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

Textbook and References 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.

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

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

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: 20% (Suggestion: 29-Oct, Fri.) Laboratories: 15% Final exam: 55% Need to pass all components in the course.

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

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, ...

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

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

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

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.

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

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

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

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

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

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

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).

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

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).

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.

Characteristics of accuracy Uncertainty = % of full scale.

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.

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

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

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

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.

Characteristics of Measurement Errors

Characteristics of Measurement Errors

Characteristics of Measurement Errors

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

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