AI Virtual Sensor · Soft Sensing & Process Estimation

Measure what your process can't.

When a critical quality or operating variable arrives late from a lab, needs an expensive analyzer, or cannot be measured directly, IndX AI Virtual Sensor estimates it continuously from the industrial signals you already collect, so operators can act while the process is still running.

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1 variablebest first deployment scope

Real timeestimate between lab readings

Existing datahistorian, PLC, DCS, MES, lab

From $75Kone-variable deployment

Temperature, pressure, flow and vibration signals feeding an IndX AI Virtual Sensor model that outputs an estimated product viscosity as a continuous magenta signal with a confidence band and periodic lab reference points.

The measurement gap

Your plant isn't blind. It's delayed.

Most plants already collect thousands of signals. The problem is that the variable that matters most is often the one operators cannot see at the moment a decision needs to be made.

The lab result arrives too late

By the time the composition, moisture or quality number comes back, the process has already drifted. The decision window closed hours ago.

The analyzer is expensive or impractical

An online analyzer or physical sensor can be costly to buy, hard to install, maintenance-heavy, or simply unavailable for the variable you need.

Uncertainty is paid for in safety margins

Operators compensate for what they cannot see: conservative setpoints, over-processing, over-dosing, excess energy, slower rates or product given away.

Dashboards cannot show what does not exist

You cannot chart a variable you never measure. The one number that would change the decision is missing from every screen on the floor.

How it works

Turn the signals you already have into the measurement you need.

A virtual sensor learns the relationship between the signals you already collect and a target variable you can only measure occasionally, then estimates that target continuously between reference readings.

Choose the variable worth knowing now

Pick one high-value target where an earlier answer changes a decision: a quality property, a composition, a condition or a proxy for it.

Connect the evidence

Historian, PLC, DCS, MES, lab, LIMS, analyzer and physical sensor signals. We work with the data infrastructure you already run.

Train and validate against ground truth

Model the operating modes and time lags, then validate the estimate against real reference measurements before anyone relies on it.

Deploy the estimate where it acts

Push the online variable into the HMI, edge, historian, MES or the operational workflow where a person or a controller uses it.

Monitor drift and retrain

Watch the model against incoming ground truth, flag drift, and retrain as the process, feedstock or equipment changes over time.

Technology architecture

Existing signalshistorian, PLC, DCS, MES

Ground truthlab, LIMS, analyzer

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IndX AI Virtual Sensorprocess-aware model, validated

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Online estimated variablecontinuous, between references

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Execution layerHMI, edge, MES, control, optimization

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Model monitoringdrift detection and retraining

Where it pays

Use AI where the measurement gap has a financial consequence.

A virtual sensor earns its place in four situations. Each one turns a delayed, expensive or missing measurement into something operators and controllers can act on now.

Real-time quality

Estimate a quality property continuously instead of waiting for the batch, reel, lot or shift to finish and the lab to report.

"Estimate product viscosity between lab samples so operators correct drift within minutes, not hours."

Analyzer augmentation and backup

Provide a validated estimate when an online analyzer is down for maintenance, or reduce dependence on an expensive instrument.

"Keep a trusted composition estimate running while the analyzer is offline for service."

Hidden process condition

Infer a condition you cannot measure directly, from thermal state to equipment condition, using the signals that do correlate with it.

"Estimate a thermal or fineness property that has no practical direct sensor on the line."

Optimization and control input

Feed the estimated variable into advanced control, optimization or a digital twin, so the value drives action, not just a display.

"Use the estimate as a controlled variable so the loop holds spec instead of chasing it."

Example target variables

ViscosityMoistureCompositionConcentrationFineness / particle propertiesProduct strengthQuality characteristicsEmissionsThermal conditionVibration / equipment stateFlow / pressure proxies

Not every variable is a fit. Suitability depends on whether reliable relationships exist in the data and whether ground truth is available to validate against.

The business case

Every minute you wait for the measurement has a price.

The value of a virtual sensor is not the model. It is the decision it lets you make earlier: less off-spec product, less giveaway, fewer conservative margins, and instrumentation cost you can defer or avoid.

Illustrative annual value = off-spec and giveaway reduced + analyzer and lab cost avoided + downtime or lost-output exposure reduced, against a $75K starting investment.

Less off-spec and giveaway

Seeing the variable in real time lets operators hold closer to target instead of over-processing to stay safe.

Deferred instrumentation cost

A validated estimate can back up or reduce dependence on an expensive analyzer, and lighten the lab sampling burden.

Throughput and availability

Where the estimate supports a condition or a control decision, it can protect output that uncertainty was quietly costing you.

Illustrative ROI calculator

Enter rough annual figures. Defaults are examples, not guarantees. The real case is validated during assessment.

Annual off-spec / product giveaway cost ($)

Annual lab / analyzer cost ($)

Annual downtime / lost-output exposure ($)

Conservative share of value this use case may capture (%)

Illustrative annual value—

Starting investment$75K

Illustrative payback—

Illustrative model only. It applies your capture percentage to the exposures you enter. Actual value depends on the variable, the process, data quality and validation.

The model is only useful if it survives the factory.

Training a model is the easy part. The hard part is industrial data engineering, validation against real ground truth, integration into control and MES, edge deployment, and catching drift before it costs you. That is what IndX AI Labs does.

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Industrial data engineeringHistorian, PLC, DCS, MES and lab data turned into features a model can trust.

Process-aware and hybrid modelsPhysics and process context, not a black box trained blind on raw tags.

Control, MES and twin integrationThe estimate lands in the systems that already run the plant.

Edge deployment and monitoringIndustrial Edge deployment with drift detection and retraining built in.

Best fit

Not every missing measurement needs AI.

A virtual sensor is a strong tool in the right conditions and a poor one in the wrong ones. Here is how we qualify a use case before anyone spends money.

Strong fit

The target variable is delayed, expensive, unreliable or impossible to measure continuously

Historical plant signals already exist for the process

Reliable ground-truth measurements exist at least periodically

The process has repeatable relationships across identifiable operating modes

Getting the answer earlier changes a real decision with a measurable cost

Probably not the first use case

No reliable ground truth exists to validate the estimate against

The process changes unpredictably without context or labels

No meaningful action or financial outcome depends on the estimate

A cheap, reliable physical sensor already solves the problem well

The goal is to replace a mandatory safety or regulatory instrument without proper validation, redundancy and compliance

Pricing

Start with one variable. Price the expansion against the value.

Starts at $75KOne-variable production deployment

One high-value target variable, one process, line or asset, taken from data readiness through to a monitored production estimate.

Data-readiness analysis

Model development

Data and feature engineering

Validation against ground truth

Production integration

Monitoring and handover

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Value-based expansion

For high-impact use cases, multi-variable, multi-line, site-wide, advanced-control and digital-twin extensions are priced against measurable business impact, not consultant days.

Reduced off-spec and rework
Less product giveaway
Avoided or deferred analyzer and instrumentation cost
Reduced lab sampling burden
Lower energy and material over-processing
Increased throughput
Avoided downtime where condition estimation applies

Assess your use case

What is the one variable you wish you could see right now?

Bring us the variable, the process, the data you have and what it costs when you learn the answer too late. We will tell you whether it is a credible virtual-sensor use case, and what it is worth.

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