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.
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.
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.
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.
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.
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.
Explore more about measurement best practices and how to reduce measurement uncertainty on our application page.
인버터 구동 전기 기계의 고속 (최대 20MS/s) 측정을 위해 설계된 HBK의 전력 분석기로 정확한 효율성 통찰력을 확보하십시오. 소규모 설정부터 수백 개의 채널이 있는 시스템까지 확장 가능한 이 제품은 전자 기계 데이터를 캡처하고 제어 및 HiL 시스템에 대한 실시간 피드백을 제공합니다.
