Unformatted text preview: EE5108 Instrumentation & Sensors
Arthur Tay 1 Contact Informations
• Lecturers: Profs Sam Ge and Arthur Tay
• Day/Time: Friday (6 – 9pm)
• Venue: LT2
• Office: E4-‐08-‐12 or ECE dept office
• Tel: 6516-‐6326
• Email: [email protected]
• Office hours: please email or call me before you come 2 Course Mechanics
• All class information, lecture notes on IVLE course website.
• Course requirements:
• 2 assignments (40%)
• Final exam (60%)
(these weights are approximate; we reserve the right to change them later) • Textbook and references (copies are available at the Central library)
• C. D. Johnson, Process Control Instrumentation Technology, Prentice Hall, 2014
E. O. Doebelin, Measurement Systems: Application and Design, McGraw Hill, 2003
R Pallas-‐Areny, J Webster, Sensors and Signal Conditioning, Wiley, 2001
References, hints, sources:
Sensors magazine site:
3 Module Descriptions
• This module covers Generalised Measurement System, Interface Electronics and Signal Processing, Noise and Interference in Measurements, and Data Transmission Techniques. As far as sensors are concerned, Motion (displacement, velocity and acceleration), Force & Tactile, Temperature, Pressure/ Flow, Machine Vision & Applications, and Optical Sensors will be discussed. Recent Advances in sensors such as smart sensors and micro-‐sensors will be highlighted. 4 Main Categories of Measurement Applications
• Monitoring of processes and operations: measurement devices are used to keep track of some quantity. • Control of processes and operations: measurement is the key to feedback control, in fact the lack of in-‐situ sensors is a major reason why significant number of manufacturing plants are run in open-‐loop. • Experimental engineering analysis: part of the engineering design, development, and research that relies on laboratory testing (either theoretical/simulation or experimentation) to answer questions. 5 Motivations
• Measurement systems • Sensor: responds to the quantity being measured by giving as its output a signal which is related to the quantity. E.g. thermocouple
• Signal conditioner: takes the signals from the sensor and manipulates it into a condition which is suitable for display or control.
• Display system: display the output from the signal conditioner.
• Feedback Control Systems • At its simplest, a control system is a device in which a sensed quantity is used to modify the behavior of a system through computation and actuation.
• A simple feedback system consists of the process whose output is to be controlled, the actuator whose output causes the process output to change, reference and output sensors that measure these signals, and the controller, which implements the logic by which the control signal that commands the actuator is calculated.
7 Key Elements in a Feedback System
• Process/Plant: • a complex assembly of phenomena that relate to some manufacturing sequence, • single variable vs. multivariable processes. • Measurement/Sensor: • measurement: conversion of the variable into some corresponding analog of the variable, such as a pneumatic pressure, an electrical voltage or current, or a digitally encoded signal, • sensor: device that performs the initial measurement and energy conversion of a variable into analogous, digital, electrical, or pneumatic information, • signal conditioning, instrumentations, • final representation of the variable value in some form required by the other elements in the process-‐control operation. 8 Key Elements in a Feedback System
• examine the error (difference between the measured variable and the reference value or set-‐
point) and determine the appropriate action to be taken, • usually some form of signal processing algorithm performed by the microprocessor-‐based computers. • Actuator/Control element:
• final control element: device that exerts a direct influence on the process (e.g. a valve to control water flow), • accepts an input from the controller, which is then transformed into some proportional operation performed on the process, • actuator: intermediate operation between controller output and final control element. It uses the controller signal to actuate the final control element. The actuator translates the small energy signal of the controller into larger energy action on the process. 9 Motivations
• Control Systems – automatic control of water level 10 Motivations
• Control Systems – shaft speed control 11 Motivations
• Digital Control Systems 12 Motivations
• Digital Control Systems
• Data Conversions:
• analog-‐to-‐digital converters (ADCs): convert analog signal (e.g. voltages) into a digital representation. In a control system, the sensor usually produces an analog output (e.g. voltage) which the ADC used to convert into a digital representation for input to the computer. • digital-‐to-‐analog converters (DACs): convert a digital signal into an analog voltage. These devices are used to convert the control output of the computer into a form suitable for the final control element. 13 Motivations
• Based on the previous applications, we hoped that at the end of the course, you will be able to • From both monitoring and control perspective: to be able to choose the correct sensors, instrumentation for a particular problem. This often boils down to sensor placement, a combination of different sensors (sensor fusion), the use of observer (estimator) to measure inaccessible variables in a physical system. The sensor specifications for monitoring and control is usually different. • For engineering analysis and design, an understanding of the sensor dynamics is important. Example, you need to design a thermal system, to simulate the response of the system accurately, it is critical to simulate both the measured and the actual temperature of the system. 14 Additional Examples
• Unmanned Aerial Vehicles More information: uav.ece.nus.edu.sg •
• Sensing: GPS, ultrasonic sonar, RPM sensor, camera
Actuation: various rotor torques
Computation: on-‐board computer system, image processing
Effect: autonomous UAVs, ground target tracking 15 Additional Examples
• Robot Soccer
More information: ; • Sensing: overhead camera system, wheel angle encoders
• Actuation: motor torques, kick mechanism
• Computation: centralized computer, vehicle microcomputers
• Effect: autonomous robot soccer platform, agile motion 16 Additional Examples
• Congestion Control & Internet • Sensing: data, ACK packets via TCP
• Actuation: transmit rate, router paths
• Computation: source, destination, router processors
• Effect: high speed data transmission, tolerant to link failures 17 Additional Examples
• Thermal Processing System in Semiconductor Manufacturing 18 Standards & Definitions
• Units: SI units (refer to any instrumentation books)
• Analog data representation: • Range of output signal should cover entire range of the actuator, e.g. if a controller send an output signal to a continuous valve, this signal will be designed to cover the range from fully closed to fully open, with all the various valve settings in between.
• Common analog standards
For pneumatic systems, we use a range of gas pressure carried in pipes. For electrical systems, we use a range of electric current carried in wires. • Pneumatic signals: 3 – 15 psi
Equivalent SI range is 20 to 100 kPa.
When a sensor measures some variable in a range, it is converted into a proportional pressure of gas (usually dry air) in a pipe. 19 Standards & Definitions
• Current signals: 4 – 20 mA
Example: Suppose the temperature range 20 to 120oC is linearly converted to the standard current range of 4 to 20 mA.
Consider a linear equation between temperature and current, we then have:
solving the 2 equations, we have
At 66oC, we have I = 11.36 mA; for 6.5 mA, we have T = 35.6oC. • Current is used instead of voltage because the system is less dependent on load. 20 Sensor Classification
• Many types of sensors for different physical quantities, classified as follows:
1. Power supply:
• Sensors may be divided into active and passive sensors according to whether its output is entirely produced by the quantity being measured or whether the quantity being measured simply modulate the magnitude of some external power source.
• Modulating (active): requires external power or excitation signal, for their operation, i.e., the excitation signal is modified by the sensor and stimulus to produce the output signal. Usually requires more wires to supply power. Example: thermistor
• Self-‐generating (passive): generate an electrical signal in response to an external stimulus, i.e., the input energy is converted by the sensor into output energy without the need for an additional power source. Examples: thermocouple, piezoelectric sensor. 21 Sensor Classification
2. Nature of output signal
• Analog: output changes in a continuous way at a macroscopic level in both its magnitude and temporal or spatial content (most of the physical measurands are analog in nature)
Examples: temperature, displacement, light intensity.
• Digital: output changes in discrete steps or states. Digital sensors do not require an ADC, and their output are more repeatable, reliable and easier to transmit.
Examples: position encoders, contact switch 3. Input/output relationship
• Zero, first, second, or higher order, usually related to the number of independent energy-‐
storing elements in the sensor, can lead to delay (not good for closed-‐loop systems). 22 Sensor Classification
4. Operating mode
• Deflection: sensor generates a response that is a deflection or a deviation from the initial condition of the instrument (the deflection is proportional to the measurand of interest)
Example: dynamometer, force to be measured deflects a spring to the point where the force exerts, proportional to its deformation.
• Null mode: sensor exerts an influence on the measured system so as to oppose the effect of the measurand (the influence and measurand are balanced through feedback until they are equal but opposite in value); usually more accurate, sensitive and does not require calibration, however slower response.
Example: Servo-‐accelerometer. 5. Physical measurement variable
• Mechanical, thermal, electrical, magnetic, radiant, chemical, etc. 23 General Input-‐Output Configuration
• Interfering and Modifying Inputs (Doebelin book):
• Sensors are usually sensitive to not only the quantity of interest; hence, the output signal might not be entirely due to the input signal.
• The desired signal, xs, passes through the gain, Gs, to output, y.
• Interfering inputs, xI, represents quantities to which the instrument is unintentionally sensitive, passes through the gain, GI, to output, y.
• Modifying inputs, xM, represents quantities that through GM,S causes a change in GS for the desired signal and through GM,I causes a change in GI
for interfering inputs. The gains, G, can be linear, nonlinear, varying, or random.
• Example: strain gages – operate on the basis of variation in the electric resistance of a conductor/semiconductor when stressed. However, temperature change also yield a resistance variation (xI with gain GI) and an electronic amplifier, GS, is usually required to measure the resistance change. The temperature variation can also act as a modifying input, xM, modifying GS through GM,S .
24 General Input-‐Output Configuration
• Compensation Techniques:
• Best approach is to design systems insensitive to interference and that respond only to the desired signals.
Example: use strain gages with low temperature coefficient (GI = 0).
• Negative feedback: commonly used to reduce the effect of modifying inputs.
( ) 1 + ( ) ( )
where the approximation is valid when () >> 1.
• Filtering: common compensation technique for interference reduction. Effective when frequency spectra of signals and interference do not overlap.
• Use of opposing inputs. Example, a gain that depend on a resistor (positive temperature coefficient) is placed in series with another resistor (negative temperature coefficient), commonly use in temperature compensation in strain gages, vibration in piezoelectric sensors.
• Smart sensors: uses the known mathematical relationship between interference and sensor output.
25 Static Characteristics
• How the sensor signal correctly represents the measured after a transient period. • Accuracy, Precision and Sensitivity
• Accuracy is how close the measured value is to the actual value. That is, the degree conformance between the measurand and the standard. Accuracy is the capacity of a measuring instrument to give Results close to the True Value of the measured quantity.
3 − 4
Accuracy = 1 −
where Xn is the nth measurand, Xi is the actual value.
• An error is the discrepancy between the true value for the measured quantity and the instrument reading.
Absolute error = Result − True value Absolute error
Relative error = True value
• Example: an angular displacement sensor ideally should generate 0.01mV per 0.001 radians angular displacement, i.e. the ideal sensitivity of the sensor is 10mV/rad. In experiment, an angular displacement of 1rad produced an output of 10.5mV, which represent 1.05rad in measured displacement. There is a 0.05rad error. Therefore, in a 1 rad range, the sensor’s absolute inaccuracy is +0.05rad, relative error is 26
5%. Static Characteristics
• Accuracy, Precision and Sensitivity
• Precision is the quality that characterizes the capability of a measuring instrument of giving the same reading when repetitively measuring the same quantity under the same prescribed conditions.
3 − E
Precision = 1 −
where Xn is the nth measurand, E is the mean value. It is a necessary but not sufficient condition for accuracy.
• Repeatability is the closeness of agreement between successive results obtained with the same method under the same conditions and in a short time interval.
• Reproducibility is the degree of coincidence between successive readings when the same quantity is measured with a given method, but in this case with a long-‐term set of measurements or with measurements carried out by different people, or performed with different instruments, or in different laboratories.
27 Static Characteristics
• Drifts and Sensitivity
• The natural tendency of a device, circuit, or system to alter its characteristics with time and environmental changes.
• Zero drift (DC offset) describes output variations when the input is zero, e.g. change in ambient conditions.
• Sensitivity drift (scale factor drift) describes the amount by which a device’s sensitivity of measurement varies as ambient conditions changes. It is the slope of the calibration curve. It is desirable to have a high and constant sensitivity.
• For a sensor whose output, , is related to the input, , by = f(), the sensitivity, H , at point, H , is H = K LML
N 28 Static Characteristics
• Describes the closeness between the calibration curve and a specified straight line. • The straight line is usually defined by the least square criterion. • The interest of linearity is that when sensitivity is constant, we only need to divide the reading by a constant value (the sensitivity) in order to determine the input.
• With microprocessor, a lookup table can be used to find the relationship, interpolation can be used to reduce the size of the table. 29 Static Characteristics
• Example: A common specification of linearity is the maximum deviation from a straight line expressed as percent of FS (Full Scale). Consider a sensor that outputs a voltage as a function of pressure from 0 to 100 psi with a linearity of 5% FS.
• Example: A sensor resistance changes linearly from 100 to 180Ω as temperature changes from 20oC to 120oC. Find a linear equation relating resistance and temperature. 30 Static Characteristics
• The main factors that influence linearity are resolution, threshold and hysteresis.
• Resolution, Threshold
• It is the smallest change of the input necessary to produce a detectable change at the output. It is usually expressed in terms of the smallest increment sensed. The higher the resolution, the smaller the increment it is able to measure. When the input increment is from zero, it is called the Threshold.
• Example, for a sensor giving a digital output, the smallest change in output signal is 1 bit. • Hysteresis
• It refers to the difference between two output values that correspond to the same input, depending on the direction (increasing or decreasing) of successive input values.
• Example, magnetizing and demagnetizing, heating and cooling process 31 Static Characteristics
• Systematic Errors
• An error is systematic when measuring the same value of a given quantity under the same conditions, it remains constant in absolute value and sign or varies according to a definite law when measurement conditions change.
• Systematic errors yield measurement bias.
• Error caused by instruments, methods, users, mechanical and electrical, constant and known.
• Systematic errors can be corrected with calibration.
• Example: to measure voltage drop across a resistor. Two approaches, 1) use a voltmeter whose accuracy is 0.1% of the reading; 2) use an ammeter whose accuracy is 0.1% of the reading and applying Ohm’s law. Given the resistor has 0.1% tolerance, which is more accurate? 32 Static Characteristics
• Random Errors
• Random errors (noise) are those that remain after eliminating the causes of systematic errors. They appear when the same value of the same quantity is measured repeatedly, using the same instrument and the same method. • True random errors (white noise) follow a Gaussian distribution.
• Sources of randomness:
• Repeatability of the measurand itself (e.g. height of a rough surface)
• Environmental noise (e.g. background noise picked up by a microphone)
• Transmission noise (e.g. 60Hz) ≫ 1
• Signal to noise ratio (SNR) should be • When taking the mean of several readings, random errors cancel
and only systematic errors remain. 33 Static Characteristics
• A dynamic range of the stimuli, which may be converted by a sensor, is called span, or input full scale (FS), or range. Span represents the highest possible input, which can be applied to the sensor without causing unacceptably large inaccuracy. • Calibration
• Sensor accuracy is determined through static calibration. The input is changed very slowly, thus taking successive constant values along the measurement range. The plot of output against input values forms the calibration curve.
• Each value of the input quantity must be known, measurement standards are such known quantities. Their values need to be at least 10 times more accurate than that of the sensor to be calibration.
• It provides a relationship between the physical measurement input (X) and the signal output (S)
• How to calibrate?
Most instruments are provided with a means of adjusting the zero and span of the instrument.
1. Zero adjustment is used to produce a parallel shift of the input-‐output curve
2. Span adjustment is used to change the slope of the input-‐output curve
34 Static Characteristics
• The largest change in the quantity to be measured to which the output does not change, or the range of input to which there is no output. Dead-‐zones arise as a result of static friction (stiction), or hysteresis. • Saturation
• Further increase in stimulus does not produce a desirable output. It is said that sensor has reached the span-‐end nonlinearity or saturation. 1 0.5 0.8 0.4 0.6 0.3 0.4 0.2 0.2 0.1 0 0 -0.2 -0.1 -0.4 -0.2 -0.6 -0.3 -0.8 -1 -0.4 0 1 2 3 4 5 6 7 8 9 -0.5 10 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 -0.8 -0.8 -1 0 -1 0 1 2 3 4 5 6 7 8 9 10 • Lag/Time-‐delay
• It refers to a delay in the change in the output from a sensor when ...
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