Are we undervaluing instrument drift in process environments?

In highly automated process plants, instrumentation controls everything.  

Flowmeters, pressure sensors, thermocouples, level gauges – all generate data that drives everything from control loops to emissions reporting.  

But what happens when those instruments quietly deteriorate?

The gradual deviation of a sensor’s output from its true value, instrument drift is often underestimated, especially in continuous processes.  

Yet over time, drift can lead to process inefficiencies and non-compliance.  

And in many facilities, there’s no systematic strategy for catching it.

Are we losing control of calibration in the age of too many sensors?

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The hidden costs of drift

Even small drifts — say, 1–2% over several months — can translate into real-world impacts.

It could lead to incorrect batching or blending. If a flowmeter under-reports by 2%, product formulations may deviate from spec.

There’s a possibility of missed alarms or over-range events, too, where a pressure transducer drifting upward might normalise dangerously high readings.

Relatedly, regulatory non-compliance will become an issue when emissions monitors or custody transfer instruments misstate reportable quantities.

Most crucially, perhaps, drift in temperature or level sensors can disrupt boiler control, leading to unnecessary expenses.

In many cases, the drift isn’t noticed until it triggers a visible problem. By then, the cost is already sunk.

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Which instruments drift the most?

Not all instruments drift equally. Factors like sensor type, process exposure, and environmental conditions all affect stability.

High-drift candidates include thermocouples, pH probes, NDIR and electrochemical gas sensors as well as low-cost pressure and flow sensors.

Even robust instruments like Coriolis meters or RTDs will drift over time, just more slowly.  

Long calibration intervals (6–12 months) are only valid if the process is stable and environmental factors are controlled.

Why traditional calibration regimes fall short

Many plants still rely on fixed interval calibration: every 3, 6 or 12 months, regardless of operating conditions.

But this so-called ‘set and forget’ model has serious limitations.

Because drift is not linear, some sensors degrade suddenly after a threshold

Static scheduling doesn’t adhere for the ‘duty cycle’, i.e. a sensor in continuous use will drift faster than one in standby.

Most obviously, manual calibration logs can be incomplete or error-prone.

Digitalisation hasn’t always helped.

Some IIoT deployments have increased the number of sensors without upgrading calibration capacity.

This creates an illusion of visibility while the data quality quietly deteriorates.

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Smarter calibration: tools and tactics

A more resilient strategy focuses on condition-based and self-diagnosing calibration approaches:

1. Built-in diagnostics

Modern instruments often include health metrics like signal-to-noise ratio or calibration drift predictions.  

These should be monitored through SCADA or asset management systems, not ignored.

2. Auto-calibrating devices

For pH, DO, and gas sensors, auto-calibration with onboard standards or cleaning cycles is increasingly common.  

These reduce technician workload and extend field life, especially in remote or hazardous zones.

3. Redundancy and cross-checking

Dual sensors (e.g. two flowmeters in parallel) can identify anomalies via deviation alarms.  

Cross-parameter logic (e.g. comparing mass and volumetric flow) can reveal drift before it becomes critical.

4. Calibration management software

Platforms like CMMS or APM suites can schedule calibration based on runtime or sensor diagnostics rather than fixed calendars.

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Valuing accuracy, not just data

Too often, calibration is seen as a compliance task, not a performance enabler.  

But if the data driving your operations is off by 2–3%, that inaccuracy multiplies across stages.

Facilities that embrace calibration as a critical control point, rather than a necessary evil, tend to see improvements.

These facilities see tighter process control, reduced waste, fewer false alarms and more credible data.

As plants grow more sensor-dense and data-dependent, the risk of calibration chaos increases.  

It’s time to treat sensor accuracy not as a maintenance line item, but as a core operational risk.