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
- MULTIVARIATE ASSET SIGNALS
- STRUCTURAL READING
- CURRENT STATE ESTIMATE
An explained asset-state estimate supports model diagnosis and better-founded maintenance decisions: engineers see which signals and time windows carry it.
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. Domain awareness alone does not authorize operation outside approved limits.
03 EXPERT SUPERVISION
Not every deviation should become an unnecessary human intervention.
ROUTINE
- ROUTINE STATES
- MODEL HANDLES INSIDE CONDITIONS
EXCEPTION
- TRUE EXCEPTION
- HOLD / ESCALATE
- 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 · HYPOTHESIS
Recover performance currently left unused because the system does not know enough about its own state.
A HYPOTHESIS TO ESTABLISH, NOT A SHIPPING XPDEEP PRODUCT.
ASSET OPERATING ENVELOPE
- STATIC OPERATING LIMIT
- CURRENT ASSET STATE + HISTORY + DEGRADATION + DOMAIN STATE
- CONDITIONAL OPERATING REGION
- ACT / HOLD / ESCALATE
- 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.
CONSTRAINED INTERVENTION · WHERE USEFUL
Where useful, a separate inference
proposes a change within constraints.
On supported time-series models, Xpdeep can compute a minimal feasible input change under stated operational constraints, with its structural justification. The existing control architecture decides whether it is applied.
IMPORTANT BOUNDARY
Structural, not automatically causal.
Minimal means minimal feasible under the specified constraints and optimization procedure, not a unique global optimum.
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
EVOLUTION
When the asset population changes, correct what changed.
Keep the rest fixed.
- FIELD EVIDENCE
- STRUCTURAL DIAGNOSIS
- TARGETED LOCAL CORRECTION (NATIVE)
- VALIDATION
- 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.
DATA · VALIDATION · EVIDENCE
What the asset owner measures, and where the evidence stands.
RELEVANT DATA AND MODEL TYPES
- Load, temperature, vibration, speed and pressure time series
- Weather, grid and operating context
- Service and maintenance history
- Drone or thermal inspection imagery where a condition model uses it
WHAT THE CUSTOMER MEASURES OR VALIDATES
- Estimate accuracy and domain coverage across its asset population
- Maintenance and service decisions against its own baseline
- Any rating, interval or warranty change, through its engineering and grid-code processes
- Behaviour of the existing control architecture and deterministic fallback
PUBLISHABLE EVIDENCE AND MATURITY
- AVAILABLE
- The underlying model-level capabilities: structural explanation, signal localization and attributed domain awareness on temporal data.
- HYPOTHESES
- Recovered operating headroom, state-dependent operating envelopes, asset-life extension, differentiated service economics, increased availability or usable power.
- NOT CLAIMED
- Delivered Energy-sector operating gains, or a condition-aware operating envelope as a shipping product.
Cross-sector model results, not measured Energy-sector outcomes.
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
Independent programme evaluation:
- 105 → 19 input variables (up to 82% fewer at equal or better accuracy);
- up to 4× faster model development.
Selected validated results across Confiance.AI and client programmes: 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.
- SENSORS / ACQUISITION
- DIGITIZED ASSET DATA
- XPDEEP MODEL
- 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-approved hard limits, the existing control architecture and deterministic fallback remain unchanged.
- The asset owner, equipment manufacturer or authorized operator validates any change.
- Domain awareness alone does not authorize operation outside approved limits.
- 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 FUNDED ASSET PROBLEM
Start with an asset problem you already measure.
ASSET-STATE ESTIMATION
A state estimate tied to performance, availability or degradation
MODEL DIAGNOSIS
An existing condition model that engineering cannot yet explain or trust across conditions
PERFORMANCE CHARACTERIZATION
An asset held inside conservative limits because its current state is not sufficiently characterized
MAINTENANCE
A maintenance or inspection decision driven by machine-state uncertainty
EQUIPMENT DESIGN
An OEM design or service offer that depends on better-characterized asset state
An immediate operational-control intervention is not required.
Phase 1 is a funded engagement, typically three to six months, on a consequential model, decision, function or programme you have prioritized. Agree the economic, operational, quality, mission, product or assurance success criterion at scoping; work on your data within your engineering environment and constraints.
Xpdeep supplies model-level technology and repeatable software deployment, not a replacement engineering lifecycle. Your teams and implementation partners can integrate and validate the model using your own tools and test processes.
01 →
One funded problem
A model, engineering problem, product function, subsystem or decision the business already owns.
02 →
Agreed criterion
Data, duration, success criterion and validation method defined at scoping.
03 →
Evidence on your application
Results assessed through the customer’s validation process; a side-by-side model comparison is one possible method.
04
Define the extension
Identify the possible Phase 2 scope at the start; use Phase 1 evidence to determine its scope.