REDUNDANT DEMO - Turbine sensor redundancy
STEP 1 - Raw evidence
Five redundant turbine sensors measuring the same operating signal are loaded automatically. Sensor 3 drifts progressively and Sensor 5 presents intermittent outliers. The active signals are sent to the server-side redundant fusion service.
Click a sensor button or a chart legend label to include/exclude that signal from the fusion.
Ready to call the server-side fusion service.
STEP 2 - SITANO Fusion
The fusion estimate is attached to each block center, then reconstructed on the same time axis as the raw signals.
Timeline uses causal rolling normalization: past and present only.
STEP 3 - Maintenance intelligence
STEP 4 - Evidence
The downloadable report summarizes raw disagreement, fusion confidence and maintenance recommendations.
HETEROGENEOUS DEMO - Industrial maintenance evidence
STEP 1 - Raw evidence
Primary field signals are loaded automatically: accelerometer, motor current, bearing temperature, gyroscope, hydraulic pressure and acoustic level. Maintenance features are derived server-side before fusion.
Click a modality to include or exclude it from heterogeneous fusion.
Ready to call the server-side heterogeneous fusion service.
HETEROGENEOUS DEMO - Industrial maintenance evidence
STEP 2 - SITANO Fusion
The engine combines dissimilar evidence and emits a degradation score, conflict indicators and an operational maintenance action.
HETEROGENEOUS DEMO - Industrial maintenance evidence
STEP 3 - Maintenance intelligence
HETEROGENEOUS DEMO - Industrial maintenance evidence
STEP 4 - Evidence
The evidence export includes active primary modalities, hidden feature-extraction context, heterogeneous fusion state and maintenance interpretation.
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This optional feedback helps SITANO qualify relevant use cases and improve the public sandbox. Do not include confidential industrial data.