sensors · 2018. 11. 26. · transducer: changes one form of energy to another measurement chain...
TRANSCRIPT
Sensors
Francesco Ferrante, PhD
Department of Physics, Engineering, Earth, and Environmental Sciences, and
Mechanics
Grenoble Image Parole Signal Automatique
Université Grenoble Alpes
Control Systems Technologies
—–
Graduate Course
Master in Systems, Control, and Information Technologies
—–
September 10, 2018
Université Grenoble Alpes - 1/28
Outline
1. Generalities on Sensors
2. Temperature Sensors
3. Position Sensors
4. Accelerometers
Université Grenoble Alpes - 2/28
Outline
1. Generalities on Sensors
2. Temperature Sensors
3. Position Sensors
4. Accelerometers
Université Grenoble Alpes - 3/28
Sensors
◮ Sensor: a device that responds to a physical stimulus andconverts it into a signal
◮ Transducer: changes one form of energy to another
Measurement chain
ConditioningSensor Transducer
MeasurementPhysical stimulus Physical Signal Electrical Signal
◮ Temperature, Flow, Mass, Level (Process Control)
◮ Position, speed, acceleration (Motion Control)
Variables of interest in Control Systems
Université Grenoble Alpes - 4/28
Sensors
◮ Sensor: a device that responds to a physical stimulus andconverts it into a signal
◮ Transducer: changes one form of energy to another
Measurement chain
ConditioningSensor Transducer
MeasurementPhysical stimulus Physical Signal Electrical Signal
◮ Temperature, Flow, Mass, Level (Process Control)
◮ Position, speed, acceleration (Motion Control)
Variables of interest in Control Systems
Université Grenoble Alpes - 4/28
Characteristics of a Sensor
◮ accuracy: agreement ofmeasured values with a givenreference
◮ repeatability: capability ofreproducing as output similarmeasured values for differentmeasurements of the samequantity
◮ stability: for a given inputyou always get the sameoutput
◮ range: the limits betweenwhich the measured inputcan vary.
◮ resolution: minimal changeof the input necessary toproduce a detectable changeat the output
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Characteristics of a Sensor◮ sensitivity: variation of the
output signal over variationof the input (sensor gain)
◮ frequency response:
dynamics of the sensor fromDC to the highestmeasurable frequency
◮ calibration curve:
relationship between themeasurand and the signalgenerated by the sensor
◮ linearity: the closeness ofthe calibration curve to aspecified straight line
◮ Rangeability=20:1
◮ fullscale =100 kg s−1
◮ accuracy= 1%Provides measurements with an accuracy of 1% within the range[5, 100]kg s−1
Example-Rangeability
◮ Hysteresis: differencebetween output readings forthe same measurand,depending on the trajectory
x (input)
y (output)
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Calibration
1. supply the sensor with a set of n samples xi
2. for each xi, repeat the measurement mi times to getyi,1, yi,2, . . . , yi,mi
3. for each i = 1, 2, . . . , n take the average yi =∑mi
j=1 yi,j
4. select a curve from a specific class, e.g., linear, polynomial, etc
5. get the best fitting parameters to interpolate{(x1, y1), (x2, y2), . . . , (xn, yn)} (least squares algorithm).
lin
explog
x
y
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Dynamic Characteristics
Sensors are considered static but actually have their own dynamics!
hA(T∞ − T (t)) = mcdT (t)
dt
where h is the convection coefficient, A is the surface area of the sensor,T is the temperature, m is the TC mass, and c is the heat capacity.
Example:Thermocouple
Therefore, it is important to define dynamical parameters to fullycharacterize a sensor.
◮ The response time: the time it takes for the output tosettle, e.g., at 95% of the value of the input.
◮ The rise time: the time taken for the output to rise to somespecified percentage of the steady-state output.
◮ The settling time: the time taken for the output to settle towithin some percentage of the steady-state value.
Université Grenoble Alpes - 8/28
Outline
1. Generalities on Sensors
2. Temperature Sensors
3. Position Sensors
4. Accelerometers
Université Grenoble Alpes - 9/28
Thermocouples
◮ Input: Temperature difference
◮ Output: Voltage
◮ Supply: Self-supplied (thermoelectric)
◮ Characteristic: Nonlinear
Some numbers...
◮ Temperature range: −200÷ 2760◦C
◮ Output Voltage: −10÷ 50 mV
◮ Sensitivity: 10÷ 50 µV ◦C−1
Université Grenoble Alpes - 10/28
Thermocouples
Thomas Johann Seebeck(Tallinn 1770–Berlin1831).
In 1821, T. J. Seebeck noticed that atemperature gradient induces an electricalvoltage gradient.
Seebeck Effect
◮ S Seebek coefficient
M1
T2T1
Vg(T1, T2)
Vg(T1, T2) =
∫ T2
T1
S(T )dT
Université Grenoble Alpes - 11/28
Thermocouples
Thomas Johann Seebeck(Tallinn 1770–Berlin1831).
In 1821, T. J. Seebeck noticed that atemperature gradient induces an electricalvoltage gradient.
Seebeck Effect
◮ S Seebek coefficient
M1M2
T2
T1
V1V2
E
E(T1, T2) =
∫ T2
T1
(S1(T )−S2(T ))dT
Vg is hard to measure, instruments will perturb the structure of thesensor.
Université Grenoble Alpes - 11/28
Thermocouples–Two junctions
To solve the problem, two junctions at different temperatures areused
Tc Th
V2V2
V1
M1
M2M2
E
E(Th)|Tc= V2(Tc, Th)− V1(Tc, Th) → Why?
◮ the function E(Th) is reported in the data sheets, usually forTc = 0 ◦C
◮ what if Tc = T c 6= 0 ◦C?E(Th)|Tc=T c
= E(Th)|Tc=0 − E(T c)|Tc=0
Université Grenoble Alpes - 12/28
Thermocouples–Two junctions
To solve the problem, two junctions at different temperatures areused
Tc Th
V2V2
V1
M1
M2M2
E
E(Th)|Tc= V2(Tc, Th)− V1(Tc, Th) → Why?
◮ the function E(Th) is reported in the data sheets, usually forTc = 0 ◦C
◮ what if Tc = T c 6= 0 ◦C?E(Th)|Tc=T c
= E(Th)|Tc=0 − E(T c)|Tc=0
Université Grenoble Alpes - 12/28
ThermistorsA thermistor is a type of resistor whoseresistance is dependent on temperature.
◮ NTC thermistors. Resistancedecreases as temperature rises.
◮ NTC thermistors. Resistanceincreases as temperature rises.
The relationship between resistance and temperature can beconsidered approximatively linear
R(T ) = R(T 0) + α(T − T 0)Vc
Rn
R(T )R(T )R(T )+Rn
Vc
Université Grenoble Alpes - 13/28
ThermistorsA thermistor is a type of resistor whoseresistance is dependent on temperature.
◮ NTC thermistors. Resistancedecreases as temperature rises.
◮ NTC thermistors. Resistanceincreases as temperature rises.
The relationship between resistance and temperature can beconsidered approximatively linear
R(T ) = R(T 0) + α(T − T 0)Vc
Rn
R(T )R(T )R(T )+Rn
Vc
Not the same as the “resistance temperature detectors” (RTDs).Look for the differences!
Université Grenoble Alpes - 13/28
Overview on Temperature Sensors
Thermocouple Thermistor
Pro
◮ Self-excitable
◮ Robust
◮ Cheap
◮ Covers a widerange of temp.
◮ Very sensitive
◮ Fast
◮ Two-wiresmeasurement
Cons
◮ Nonlinear
◮ Low outputvoltage (noiseprone)
◮ TemperatureRef.
◮ Low sensitivity
◮ Nonlinear
◮ Limited range
◮ Current Ref.
◮ Self-heating
Université Grenoble Alpes - 14/28
Overview on Temperature Sensors
Thermocouple Thermistor
Pro
◮ Self-excitable
◮ Robust
◮ Cheap
◮ Covers a widerange of temp.
◮ Very sensitive
◮ Fast
◮ Two-wiresmeasurement
Cons
◮ Nonlinear
◮ Low outputvoltage (noiseprone)
◮ TemperatureRef.
◮ Low sensitivity
◮ Nonlinear
◮ Limited range
◮ Current Ref.
◮ Self-heatingLook out there and tell us how good and bad are “resistance tem-perature detectors” (RTDs).
Université Grenoble Alpes - 14/28
Outline
1. Generalities on Sensors
2. Temperature Sensors
3. Position Sensors
4. Accelerometers
Université Grenoble Alpes - 15/28
Position Sensors
Provide an electrical signal proportional to the displacement of abody with respect to a reference position
◮ linear displacements: potentiometers
◮ angular displacements: potentiometers, resolvers, syncros, andencoders (digital)
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Absolute Encoders
Provides an encoded version of the absolute position.The most used absolute encoder is the optical encoder.
◮ The optical encoder iscomposed by:◮ a disc is made of glass or
plastic with transparentand opaque areas;
◮ a light source;◮ a photo detector array
that reads the opticalpattern that results fromthe disc’s position
Position is encoded via Gray code. Why?
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Absolute Encoders and Gray Code
Frank Gray. 13 September1887–23 May 1969. (Belllabs)
◮ In Binary Code, bit switchingmay not take placesimultaneously
◮ this phenomenon may leadto errors in the decodingprocess
◮ This phenomenon cannotoccur with a Gray coding
◮ Adjacent cells differ only byone bit
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Incremental Encoder
Output is a pulse signal that is generated when the transducer diskrotates
◮ The (optical) incrementalencoder is composed by:◮ a disc is made of glass or
plastic with three tracksof transparent and opaqueareas;
◮ a light source;◮ a photo detector
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Incremental Encoder◮ At each complete rotation, tracks
A and B generate Nr pulses◮ tracks A and B (channels) are in
quadrature. This enables rotationdirection detection;
◮ track Z is used to define thereference position, in case useful.
Typical number of ticks per revolutionNr = 100 ÷ 5000
Resolution of the encoder= 2πNr
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Incremental Encoder-Signal Processing Issues
Different approaches to decode
◮ 1X decoding: Trigger on risingeither falling edges of one channel.Nr pulses tour.
◮ 2X decoding: Trigger on risingeither falling edges of both channelsA and B. 2Nr pulses tour.
◮ 4X decoding: Trigger on rising andfalling edges of both channels Aand B. 4Nr pulses tour.
A
B
Z
U/DD Q
clkclk
clear
Simple hardware 1Xdecoding.
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Incremental Encoder-4X decoding
Finite-state machine
Ch. A Ch. BState 0 1 1State 1 1 0State 2 0 1State 3 0 0
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Speed Estimation
Encoders can be used to estimate angular speed. Differentmethods:◮ Numerical derivative: One step Euler approximation.
θ[k] = θ(kT ) ≈1
T(θ[k]− θ[k])
can be improved by considering finer higher orderapproximations of the derivative. Very noisy!
◮ Filtering: One assumes that
[θ[k]
θ[k]
]
=
[1 T0 1
]
︸ ︷︷ ︸
A
[θ[k − 1]
θ[k − 1]
]
︸ ︷︷ ︸
x[k−1]
+ v︸︷︷︸
noise
θ[k] =[1 0
]
︸ ︷︷ ︸
C
[θ[k]
θ[k]
]
+ w︸︷︷︸
noise
Then a (causal) estimator is designed to reconstruct the missingstate x2 = θ.
Université Grenoble Alpes - 23/28
Speed Estimation
Encoders can be used to estimate angular speed. Differentmethods:◮ Numerical derivative: One step Euler approximation.
θ[k] = θ(kT ) ≈1
T(θ[k]− θ[k])
can be improved by considering finer higher orderapproximations of the derivative. Very noisy!
◮ Filtering: One assumes that
[θ[k]
θ[k]
]
=
[1 T0 1
]
︸ ︷︷ ︸
A
[θ[k − 1]
θ[k − 1]
]
︸ ︷︷ ︸
x[k−1]
+ v︸︷︷︸
noise
θ[k] =[1 0
]
︸ ︷︷ ︸
C
[θ[k]
θ[k]
]
+ w︸︷︷︸
noise
Then a (causal) estimator is designed to reconstruct the missingstate x2 = θ.
Kalman Filter
x[k] = Ax[k − 1] + L[k]︸︷︷︸
Kalman Gain
(θ(k)− CAx(k − 1))
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Outline
1. Generalities on Sensors
2. Temperature Sensors
3. Position Sensors
4. Accelerometers
Université Grenoble Alpes - 24/28
Accelerometers
Provide Acceleration measurements based on inertia.
Mechanical energy is converted into an electrical signal. Differenttechnologies:
◮ Piezoelectric: no supply, good linearity, and high bandwidth;◮ piezoresistive : supply is needed, detect static acceleration,
i.e., “gravity”◮ capacitive: large bandwidth, low cost, poor resolution◮ micro electro-mechanical systems. The new Era!
Université Grenoble Alpes - 25/28
Accelerometers
◮ u body acceleration
◮ x relative position ofthe seismic mass
◮ M mass of theseismic mass
◮ K stiffness
◮ b damping factor
Mu = Mx+Kx+ bx
Assumingum = κy
the transfer function of the sensor is
Um(s)
U(s)= F (s) =
κ
s2 + (b/M)s +K/M
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Accelerometers-Frequency Analysis
F (s) =κ
s2 + (b/M)s +K/M ωr =
√
K
M= 2πFr ζ =
b
2
√
1
KM
Université Grenoble Alpes - 27/28
Homework
Carry out a little research on:Pressure, level detector, and flow sensors.Generate a small report including, principles of operation, type ofoutput signal, circuitry needed to interface with a PLC, the moredetailed the better! Due in two weeks, i.e., October 24.
Université Grenoble Alpes - 28/28