Dimensionless frequency offset values can be converted to units of frequency Hz if the nominal frequency is known.. To illustrate this, consider an oscillator with a nominal frequency of
Trang 1Dimensionless frequency offset values can be converted to units of frequency (Hz) if the nominal frequency is known To illustrate this, consider an oscillator with a nominal frequency of 5 MHz and a frequency offset of +1.16 × 10-11 To find the frequency offset in hertz, multiply the nominal frequency
by the offset:
(5 × 106) (+1.16 × 10-11)=5.80 × 10-5=+0.0000580 Hz Then, add the offset to the nominal frequency to get the actual frequency:
5,000,000 Hz + 0.0000580 Hz = 5,000,000.0000580 Hz
Stability
Stability indicates how well an oscillator can produce the same time or frequency offset over a given time
interval It doesn’t indicate whether the time or frequency is “right” or “wrong,” but only whether it stays the same In contrast, accuracy indicates how well an oscillator has been set on time or on frequency To
understand this difference, consider that a stable oscillator that needs adjustment might produce a frequency with a large offset Or, an unstable oscillator that was just adjusted might temporarily produce
a frequency near its nominal value Figure 17.7 shows the relationship between accuracy and stability
Stability is defined as the statistical estimate of the frequency or time fluctuations of a signal over a given time interval These fluctuations are measured with respect to a mean frequency or time offset
Short-term stability usually refers to fluctuations over intervals less than 100 s Long-term stability can
refer to measurement intervals greater than 100 s, but usually refers to periods longer than 1 day
Stability estimates can be made in either the frequency domain or time domain, and can be calculated from a set of either frequency offset or time interval measurements In some fields of measurement, stability is estimated by taking the standard deviation of the data set However, standard deviation only
Trang 2Dimensionless frequency offset values can be converted to units of frequency (Hz) if the nominal frequency is known To illustrate this, consider an oscillator with a nominal frequency of 5 MHz and a frequency offset of +1.16 × 10-11 To find the frequency offset in hertz, multiply the nominal frequency
by the offset:
(5 × 106) (+1.16 × 10-11)=5.80 × 10-5=+0.0000580 Hz Then, add the offset to the nominal frequency to get the actual frequency:
5,000,000 Hz + 0.0000580 Hz = 5,000,000.0000580 Hz
Stability
Stability indicates how well an oscillator can produce the same time or frequency offset over a given time
interval It doesn’t indicate whether the time or frequency is “right” or “wrong,” but only whether it stays the same In contrast, accuracy indicates how well an oscillator has been set on time or on frequency To
understand this difference, consider that a stable oscillator that needs adjustment might produce a frequency with a large offset Or, an unstable oscillator that was just adjusted might temporarily produce
a frequency near its nominal value Figure 17.7 shows the relationship between accuracy and stability
Stability is defined as the statistical estimate of the frequency or time fluctuations of a signal over a given time interval These fluctuations are measured with respect to a mean frequency or time offset
Short-term stability usually refers to fluctuations over intervals less than 100 s Long-term stability can
refer to measurement intervals greater than 100 s, but usually refers to periods longer than 1 day
Stability estimates can be made in either the frequency domain or time domain, and can be calculated from a set of either frequency offset or time interval measurements In some fields of measurement, stability is estimated by taking the standard deviation of the data set However, standard deviation only
Trang 3Sensor and Actuator
Characteristics
18.1 Range
18.2 Resolution
18.3 Sensitivity
18.4 Error
18.5 Repeatability
18.6 Linearity and Accuracy
18.7 Impedance
18.8 Nonlinearities
18.9 Static and Coulomb Friction
18.10 Eccentricity
18.11 Backlash
18.12 Saturation
18.13 Deadband
18.14 System Response
18.15 First-Order System Response
18.16 Underdamped Second-Order System Response
18.17 Frequency Response
Mechatronic systems use a variety of sensors and actuators to measure and manipulate mechanical, electrical, and thermal systems Sensors have many characteristics that affect their measurement capa-bilities and their suitability for each application Analog sensors have an output that is continuous over
a finite region of inputs Examples of analog sensors include potentiometers, LVDTs (linear variable differential transformers), load cells, and thermistors Digital sensors have a fixed or countable number
of different output values A common digital sensor often found in mechatronic systems is the incremental encoder An analog sensor output conditioned by an analog-to-digital converter (ADC) has the same digital output characteristics, as seen in Fig 18.1
18.1 Range
The range (or span) of a sensor is the difference between the minimum (or most negative) and maximum inputs that will give a valid output Range is typically specified by the manufacturer of the sensor For example, a common type K thermocouple has a range of 800∞C (from -50∞C to 750∞C) A ten-turn potentiometer would have a range of 3600degrees
Joey Parker
University of Alabama
Trang 4Sensors
19.1 Linear and Rotational Sensors
Contact • Infrared • Resistive • Tilt (Gravity) • Capacitive • AC Inductive • DC Magnetic • Ultrasonic • Magnetostrictive Time-of-Flight • Laser Interferometry 19.2 Acceleration Sensors
Overview of Accelerometer Types • Dynamics and Characteristics of Accelerometers • Vibrations • Typical Error Sources and Error Modeling • Inertial
Accelerometers • Electromechanical Accelerometers • Piezoelectric Accelerometers • Piezoresistive Accelerometers • Strain-Gauge Accelerometers • Electrostatic Accelerometers • Micro- and Nanoaccelerometers • Signal Conditioning and Biasing 19.3 Force Measurement
General Considerations • Hooke’s Law • Force Sensors 19.4 Torque and Power Measurement
Fundamental Concepts • Arrangements of Apparatus for Torque and Power Measurement • Torque Transducer Technologies • Torque Transducer Construction, Operation, and Application • Apparatus for Power Measurement 19.5 Flow Measurement
Introduction • Terminology • Flow Characteristics • Flowmeter Classification • Differential Pressure Flowmeter • The Variable Area Flowmeter • The Positive Displacement Flowmeter • The Turbine Flowmeter • The Vortex Shedding Flowmeter • The Electromagnetic Flowmeter • The Ultrasonic Flowmeter • The Coriolis Flowmeter • Two-Phase Flow • Flowmeter
Installation • Flowmeter Selection 19.6 Temperature Measurements
Introduction • Thermometers That Rely Upon Differential Expansion Coefficients • Thermometers That Rely Upon Phase Changes • Electrical Temperature Sensors and Transducers • Noncontact Thermometers • Microscale Temperature Measurements • Closing Comments 19.7 Distance Measuring and Proximity Sensors
Distance Measuring Sensors • Proximity Sensors 19.8 Light Detection, Image, and Vision Systems
Introduction • Basic Radiometry • Light Sources • Light Detectors • Image Formation • Image Sensors • Vision Systems
19.9 Integrated Microsensors
Introduction • Examples of Micro- and Nanosensors • Future Development Trends • Conclusions
Kevin M Lynch
Northwestern University
Michael A Peshkin
Northwestern University
Halit Eren
Curtin University of Technology
M A Elbestawi
McMaster University
Ivan J Garshelis
Magnova, Inc.
Richard Thorn
University of Derby
Pamela M Norris
University of Virginia
Bouvard Hosticka
University of Virginia
Jorge Fernando Figueroa
NASA Stennis Space Center
H R (Bart) Everett
Space and Naval Warfare Systems Center
Stanley S Ipson
University of Bradford
Chang Liu
University of Illinois