Measurement Minute: Episode 6

Improve efficiency ratio and model validation in electric motors with low measurement uncertainty. Make confident, data-driven decisions using electrical power data.

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Why measurement uncertainty matters in engineering

When it comes to testing and measurement, many companies love to talk about accuracy. At HBK, we prefer to talk about something far more meaningful: measurement uncertainty.

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Understanding measurement uncertainty

Think of measurement uncertainty as the “confidence band” around your measurement. It tells you how sure you can be about the result – whether you’re measuring electric power, torque, or any other parameter.

Rather than a single number, uncertainty gives you a range you can trust. And that trust is essential, especially when working with electric motors.

Why it matters in electric motor testing

When tuning or adjusting motor controls, you need to know that the changes you’re making have a real effect – not just random variation within your measurement system. A low measurement uncertainty gives you that confidence.

It also allows you to compare your results to models with greater precision. If your experimental data falls outside the expected confidence band, you know your model needs refinement.

The impact on efficiency measurements

When calculating motor efficiency, measurement uncertainty becomes even more important – because it’s additive. The uncertainties from both mechanical power and electrical power contribute to your total efficiency uncertainty. Lower uncertainty means you can determine efficiency more precisely and trust your efficiency ratio.

Knowing where the losses go

Finally, understanding measurement uncertainty helps you trust where your losses are going. When you can confidently identify losses, you can trace them to heat generation. And knowing where the heat is helps you design cooler, more efficient, and more reliable motors.

Enabling confident, data-driven decisions

Measurement uncertainty isn’t just a technical detail – it’s the foundation for making data-driven decisions in engineering. It empowers you to validate changes, refine models, and design better products with confidence.

Ready to reduce measurement uncertainty?

Explore more about measurement best practices and how to reduce measurement uncertainty on our application page.

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