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ENERGY

Asset state, understood
well enough to run the
installed base differently.

Understanding asset state precisely, and when that understanding holds, can support availability, usable performance and supervision across an installed base. Maintenance is one use, not the whole proposition.

THE EXISTING CONTROL SYSTEM REMAINS IN CONTROL

01 ASSET STATE

The asset is not one operating point.
Its state evolves continuously.

Rotating and energy assets accumulate history: load, temperature, vibration, operating cycles, environment and degradation history.

Xpdeep can read the learned structural dependencies that carry the model output across these temporal signals.

ILLUSTRATIVE ASSET VARIABLES

  1. MULTIVARIATE ASSET SIGNALS
  2. STRUCTURAL READING
  3. CURRENT STATE ESTIMATE

An explained estimate and attributed domain state give the customer’s system richer inputs for deciding when to act, hold or escalate.

02 DOMAIN

A model should know when the asset state has moved beyond what it learned.

A rare but valid operating condition can remain inside domain. A degraded sensor or upstream error can appear as a departure attributed to the affected input.

TECHNICAL BOUNDARY: Naming the physical cause remains the engineer’s call.

03 INTERVENTION

Prediction tells you what may happen.
Xpdeep computes what can change now.

PRIMARY INFERENCE

ASSET STATE

PREDICTSTRUCTURAL EXPLANATIONCHECK DOMAIN

WHEN AN INTERVENTION IS USEFUL

PRESCRIBEJUSTIFY

SYSTEM RULE

ACTHOLDESCALATE

Prediction and structural explanation share one forward pass. Prescription is a separate inference, invoked when useful on temporal and tabular stages.

Xpdeep computes a minimal feasible input change under stated operational constraints and returns the structural justification of why that intervention changes the predicted outcome.

IMPORTANT BOUNDARY

Structural, not automatically causal.

Minimal means minimal feasible under the specified constraints and optimization procedure, not a unique global optimum.

AVAILABILITY

Not every deviation should become an unnecessary human intervention.

ROUTINE

  1. ROUTINE STATES
  2. MODEL HANDLES INSIDE CONDITIONS

EXCEPTION

  1. TRUE EXCEPTION
  2. HOLD / ESCALATE
  3. EXPERT

When the model can state its domain and decision context, expert attention can move toward the cases that genuinely require review.

POTENTIAL OPERATIONAL CONSEQUENCES

  • Fewer unnecessary escalations
  • Faster diagnosis
  • More assets per expert (expert-to-asset ratio)
  • More continuous operation where conditions permit
  • Improved remote triage

04 OPERATING HEADROOM

Recover performance currently left unused because the system does not know enough about its own state.

THIS IS A TRANSFORMATION PATTERN, NOT A PRODUCT.

ASSET OPERATING ENVELOPE

  1. STATIC OPERATING LIMIT
  2. CURRENT ASSET STATE + HISTORY + DEGRADATION + DOMAIN STATE
  3. CONDITIONAL OPERATING REGION
  4. ACT / HOLD / ESCALATE
  5. STATIC BASELINE PRESERVED

POTENTIAL SURFACES

  • Usable capacity
  • Power
  • Efficiency
  • Operating interval
  • Availability

Application opportunity: establish usable operating headroom within approved limits and measure its economics. Approved hard limits, the static baseline and customer fallback are preserved; domain state does not authorize expanding operating limits.

05 SERVICE ECONOMICS · FOR EQUIPMENT OEMs

The installed base can support more than maintenance.

For equipment manufacturers, more precisely characterized asset state can support new product and service offers. For operators, the opportunity is availability and operating performance, covered above.

POTENTIAL VALUE SURFACES

  • PERFORMANCE SERVICES

    Potential premium performance tiers

  • AFTERMARKET

    More software and service value from installed assets

  • DIFFERENTIATED EQUIPMENT

    Explained asset-state intelligence as part of the product

Establish the application’s performance and economics against agreed KPIs.

EVOLUTION

When the asset population changes, correct what changed.
Keep the rest fixed.

  1. FIELD EVIDENCE
  2. STRUCTURAL DIAGNOSIS
  3. TARGETED LOCAL CORRECTION (NATIVE)
  4. VALIDATION
  5. REDEPLOYMENT

REST OF MODEL FROZEN

Controlled evolution is a validated change between operational periods, not continuous learning.

POTENTIAL VALUE

  • Faster diagnosis
  • Targeted correction
  • Preserved model regions
  • Controlled correction of an existing model

Native mode: targeted correction of an existing model, validated and applied between operating periods. Reducing the cost of adaptation across asset variants is an application-specific hypothesis; transfer between assets or variants is not automatic and requires validation.

What is live and measured, across sectors.

  • PLATFORM

    Core training, structural explanation, optimization and prescriptive engines are live.

  • TEMPORAL EDGE

    Xpdeep’s deepest scientific edge is multivariate time series and digitized signals. On time-series data, Xpdeep models achieve accuracy at least equivalent to standard deep learning and frequently superior.

  • VALIDATED RESULTS

    Selected validated results across Confiance.AI and client programmes include:

    • 105 → 19 input variables (up to 82% fewer at equal or better accuracy);
    • up to 4× faster model development;

    Independent programme evaluation: up to 35× faster inference; models up to 50% smaller.

    CROSS-SECTOR MODEL-DEVELOPMENT AND INFERENCE RESULTS

INTEGRATION

Xpdeep changes the model.
Not your asset-management stack.

  1. SENSORS / ACQUISITION
  2. DIGITIZED ASSET DATA
  3. XPDEEP MODEL
  4. CONTROLLER / SCADA / APPLICATION / SERVICE SYSTEM

Condition-monitoring and asset-performance-management systems remain in place. Xpdeep adds structural explanation of learned estimates, attributed domain state and, where useful, explained constrained interventions.

NO PROPRIETARY TRANSPORT OR LOGGING FRAMEWORK IMPOSED.

Energy engineering boundaries.

EXPAND

  • SCADA, controllers, CMMS and asset-management systems remain in place.
  • Structural integration requires read access to the PyTorch architecture and weights, or a Native model.
  • Customer hard limits remain unchanged.
  • The customer’s surrounding system defines and authorizes actions.
  • Attributed domain state covers the retained model inputs; alone, it does not authorize action or establish system safety.
  • Structural justification does not automatically establish physical causality.
  • Minimal means minimal feasible under the stated constraints and optimization procedure.
  • Targeted editing and controlled correction require Native mode.
  • System validation and certification remain with the customer and relevant authority.
  • Reduced requalification scope requires application-specific evidence and acceptance.

START WITH ONE ASSET DECISION

Start with an operating decision you already measure.

  • ASSET STATE

    A state estimate tied to performance or availability

  • VIBRATION / THERMAL

    A temporal model tied to rotating-equipment condition

  • OPERATING ADJUSTMENT

    A bounded decision about what to change and when

  • REMOTE TRIAGE

    An expert decision repeatedly triggered by machine-state uncertainty

  • OPERATING HEADROOM

    A system held inside conservative limits because current state is not sufficiently characterized

Run an Xpdeep model alongside your existing model for an agreed number of weeks. Compare performance on the same data against agreed KPIs, using your own validation chain.

An Xpdeep model is a PyTorch model with a different architecture. Customers can validate it using their own tools and test processes.

  1. 01 →

    One funded decision

    A model or decision loop the business already owns.

  2. 02 →

    Agreed comparison

    Data, duration and KPIs defined at scoping.

  3. 03 →

    Evidence on your application

    Side-by-side results using the customer’s validation process.

  4. 04

    Define the extension

    Identify the possible Phase 2 scope at the start; use Phase 1 evidence to determine its scope.

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