1. MECH 373 Instrumentation and Measurement
John Cheung
Phone: x3791, Office Location: EV
Course Website: Access from your “My Concordia” portal

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

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
1. Definition and classification of dynamic systems (chapter 1)
Lumped/distributed, continuous-time/discrete-time, linear/nonlinear systems, quantization and superposition property
2. Translational mechanical systems (chapter 2)
Variables, absolute and relative displacements, spring and damper laws, free-body diagrams, Newton’s Laws, energy and power, series/parallel connections
3. Standard forms for system models (chapter 3)
Input-output and state variable equations, matrix formulation
4. Block diagrams and computer simulation with Matlab/Simulink (chapter 4)
Basics on using Matlab/Simulink for system modeling, simulation and analysis
5. Rotational mechanical systems (chapter 5)
Variables, absolute and relative angular displacements, spring and damper laws, free body diagrams, moment of inertia, Newton-Euler’s Law, lever and gears
6. Electrical systems (chapter 6)
Variables, element laws for resistor, capacitor and inductor, energy and power, open and short circuits, Kirchhoff’s Laws (current and voltage), resistive circuits, series/parallel connections, impedance, operational amplifiers
7. Analysis and solution techniques for linear systems (chapters 7 and 8)
Laplace transform and its properties, transfer function, natural and forced responses of first-order (emphasis on circuits) and second-order (emphasis on translational/ rotational) systems, transient and steady-state responses, frequency response.
8. Developing a linear model (chapter 9)
Linearization of nonlinear models and linear representation of nonlinear components, computer simulation of nonlinear and linearized systems
9. Electromechanical systems (chapter 10)
Resistive coupling and the voltage divider, coupling by a magnetic field: Laplace force and Faraday’s law for induced voltages, devices coupled by magnetic fields: microphone, galvanometer and DC motor
10. Thermal and fluid systems (chapters 11, 12)
Thermal and fluid capacitances, resistances and sources, 1st Law of Thermodynamics, conservation of mass, system dynamic models

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.
1. Definition and classification of dynamic systems (chapter 1)
Lumped/distributed, continuous-time/discrete-time, linear/nonlinear systems, quantization and superposition property
2. Translational mechanical systems (chapter 2)
Variables, absolute and relative displacements, spring and damper laws, free-body diagrams, Newton’s Laws, energy and power, series/parallel connections
3. Standard forms for system models (chapter 3)
Input-output and state variable equations, matrix formulation
4. Block diagrams and computer simulation with Matlab/Simulink (chapter 4)
Basics on using Matlab/Simulink for system modeling, simulation and analysis
5. Rotational mechanical systems (chapter 5)
Variables, absolute and relative angular displacements, spring and damper laws, free body diagrams, moment of inertia, Newton-Euler’s Law, lever and gears
6. Electrical systems (chapter 6)
Variables, element laws for resistor, capacitor and inductor, energy and power, open and short circuits, Kirchhoff’s Laws (current and voltage), resistive circuits, series/parallel connections, impedance, operational amplifiers
7. Analysis and solution techniques for linear systems (chapters 7 and 8)
Laplace transform and its properties, transfer function, natural and forced responses of first-order (emphasis on circuits) and second-order (emphasis on translational/ rotational) systems, transient and steady-state responses, frequency response.
8. Developing a linear model (chapter 9)
Linearization of nonlinear models and linear representation of nonlinear components, computer simulation of nonlinear and linearized systems
9. Electromechanical systems (chapter 10)
Resistive coupling and the voltage divider, coupling by a magnetic field: Laplace force and Faraday’s law for induced voltages, devices coupled by magnetic fields: microphone, galvanometer and DC motor
10. Thermal and fluid systems (chapters 11, 12)
Thermal and fluid capacitances, resistances and sources, 1st Law of Thermodynamics, conservation of mass, system dynamic models

5. Course Objective / Requirement
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: % (Suggestion: 29-Oct, Fri.)
Laboratories: %
Final exam: %
Need to pass all components in the course.

6. What Could 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. Purpose of Measurement Systems (2)
Process, machine or system being measured
Observer
Input
True value of variables
Measurement
System
Output
Measured value of variables

9. Purpose of Measurement Systems (3)
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

10. Purpose of Measurement Systems (4)
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)
Desired
output + -
Error
signal
Control
signal
Actuating
signal
Plant
output
Controller
Actuator
Plant
Sensor

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

13. Essential Elements Measurement System Input Output Sensing Element
True value of variables
Measurement
System
Output
Measured value of variables
Sensing Element
Conditioning Element
Processing Element
Displaying Element

14. Sensing Elements 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 Element
Conditioning Element
Processing Element
Displaying Element

15. Signal Conditioning Elements
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)
Sensing Element
Conditioning Element
Processing Element
Displaying Element

16. Signal Processing Elements
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)
Sensing Element
Conditioning Element
Processing Element
Displaying Element

17. Data Display Elements
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
Sensing Element
Conditioning Element
Processing Element
Displaying Element

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

19. Definition of 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.
E = measured valve (s) – true valve = system output – system input

20. Definition of Precision errors
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).

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. Measurement Errors
Sensing
Element
Conditioning
Processing
Presentation
True
Value
Measured K1 K2 K3 K4 I O
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. Types of Measurement Errors
Sources of errors
Improper sensing position
Improper element calibration
Improper data acquisition method
Improper sampling rate (Ch. 5)
Elements non-linearity
Environment effects
Characteristic of errors
Systematic errors
Random errors
Parameter tracking errors

25. Types of Measurement Errors
Calibration.
Loading.
Non-linearity.
Hysteresis.
From Dr. Bruce McNair’s lecture slides (SIT)
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. How to reduce the measurement errors? Topic of next lecture
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.
From Dr. Bruce McNair’s lecture slides (SIT) 32 32