MEASUREMENT Sc by Ashish sir niitr 4.pptx

Ppt

1. 4 MEASUREMENT SCIENCES- 4

2. Input-Output Configurations of Measuring Instruments

3. Desired inputs : Desired inputs are defined as quantities for which the instrument or the measurement system is specifically designed to measure and respond. Interfering inputs : Interfering inputs represent quantities to which an instrument or a measurement system becomes unintentionally sensitive.

4. Modifying inputs: Modifying inputs are defined as inputs which cause a change in input-output relationships for either desired inputs or interfering inputs or for both.

5. Interfering inputs : i ) Temperature: Due to change in temperature there will be change in the resistance of strain gauge thereby producing output voltage. ii) 50 Hz field of nearby power lines will induce voltage in strain gauge circuit thus producing output voltage even in the absence of an applied force.

6. Modifying input : The battery voltage e i is modifying input since it changes the proportionality factor between the desired input and the output and also between the interfering input and output.

7. Correcting the output of a system or process typically involves identifying and compensating for errors, inaccuracies, or deviations from the desired or expected outcome. The specific method for calculating output correction depends on the nature of the system, the type of errors encountered, and the available information. Here are some common methods of calculating output correction: Calibration: Calibration involves comparing the actual output of a system or instrument to a known standard and adjusting it accordingly. This is often used in measurement devices and sensors. The calibration process establishes a relationship between the measured output and the true value, allowing for correction. Feedback Control: In control systems, feedback mechanisms are employed to continuously monitor the system's output and adjust the input to maintain the desired output. Proportional-Integral-Derivative (PID) controllers are commonly used for this purpose. Model-Based Correction: Using mathematical models of the system, it's possible to predict the expected output for a given input. By comparing the predicted output with the actual output, corrections can be calculated and applied to bring the system back to the desired state. Signal Processing Techniques: In systems involving signals, various signal processing techniques can be applied for correction. Filtering, noise reduction, and adaptive signal processing methods can help improve the quality of the output signal. Machine Learning and Data-Driven Methods: Machine learning algorithms can be trained to learn patterns and relationships between inputs and outputs. Once trained, these models can predict corrections needed based on new input data, providing a data-driven approach to output correction. Environmental Compensation: Some systems are sensitive to environmental conditions. Monitoring and compensating for changes in temperature, humidity, or other external factors can be a method of correcting the output. Error Propagation Analysis: Understanding how errors propagate through a system can guide the calculation of corrections. By identifying the sources of errors and their contributions to the overall output, targeted corrections can be applied. Iterative Methods: In situations where the correction is not straightforward, iterative methods may be employed. The system is adjusted incrementally based on feedback or measurements until the desired output is achieved. Redundancy and Fault Tolerance: Systems designed with redundancy and fault-tolerant features can automatically switch to alternative components or methods when errors are detected, minimizing the impact on the overall output. Adaptive Control: Adaptive control systems continuously adjust the control parameters based on real-time measurements and feedback, allowing the system to adapt to changing conditions and correct for variations. The choice of the correction method depends on the specific characteristics of the system and the nature of the errors. Often, a combination of these methods may be used to achieve the best results in different scenarios.

8. Percentage Limiting Error

9. 4 MEASUREMENT SCIENCES- 5

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11. Instrument Performance Characteristics Static calibration: All the static performance characteristics are obtained in one form or another by a process called static calibration. The calibration of all instruments is important since it affords the opportunity to check the instrument against a known standard and subsequently to find error and accuracy.

12. Characteristics of instruments and measurements systems Actually all working instruments i.e those instruments which are actually used for measurement work must be calibrated against some reference instruments which have a higher accuracy It is impossible to calibrate an instrument to accuracy greater than that of the standard with which it is compared. i.e the calibration standard should be at least about 10 times as accurate as the instrument being calibrated.

13. Errors in measurements: Measurements done in a laboratory or at some other place always involve errors. No measurement is free from error True Value: R efers to a value that would be obtained if the quantity under consideration were measured by an examaplar method” that is a method agreed on experts as being sufficiently accurate for the purposes to which the data ultimately will be put to use. Static Error: I s the difference between the measured value and the true value of the quantity. Absolute static error Eo = Vm-Vt Relative static error Er = Eo / Vt

14. Noise Factor: Noise factor is defined as S/N at input F= S/N at output Where S= signal power N= Noise power Noise figure : If noise factor is expressed in decibel it is called noise figure nf = 10 log F The measurement of noise figure is most meaningful measurement for amplifiers, transistors and vacuum tubes since it is measure of noise generated within the device

15. Accuracy: Is the closeness with which an instrument reading approaches the true value of the quantity being measured. Thus accuracy of measurement means conformity of truth . The accuracy of a complete system is dependent upon the individual accuracy of the different functional elements of the system. Precision : Is the closeness with which individual measurement are distributed about their mean value. Precision is used in measurement to describe the consistency or the reproducibility of the results. High precision means tight cluster of repeated results while low precision indicates a broad scattering of the results

16. Precision Index Describes the spread or dispersion of repeated results about their mean value. A large value of precision index represent high precision of the data and vice versa. Significant Figures: An indication of the precision of the measurement is obtained from the number of significant figures in which it is expressed. Significant figures convey actual information regarding the magnitude and the measurement precision of a quantity. The more the significant figures ,the greater the precision of measurement

17. Static sensitivity : It is defined as the slope of the calibration curve. It is defined as the ratio of the magnitude of the output signal or response to the magnitude of the input signal of the quantity being measured. Static sensitivity = Infinitesimal change in output / Infinitesimal change in Input = Δ O/ Δ i Inverse sensitivity or deflection factor= Δ i / Δ O

18. A low input impedence device connected across the signal source draws more current and drain more power from the voltage signal source (loads the source more heavily) than a high input impedence device. e.g voltmeter infinite input impedence

19. For voltage sources, the lower the output impedence, the lower is the voltage drop and also lower is the power consumption. There should not be any loading effect if the output impedence Zo of the voltage source is equal to zero

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21. Generalized impedence and stiffness concept: The introduction of any measuring instrument in a measurement system results in extraction of energy from the system thereby resulting in loading effect, causing a distortion of the measurand . Hence perfect measurement of measurand is impossible. Loading effects occur due to extraction of energy from the measurand . The physical variables which determine the flow of energy in all dyanamical system can be classified as

22. Through variables Across variables Through variables : Through variables are those which can be specified and measured at one point in space. These variables are also called flow variables Across variables: Across variables are those which can be specified and measured at two point in space. These variables are also called effort variables . Generalized input impedence Zgi = qi1/qi2 where qi1 = across variables & qi2= through variables

23. Zero drift : If the whole calibration gradually shifts due to undue warming up of electronic tube circuits ,Zero drift sets in. This can be prevented by zero setting. Span Drift or Sensitivity drift : If there is a proportional change in the indication all along the upward scale the drift is called span drift or sensitivity drift. Zonal drift: If the drift occurs only over a span of the instrument it is called zonal drift.

24. The drift is due to stray electrical and magnetic field, thermal e.m.f , changes in the temperature, mecahnical vibrations ,wear and tear, and high mechanical stressed developed in some part of the instruments or the system. Drift is undesirable quantity in industrial instrumentation because it is rarely apparent and cannot be easily compensated. Stray electrostatic and magnetic field can be prevented by proper shielding and the effect of mechanical vibrations can be reduced by having proper mounting