 to 12 weeks. You buy it once and own it forever — no monthly fees, ever.
What You Get — One Server, Full Stack, Source Code Included
A pre-configured RTX PRO 6000 Blackwell server arrives at your control room with the Health Agent runtime, the PI Historian connectors, the trained turbine fleet models, and full source code. Plug it into your OT network, point it at your tag namespace, go live.
Perpetual License
No monthly fees, no per-tag charges, no per-asset billing. Ever.
Data Sovereignty
Vibration streams, model weights, audit trails — all behind your firewall. NERC CIP friendly.
Source Access
Source code and modification rights. Build unit-specific fault signatures in-house.
AI-Native Core
Health Agent, NLP work orders, multi-asset fleet view — pre-loaded and ready.
Frequently Asked Questions
How fast can the Health Agent be running on our turbine?
From signed order to live tickets is typically 14 to 22 weeks. The RTX PRO 6000 Blackwell server arrives in 4 to 6 weeks pre-configured. Once on-site, our team baselines the agent on your historical PI data, validates it by backtesting against past forced events, and goes live in 6 to 12 weeks. There is no months-long consulting engagement — the agent ships pre-trained on a fleet of comparable turbines and only needs unit-specific fine-tuning.
Will the agent work with our PI Historian tag namespace?
Yes. The agent reads PI Historian directly via PI Web API and OPC-UA. Tag names like TPLANT.TURB01.VIBRATION_X, BEARING_TEMP, GOV_VALVE_POS, GEN01.CURRENT_A, and WINDING_TEMP are mapped during week 2 of deployment. We have connected to PI servers paired with Emerson Ovation, GE Mark VI/VIe, Siemens SPPA-T3000, ABB Symphony Plus, and Yokogawa Centum DCS systems. The agent does not write back to your DCS — recommendations flow into the integrated CMMS where your maintenance team acts on them.
What does the 94% confidence number actually mean?
It is the model's certainty that the multi-tag pattern matches a known failure signature in the trained library. The agent calculates this by comparing the current envelope deviation against thousands of historical fault events from comparable turbines plus your own backtested events. A 94% confidence score is paired with the specific failure mode predicted, the affected component, the predicted failure window in days, and a recommended action. Your team sees all four pieces of context before deciding to action the ticket.
Does the agent need new sensors, or does it work with what we have?
It works with what you have. The five PI tags listed are already on every modern turbine generator — vibration probes, RTDs on bearings and windings, valve position feedback, and CTs on the stator. The agent reads existing instrumentation through the historian. If your unit lacks one of the five inputs, the agent runs in degraded mode on the remaining tags with reduced confidence, and we can flag candidate sensor additions during the baseline phase.
What does "you own it" really mean? What costs come later?
It means exactly that. You pay one price up front for the hardware, software, source code, and modification rights. There are no monthly fees, no per-tag charges, no per-asset billing, no annual escalator. The only optional future costs are entirely your choice: support contracts if you want our help with model retraining, custom feature work for unit-specific needs, and hardware refresh whenever you decide. You can run the system forever without paying us anything more.
May 12 · 5:30 PM EST · Hands-On Hardware
Lock Your Spot. Watch the 94% Confidence Agent Catch a Real Fault.
Walk into a working Turbine and Generator Health Agent deployment. Watch it ingest live PI tags, fit them to the model envelope, and surface the inspection ticket with confidence score and recommended action. Ask any question to the engineers who built it. Leave with a quote and an order date. Pilot to fully running in 6 to 12 weeks. Buy it once, own it forever — no monthly fees, ever.
