Bias, Linearity, and Stability Study in Measurement
→ These studies are critical in measurement system analysis and calibration.
→ These studies are a combination of the interaction of a part, an operator, and an instrument.
→ By using this analysis, we can make our system more robust.
→ Measurement system analysis (MSA) is an important tool for assessing the performance of a measurement system and its ability to produce reliable data.
→ Now, we will learn about all the above-mentioned concepts in detail.
→ Refer to the below-mentioned classification of the variation.
Table of Contents:
- Bias, Linearity, and Stability Study in Measurement
- What is Bias?
- What is Linearity?
- When to Conduct a Linearity Study?
- How to Perform a Linearity Study?
- Example of Linearity
- Possible Causes of Linearity Error
- What is Stability?
- When to Conduct a Stability Study?
- How to Perform a Stability Study?
- Example of Stability
- Possible Causes of Stability Error
- Conclusion
What is Bias?
→ Bias is the difference between the observed measurement average and the reference value.
→ In simpler terms, bias occurs when measurements are consistently off-target, either overestimating or underestimating the true value.
→ This can be caused by different factors, such as calibration issues, instrument defects, or environmental conditions affecting measurement.
→ The reference value, also known as the accepted reference, true value, or master value.
When to Conduct a Study?
⏩Refer to the following key points:
- During the initial validation of a measurement system
- After calibration or maintenance
- When switching to a new method
- When changing to a new environment
- Before audits or certifications
How to Perform a Study?
⏩Refer to the below-mentioned steps:
- Select the accepted reference value for the part
- Select the measurement system
- Determine the sample size
- Perform measurement
- Calculate bias using the formula
Example of a Bias Study:
→ As we know, the bias is the difference between the observed average of measurement and the reference value.
→ Now, we will take one example to understand this concept in detail.
→ Refer to the below picture; we have mentioned different readings and calculated the bias value.
Possible Causes of Bias:
- Worn instrument/fixture
- Worn or damaged master
- Improper calibration
- Poor quality instrument
- Measuring the wrong characteristic
- Improper use of an instrument
- Unclear Procedures
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What is Linearity?
→ Linearity is the difference in the bias values through the expected operating range of the gauge.
→ For example, if we have a vernier caliper of 300mm range, then at the time of calibration, we have to cover the entire range of the vernier so we can compare and decide whether this instrument has variation or not.
→ Linearity issues can lead to misinterpretation of data and impact decision-making.
When to Conduct a Study?
⏩Refer to the following key points:
- Validation of a measurement system
- After a major calibration or repair
- Periodically as a part of a measurement system analysis
- Before regulatory audits or quality certifications
How to Perform a Study?
⏩Refer to the below-mentioned steps:
- Select a reference standard
- Choose the measurement system
- Determine sample size
- Measure and record data
- Take 10 – 12 repeated readings on each part
- Calculate the part’s bias and plot it against the references
- Linearity is represented by the slope of the best-fit line of these points
Example of Linearity:
→ Linearity is the bias at the different operating range of the measuring instrument.
→ Now we will take one example for better understanding this concept.
→ Refer to the below picture, we have mentioned different samples' readings and calculated different values.
→ Now we will plot a graph of the different reference value vs bias.
→ From the below graph, we can easily understand the linearity with the help of the slope.
Possible Causes of Linearity Error:
- Environmental factors such as temperature and humidity
- Instrument without calibration
- Worn instrument or fixture
- Worn or damaged master
- Poor quality instrument
- Wrong gauge for the application
What is Stability?
→ Stability means the bias of a measurement system changes over time.
→ In other words, stability evaluates if the measurement system's performance remains consistent over time.
→ It indicates whether the system maintains its accuracy and precision under normal operating conditions.
→ Stability issues can lead to inaccuracies that are not immediately visible but can affect long-term data reliability.
When to Conduct a Study?
⏩Refer to the following key points:
- During the initial qualification of a measurement system
- As part of routine quality control
- After a calibration
- After maintenance, repairs, or software updates
- Before audits or certifications
How to Perform a Study?
⏩Refer to the below-mentioned steps:
- Select a reference standard
- Choose the measurement system
- Determine study duration and frequency
- Collect the data
- Analyze the data
- Interpret the results
- Take action (if needed)
Example of Stability:
→ Stability measures the bias over time.
→ Also, it is known as drift.
→ Now we will take one example for better understanding this concept.
→ Refer to the below picture, we have mentioned different readings over time.
Possible Causes of Stability Error:
- Instrumentation Errors
- Environmental Factors
- Sample Handling & Preparation Errors
- Human Errors
- Methodological Errors
- Data Processing & Software Errors
Conclusion:
→ Bias, linearity, and stability are interconnected aspects of Measurement System Analysis.
→ They directly impact measurement accuracy.
→ If bias, linearity, and stability are all within acceptable limits, the measurement system is reliable and accurate.
→ If any of these fail, corrective actions such as recalibration, maintenance, or process improvements are required to maintain measurement integrity.
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