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Thousands of alarms. One cause. Find it before it spreads.

Causal Intelligence for Complex Engineering Systems

Rhizant builds a causal digital twin of your engineered system from the sensor data and engineering knowledge you already have. It traces faults to their root, predicts what a change will do, and tells you how sure it is.

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Illustrative demo

Root cause in one view.

A diesel engine on a test stand. An alarm flood begins: twelve alarms in two and a half minutes. The causal twin traces them back to one cause.

Diesel engine on a test stand Illustrative example with synthetic data

What the control room sees

120 alarms
  1. 0:00 Coolant level low first alarm cleared
  2. 0:20 Coolant pressure low consequence cleared
  3. 0:45 Coolant temperature high consequence cleared
  4. 1:05 Cylinder head temperature high consequence cleared
  5. 1:10 Oil temperature high consequence cleared
  6. 1:20 Charge air temperature high consequence cleared
  7. 1:30 Exhaust temperature high consequence cleared
  8. 1:40 Oil pressure low consequence cleared
  9. 1:50 NOx high consequence cleared
  10. 2:05 Fuel consumption high consequence cleared
  11. 2:15 Engine derate active consequence cleared
  12. 2:30 Power output low consequence cleared

Root cause, inferredCoolant leak. It has no sensor of its own, so it raised no alarm.

What the twin sees

MonitoringAlarm floodTracing backRoot cause: coolant leakWhat-if: all alarms clear

Engine load normal ruled out Head gasket not measured ruled out Coolant hose not measured leak: root cause repaired Ambient temp normal ruled out Coolant level normal alarm first alarm clear Coolant pressure normal alarm consequence clear Coolant temp normal alarm consequence clear Cyl. head temp normal alarm consequence clear Oil temp normal alarm consequence clear Charge air temp normal alarm consequence clear Exhaust temp normal alarm consequence clear Oil pressure normal alarm consequence clear NOx normal alarm consequence clear Fuel use normal alarm consequence clear Engine derate normal alarm consequence clear Power output normal alarm consequence clear Root cause Confidence: high Leak repaired All alarms clear Head gasket not measured ruled out Coolant hose not measured leak: root cause repaired confidence: high all alarms clear Engine load normal ruled out Ambient temp normal ruled out Coolant level normal alarm first alarm clear Coolant pressure normal alarm consequence clear Coolant temp normal alarm consequence clear Cyl. head temp normal alarm consequence clear Oil temp normal alarm consequence clear Charge air temp normal alarm consequence clear Exhaust temp normal alarm consequence clear Oil pressure normal alarm consequence clear NOx normal alarm consequence clear Fuel use normal alarm consequence clear Engine derate normal alarm consequence clear Power output normal alarm consequence clear
  1. 1

    Map Place the alarms on the causal graph: all twelve sit on one flow.

  2. 2

    Trace back Walk back from exhaust temperature to the first alarm, coolant level.

  3. 3

    Test causes Test the inputs upstream. Only a coolant leak explains all twelve alarms; the other three are ruled out.

With the leak repaired, the twin predicts that all twelve alarms clear.

Ask the twin

Engineer

Why did exhaust temperature spike?

Rhizant

Exhaust temperature rose because coolant temperature rose, because coolant level dropped. The most likely root cause is a coolant leak.

  1. Coolant leak
  2. Coolant level low
  3. Coolant temp high
  4. Exhaust temp high

All twelve alarms are consequences of it.

Recommended action: inspect the coolant circuit before changing anything else.

Suggested follow-up Is the exhaust temperature sensor itself trustworthy?

How it works

Sensor data and manuals in. A twin you can ask why out.

01

Connect

Stream sensor and operating-condition data from historians, SCADA, telematics, or an existing digital twin. Add manuals, schematics, and maintenance logs. Nothing has to be centralized first.

02

Model

Rhizant drafts a causal graph of your system from its documents, confirms and corrects it against your time-series data, and builds in the physics you already know. Every link carries a confidence score and its evidence.

03

Act

Ask why a fault happened, what will happen if you change a setting, and which action to take. Review it yourself, or let the twin intervene in closed loop.

Capabilities
WhyWhy did this fault happen?
What ifWhat will happen if we change this setting?
What to doWhich action should we take?
Human in the loopClosed loop

Industries

Where this applies

For engineers and operations teams responsible for complex, sensor-instrumented systems, starting with the people who untangle faults and alarms every day: reliability engineers, controls engineers, plant operators, maintenance managers, and field-quality and warranty engineers.

01

Engines and powertrainsDiesel engines, heavy-duty equipment, OEM fleets
The pain
Field failures across thousands of units. Emissions and fuel targets. Warranty costs.
What the twin does
Root cause analysis, emissions and fuel-efficiency interventions, warranty and field-failure analysis, remaining useful life.
Ask it
“Which design or usage factors are causing these field failures?”
02

Power generation and gridPower plants and grids
The pain
Alarm floods in the control room. Cascading failures. Safety margins.
What the twin does
Alarm flood suppression, fault propagation forecasting, operational safety.
Ask it
“A line tripped. What fails next, and how soon?”
03

Process and semiconductor manufacturingSemiconductor and chemical manufacturing
The pain
Faulty product, and nobody knows which of hundreds of settings is to blame. Sensors that cry wolf.
What the twin does
Process optimization, sensor fault vs machine fault, sensor placement.
Ask it
“Yield dropped. Which setting is actually to blame?”
04

Wind, rotating equipment, and fleets across sitesWind farms, other rotating equipment, vehicle fleets
The pain
Models that break when moved to a new site or season. Maintenance that predicts but does not explain.
What the twin does
Robustness across sites, prescriptive maintenance, learning from past decisions.
Ask it
“This turbine will fail. Why, and what exactly do we fix?”

See why it failed. Decide what to change. Before you touch the machine.

A 30-minute conversation with the founder about your system, your data, and whether a causal twin fits.

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Looking for design partners

  • A painful problem

    A fault, alarm, or reliability problem your team needs explained.

  • The sensor data to go with it

    Existing sensor data and documents. No new experiments needed to start.

  • Book a call

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  • Email

    [PLACEHOLDER: contact email]

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