Introduction to Instrumentation Engineering

1. Introduction to Instrumentation Engineering Chapter 1: Measurement Error Analysis By Sintayehu Challa

2. Goals of this Chapter • Differentiate the types of error • Every measurement involves an error • Give an overview of data analysis techniques in instrumentation systems • Understand basic mathematical tools required for this purpose Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

3. Overview • Measurement error analysis • Types of errors and uncertainty • Statistical analysis • Gaussian and Binomial distributions • Method of Least Squares Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

4. Measurement Error • Types of errors: Systematic and random errors • Systematic error • Cause repeated readings to be in error by the same amount • Consistent, or fixed error component • May arise due to instrument short coming & environmental effects • Related to calibration errors and can be eliminated by correct calibration • Or human error such as consistent misreading and arithmetic error such as incorrect rounding off • Or by using an inadequate measurement methods • Example unjustified extrapolation of experimental data • Accuracy is related to such type of errors Introduction to Instrumentation Engineering- Ch. 2 Analysis of Experimental Data

5. Measurement Error …. • Systematic errors analysis can be divided into • Worst-cases analysis and RMS error analysis • Worst-case analysis: Let Qm be the measured quantity and Qt be true quantity • Error: • Relative error: • E.g., if the measured value is 10.1 when the true value is 10.0, the error is -0.1. If the measured value is 9.9 when the true value is 10.0, the error is +0.1 Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

6. Error & Uncertainty • Uncertainty • Since the true value cannot be known, the error of a measurement is also unknown • Thus, the closeness of the value obtained through a measurement to the true value is unknown • We are uncertain how well our measured value represents the true value • Uncertainty characterizes the dispersion of values • ±Ea is the assigned uncertainty of Ea Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

7. Error and Uncertainty … • Differentiate between error and uncertainty • Error indicate knowledge of the correct value • May be either positive or negative! • Uncertainty indicate lack of knowledge of the correct value or may be either positive or negative! • Is always a positive quantity, like standard deviation Introduction to Instrumentation Engineering- Ch. 2 Analysis of Experimental Data

8. Combined Uncertainty … • Given a function • The RMS error is given as • And Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

9. Overview • Measurement error analysis • Types of errors and uncertainty • Random Error Analysis Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

10. Measurement Error …. • Random errors • Due to unknown cause and occurs when all systematic errors have been accounted for • Caused by random electronic fluctuations in instruments, unpredictable behavior of the instrument, influences of friction, etc… • Random fluctuations usually follow certain statistical distribution • Treated by statistical methods • Characterized by positive and negative errors • Such errors are related to precision Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

11. Statistical Analysis • Allows analytical determination of uncertainty of test result • Arithmetic mean (or most probable value) of n readings x1 toxnis given by • With a large sample, frequency distribution of the individual xi’s can be used to save time • If a particular value of xi occurs in the sample fj times, the mean value can be determined as • The sample frequency fj/n is an estimate of the probabilityPjthat x has the value of xj in the population sample Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

12. Statistical Analysis … • Deviation xi-xm is the difference of all readings or observations from the mean reading • Is a good indicator of the uncertainty of the instrument • Average deviation: Sum of the absolute value of all deviations, i.e., • Tends to zero and gives an indication of the precision of the instrument (low value shows that the instrument is highly precise) • Standard deviation: deviation from the mean & is given as • σ is called the population or biased standard deviation • Measure the extent of expected error in any observation • Variance: σ2 Introduction to Instrumentation Engineering- Ch. 2 Analysis of Experimental Data

13. Statistical Analysis … • Using the probability distribution Pj and noting that Pj=fj/n • For most distributions (both real and theoretical) met in statistical work, more than 94% of all observations in the population are within the interval xm  2 • Generally, it is desirable to have about 20 observations in order to obtain reliable estimate of  • For smaller set of data, the expression for  modifies to • Called unbiased or sample standard deviation Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

14. Cumulative Frequency Distribution • Sometimes the investigator is interested in estimating the proportion of the data whose values exceed some stated level or fall short of the level • E.g., for the random number, if the cumulative frequency distribution for drawing digits less than 3 is 0.75 and the cumulative frequency distribution for drawing digits >= 3 is 0.25 • See the cumulative frequency distribution shown in the figure Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

15. Overview • Measurement error analysis • Types of errors and uncertainty • Statistical analysis • Gaussian distributions • Method of Least Squares Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

16. Gaussian Distribution • Measurements will always have random errors • For a large number of data, these errors will have a normal distribution which follows • P(x) is the probability density function • It gives the probability that the data x will lie between x and x+dx • Is called the Gaussian or Normal error distribution • Xm is the mean and  is the standard deviation Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

17. Gaussian Distribution … • Gaussian error distribution for σ=0.5 and 1 and xm=3 • Probability density function has the property • Xm is the most probable reading • The value of the maximum probability density function is Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

18. Gaussian Distribution … • The standard deviation is a measure of the width of the distribution curve about the mean • Smaller σ produces larger value of the maximum probability • For a measurement, this tends to go to more precision • The probability that a measurement will fall within a certain range x1 of the mean reading is given by Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

19. Contnd. • Almost all random observation have a probability density distribution P(x) which is a well known Analytical form • Where d is the deviation and h is a parameter representing a measure of precision • The relationship between h, σ and D for a Gaussian Distribution • The probable error has been used in experimental work Probable Error=0.675 σ Instrum. & Control Eng. for Energy Systems - Ch. 2 Analysis of Experimental Data

20. Class Exercise The measurement of Resistance of a 100 ohm resistor give the following results 101.2,101.7,101.3,101.0,101.5,101.3,101.2,101.4,101.3 and 101.1 ohms Assuming that the random errors are present Determine a) The arithmetic mean of the data b) The Deviation of the data and the sum of deviation of the data. c)The Average deviation of the data d) The Standard deviation and e) The Probable Error Instrum. & Control Eng. for Energy Systems - Ch. 2 Analysis of Experimental Data

21. Class Exercise A set of Independent current measurement was taken by six observers and recorded as 12.8mA,12.2mA,12.5mA,12.9mA,12.6mA and 12.4mA Determine a) The arithmetic mean of the data b) The Deviation of the data and the sum of deviation of the data. c)The Average deviation of the data d) The Standard deviation and e) The Probable Error Instrum. & Control Eng. for Energy Systems - Ch. 2 Analysis of Experimental Data

22. Overview • Measurement error analysis • Types of errors and uncertainty • Statistical analysis • Gaussian distributions • Method of Least Squares Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

23. Random Error Analysis of Scattered Data • If the scatter of Data point is not too great, it is relatively simple to draw the input and output using straight line through the data points. • If the Data points are widely scattered the Linear Least square algorithm or Regression Analysis can be used • The LLSA is a method for fitting the best curve of a pre-determined form through a series of experimental data points • Or • The LLSA is also known as the “minimum square error method for the best curve fitting” Instrum. & Control Eng. for Energy Systems - Ch. 2 Analysis of Experimental Data

24. Method of Least Squares • In the operation of an instrument, input parameter is varied over some range • Could be in increments or decrements • Happens during calibration or measurement • Least square can be applied to determine an equation for a measured data • Used to fit the data into a line (cure) to give a working relation between input and output • This relation will help to determine the characteristics of the instrument Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

25. Method of Least Squares … • Example: Linear Least Square Analysis (LLSA) • Suppose xi and yi be the input and measured values, respectively such that the data points (x1 , y1), (x2 , y2), ….. (xn , yn) are obtained • If the expected straight line is of the form y = mx + b • where m is the slope and b is the intercept • The error, which is the difference between the actual and measured data, summed for all points is given as • Minimizing S using Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

26. Method of Least Squares … • Will give • And Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data

27. Method of Least Squares … • Example: Assume that the input and output are related by a second order equation y = b2x2 + b1x +b0 • The error will take the form • Minimizing the error with respect to b0, b1, and b2 yields • n = total number of data points Introduction to Instrumentation Engineering - Ch. 2 Analysis of Experimental Data