MECH 373 Instrumentation and Measurement

1. MECH 373Instrumentation and Measurement John Cheung Phone: x3791, Office Location: EV 3 - 280 Email: jcheung373@gmail.com Course Website: Access from your “My Concordia” portal

2. Textbook and References • Textbook: • Anthony Wheeler & Ahmad Ganji, “Introduction to Engineering Experimentation”, 3nd ed., Pearson-Prentice Hall, 2004, ISBN 0-13-065844-8. • Reference: • R. S. Figliola and D. E. Beasley, “Theory and Design for Mechanical Measurements”, Wiley, 2006, ISBN: 978-0-471-44593-7.

3. Course Outline 1. Introduction • course objective and requirements; why measurement systems, experimental design 2. General Characteristics of Measurement Systems • components • instrumentation • error – systematic & random, accuracy, precision, sensitivity • calibration, traceability of standards • dynamic measurement systems – response, damping, etc 3. Measurement Systems with Electrical Signals • sensors, amplification, attenuation, filtering • measurement instruments • sensor principles and characteristics 4. Computer-based Data Acquisition Systems • system components – principles of A/D & D/A conversion

4. Course Outline 5. Sampling and Analysis of Time-Varying Signals • characteristics of time-varying signals • sampling rate considerations • filtering 6. Statistical Analysis of Experimental Data • noises • experimental considerations 7 Experimental Uncertainty Analysis • propagation of uncertainty • uncertainty analysis 8. Sensor Systems for Engineering Applications • measurement of various parameters of interest to engineers, e.g. displacement, velocity, temperature, pressure, flow, vibration, stress, liquid level etc.

5. Objective: Introduce the fundamental principles that need to be followed when setting up a measurement experiment. Develop a basic understanding of measurement systems and its role in engineering. Learn how to analyze experimental data. Requirements: Quizzes (two): 10% (Based on questions from assignments and tutorials) Midterm: 20% (Suggestion: 29-Oct, Fri.) Laboratories: 15% Final exam: 55% Need to pass all components in the course. Course Objective / Requirement

6. WhatCould I Learn from This Course? You will be able to: • Use basic instruments • Design measurement systems • Select right sensors • Design engineering tests • Perform measurements • Analyze test data

7. Purpose of Measurement Systems (1) Human uses various sensation methods to explore our surrounding world. Vision: light, color, shape, size, … Sound:tone, volume, ... Touch: smooth, rough, ... Smell: odor, ... Taste: sweet, salty, ... Feeling: hot, cold, ... Motion:move, turning, up, down, vibration, ...

8. Output Input Measured valueof variables True valueof variables Observer Process,machine orsystem beingmeasured Purpose of Measurement Systems (2) Measurement System

9. They are used for many purposes in a wide variety of application areas: Experimental engineering analysis Monitoring of processes and Control of processes Experimental engineering analysis – Engineering research - relies on laboratory experiments to find solutions for new products or processes. Development – Validation and testing on improved products. Performance testing – Check reliability, product life and product performance. Semi-active application, needs experimental design Monitoring of processes – when the measurement device is being used to keep track of some quantity, e.g. tracking weather conditions or engine health conditions – passive application Purpose of Measurement Systems (3)

10. Control of processes – the measurement is used not only to track a quantity but also to change its value in case it is not equal to the desired value – active application (e.g. household furnace) Control signal Actuating signal Plant output Actuator Plant Desired output Error signal Controller + - Sensor Purpose of Measurement Systems (4)

11. What is a Measurement? • Encyclopedia Encarta In classical physics and engineering, measurement generally refers to the process of estimating or determining the ratio of a magnitude of a quantitative property or relation to a unit of the same type of quantitative property or relation. Process of measurement involves the comparison of physical quantities of objects or phenomena … • Wikipedia Measurement is the estimation or determination of extent, dimension or capacity, usually in relation to some standard or unit of measurement.

12. States of process or system Empirical Space Curves or values Abstract Space Measurement Theory Measurement is a mapping of a source set in the empirical domain space onto an image set in the abstract range space.

13. Output Input Measured valueof variables True valueof variables Essential Elements Measurement System SensingElement ConditioningElement ProcessingElement DisplayingElement

14. In contact with the information carrier or medium Giving a signal output related to the quantity being measured Examples: strain gauge, R depends on mechanical strain; thermocouple, V depends on the temperature; Linear variable differential transformer (LVDT), L depends on the displacement. Sensing Elements SensingElement ConditioningElement ProcessingElement DisplayingElement

15. Prepare sensor outputs suitable for further processing. Mostly use various conditioning circuits. Examples: deflection bridge, converts resistance change into a voltage change (strain gauge) amplifier, amplifies millivolts to volts Filter and attenuation (noise reduction) Signal Conditioning Elements SensingElement ConditioningElement ProcessingElement DisplayingElement

16. Converting conditioned output into forms more suitable for presentation. Calculating secondary variable from measurable variables. Examples: analog-to-digital converter or vice verse analog or digital filter signal compensation (FFT, averaging) Signal Processing Elements SensingElement ConditioningElement ProcessingElement DisplayingElement

17. Display and/or store measured signals in recognizable form. Use of analog and/or digital form. Examples: visual display units, like Oscilloscope analog chart recorders digital data array Data Display Elements SensingElement ConditioningElement ProcessingElement DisplayingElement

18. Measurement error • Error in measurement: • Error = Measured value – true value. • Types of error in experiments: • Systematic errors (fixed or bias errors) • Random errors (precision errors).

19. Systematic errors: Defined as the closeness of agreement between a measured value or an average of measured values and the true value. Examples – calibration errors, linearity errors. Definition of Systematic errors E = measured valve (s) – true valve = system output – system input

20. Precision errors: Characterize the degree of mutual agreement among a series of individual measurements. Highly precise measuring system gives same value each time it read, but it may have large systematic error (not very accurate). Examples: Environment (noise, temperature), measurement systems (need shielding). Definition of Precision errors

21. Accuracy of measurement • Accuracy – Closeness of agreement between measured value and true value – specify uncertainty in device specifications. • Include both residual systematic and random errors in measurement system (sensor) – specified as a % of full scale. • For example, accuracy = ±5% of full scale for output with 0 to 5V range. • Uncertainty = ± 0.25V. • Lower end of the range – unsatisfactory.

22. Characteristics of accuracy Uncertainty = % of full scale.

23. True Value Measured Value Sensing Element Conditioning Element Processing Element Presentation Element I O K1 K2 K3 K4 Measurement Errors • None of the elements can be perfectly manufactured and integrated in the system, hence error. • Error increases through different measurement elements from sensor element to output element.

24. Sources of errors Improper sensing position Improper element calibration Improper data acquisition method Improper sampling rate (Ch. 5) Elements non-linearity Environment effects Types of Measurement Errors Characteristic of errors • Systematic errors • Random errors • Parameter tracking errors

25. Types of Measurement Errors Calibration. Loading. Non-linearity. Hysteresis. systematic error (bias error) = average of readings – true value

26. Types of Measurement Errors Temperature. Noise. Environment. Variability in components being measured due to manufacturing processes. random error = reading – average of readings

27. Types of Measurement Errors Either: • Parameter changes too rapidly – sampling rate not good enough. • Parameter goes outside measurement range – not within bandwidth. • Parameter change is too small to be observed – poor resolution in sampling.

28. Characteristics of Measurement Errors

29. Characteristics of Measurement Errors

30. Characteristics of Measurement Errors

31. Summary How to reduce the measurement errors? Topic of next lecture

32. Intrusive and non-intrusive device Intrusive measurement system – large loading error, e.g. thermometer used for water temperature measurement. Non-intrusive – Negligible loading errors, e.g. radar gun. 32