CHAPTER 1
INTRODUCTION
ME 365 is the first course in a three course sequence in the undergraduate curriculum,
designed to acquaint you with systems, measurements and control. The other courses are ME 375
and ME 475. ME 365 is devoted to measurement systems, and covers the basic principles of the
art of making good measurements. At the end of the course you should have a reasonable grasp
on what you need to look for when putting a measurement system together, and how to calibrate
the system to make sure that it meets all of your (and your boss's) specifications. In this course
you will only make some fairly simple mechanical measurements of temperature and motion,
primarily displacement. However, the basic principles that you learn will have relevance to all
measurements that you are likely to make.
In making measurements of mechanical systems you will be taking some mechanical
quantity, e.g., temperature, displacement, or flow rate, and creating a voltage that is proportional
to it. Hopefully in doing so you will not change the process whose response you are trying to
measure. This voltage can then be measured with a voltmeter, a digital multimeter, an
oscilloscope or a computer equipped with analog to digital conversion boards. As such, you will
be dealing with electrical circuits, even though you are actually interested in mechanical
phenomena. Some Mechanical Engineering students feel uncomfortable with this; after all did
you not choose ME in preference to EE? However, lets think about mechanical systems we are
familiar with, e.g., automobiles, machines that most ME's are very interested in. Open up the
hood, there we see both electrical and mechanical systems working together. Learning about
how to make measurements of mechanical phenomena will be your first step in learning about
electro-mechanical systems.
Why Take Measurements?
First let’s try to answer the question, why do you, as a mechanical engineer, need to
understand the basics of measurements? I am assuming that as a mechanical engineer you are
interested in studying mechanical systems. You want to be able to design better machines, fix
machines that don't work, and not design machines that have catastrophic failures resulting in
injury or death. You must therefore have tools available to you to help you do this. An ability to
take measurements is one of these tools.
When we are interested in the behavior of a machine or its component parts, we use our
understanding of the physics that governs the behavior of the components, make some
assumptions, and then derive a set of governing equations. Simulating or solving these equations
allows us to examine how parameters, e.g. material properties, affect the nature of the response
of the machine. In machine design, we can use our knowledge of the conditions under which the
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machines will operate and the desired response to guide our choice of the material properties. We
use the models to optimize our parameter choices.
How good are these models? Does the machine really behave like the model, or did those
assumptions we made invalidate the model? What we do, to see if our model is any good, is
drive the system with a known input (e.g., a force to one part of a mechanism) and we observe
the response (the displacement of one part of the mechanism). Actually we would like to
measure the displacement and compare the values to the output of the model. So the first reason
for making measurements is to validate our theoretical models, and to improve our understanding
of the machine behavior.
Secondly, the model contains parameters related to material or component properties. Some
of these may be available from look-up tables, many need to be measured. One common
example of a property that has to be measured is the damping in a system. Damping can be
caused by viscous effects, a body moving in a fluid, or by dry friction, surfaces rubbing against
one another.
Sometimes we are unable, due to constraints beyond our control or our inability to model the
machine behavior accurately, to achieve the desired behavior. We then monitor the machine
output and adjust the input to achieve the desired output; this is feedback control. People do
feedback control all the time, e.g., we drive a car, notice that the car is veering to the right and
adjust the steering accordingly. We use our eyes to measure the response of the car and adjust the
input (the steering) to get the desired car motion. When we get home we also try to fix the
problem. We learn about the design of feedback controllers for machines in ME 475. So here
are two more reasons for making measurements: to measure the response for control, and to
monitor and identify faults.
It is still a good idea for the mechanical engineer to be involved in the measurement part of
the system, even though that part involves electrical components. Experience tells us that in
most cases an understanding of the mechanical systems being controlled or monitored is
essential for effective control. It is not always clear where one should measure the response of a
machine to yield the maximum information. It is possible to measure in a poor location that does
not allow you to observe and control the phenomenon that you are interested in. Understanding
the mechanical system behavior will help you determine the best measurement locations.
In component manufacture, one would like to be able to detect and reject faulty components
automatically. Measurements of component geometry and correct machine or component
operation are a part of most machinery assembly lines. Time constraints mean that it is almost
impossible to test every complex piece of machinery leaving a factory. The design of fast and
reliable tests to detect faults in complex mechanical machinery is of interest to most
manufacturers. What to measure, where to measure in the machine, and how to process the
signals to get the information required is still very much in the hands of the engineer who
understands how the machine works.
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The Many Facets of Measurements
Earlier in the chapter I referred to the art of measurements. This is because there are many
options available when assembling a measurement system and it is possible to design different
systems that will achieve the same objectives. The design of a good measurement system is an
iterative process, partly because initially you will probably not have all the information that you
require for the design, especially if the quantity that you are measuring has not been measured on
this particular machine before. A problem often encountered is that you cannot measure at the
apparently optimum location because of safety issues, inaccessibility, or compromising of
machine operation. You may have the choice of a highly expensive instrument that requires
machine modification but yields excellent signals, or a cheaper instrument that produces noisy
signals and further processing will be needed to yield the required information. You may also
have a choice of how to measure the quantity you are interested in, e.g., you can measure
displacement with a potentiometer, a strain gage, an optical probe or by integrating an
accelerometer signal twice. Factors such as robustness, system cost and level of operator
understanding required to run the system will enter into your design decisions.
One problem with measurements is that you may repeat a measurement and not get the same
answer. Environmental effects such as temperature and humidity changes and fluctuations in the
power supply can affect the operation of measurement systems, and also the system you are
taking measurements from. High voltage lines close by can also introduce measurement noise.
So how do you report, e.g., the temperature of the machine under specific operating conditions, if
your answer is different every time you repeat the measurement? Hopefully the measurements
are close to one another, else you will have to redesign the measurement system or think more
about other factors that affect the machine output, in addition to those given in the specified
operating conditions. Chapter 4 is an introduction to statistics, and contains a description of how
to discuss and report measurements that have some randomness associated with them.
Having produced a voltage that is proportional to the quantity that we wish to measure, how
do we measure the voltage? We will, as in industry, often use a computer or a microprocessor
with analog to digital converters (ADC) attached to make the measurement. These data
acquisition systems are often set up to take measurements every so many seconds (T seconds).
Inside the computer information is stored in coded form, e.g., as a 16 bit binary code. How do
these codes relate to the voltage we wish to measure? If we only take samples every T seconds,
what about all the information in the signal between the samples? Did we lose anything in this
sampling process? If the signal does not change, probably not. If it does, maybe. This is
discussed in Chapter 3.
If the quantity that we are trying to measure is continually changing, will the voltage that we
are measuring be able to follow this continually changing quantity? If the changes speed up, at
what point will the voltage fail to keep up with the changes? This is like talking to someone who
does not speak the same language. If you talk to one another slowly then you may be able to
translate fast enough to keep up with the conversation, whereas if the person speaks quicker you
may not be able to absorb information given at such a high rate (a bit like sitting in some
lectures, no doubt). At a particular information rate or frequency (words per second) you start to
lose the ability to process all the information, you can think of this as your cut-off frequency.
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Measurement systems also have cut-off frequencies. In fact, many measurement system
components, e.g., piezoelectric accelerometers, also do not respond well if signals are fluctuating
too slowly, perhaps we can make the analogy with the lecture pace being so slow that everyone
goes to sleep! In order to assess the ability of the measurement system to follow low and high
frequency fluctuations, we generate frequency response plots. These are plots of how the
amplitude and phase of a sine wave changes as a function of its frequency, when the sine wave is
passed through the system. We track the behavior of a sine wave because its a simple example
of a fluctuating signal. The higher the frequency of the sine wave, the faster the fluctuations. The
shape of the frequency response plots can also be used to identify the type of differential
equation model that describes the system, and can be used to estimate system parameters such as
natural frequencies, damping ratios, time constants and sensitivity. System identification for
simple systems is discussed in Chapter 6.
It is also possible to express more complicated signals as a sum of sines and/or cosines
(Fourier Series, Chapter 8). If we assume that the systems we are studying are behaving linearly,
we can take each sine in the sum and pass it though the system. The frequency response function
will tell us how the amplitude and phase of the sine will change as it passes through the
measurement system. We can then take the responses due to each of the sines and add them up to
predict the measurement system's response to a complicated signal.
Frequency analysis of measurements is often performed to tell us something about the system
that we are observing. A high response in a particular frequency region may be related to
physical characteristics of the system. For example, in noise control it is important to understand
which frequencies are strongest because the noise control strategy will depend on the type of
noise. Higher frequency noise can be controlled by using linings made of materials such as foam
or fiberglass; these materials are used to line airplane fuselages. Low frequency noise would be
relatively unaffected by such treatments. Fourier series analysis, as described in Chapter 8, is the
basis of the frequency analysis techniques widely used in industry.
Measurement systems are made up of many components. For example a sound measurement
system may consist of a microphone, a preamplifier, a measurement amplifier, a filter and a
spectrum analyzer. Each component will have its own frequency response characteristics.
Ideally, the frequency response of the whole measurement system is found by multiplying the
frequency response functions of the individual components together (that is complex
multiplication: multiplying the amplitude changes and adding the phase changes). However, this
will only give the correct result if very little current flows through the connection circuits. In
reality some current flows, and in poorly designed systems a lot of current flows. This results in
a much more complicated expression for determining the frequency response function for the
entire measurement system. This problem is called loading and is described in Chapter 7.
We use operational amplifier circuits to help stop this loading problem. They are also used in
filter design and controller design (ME 475). Any system can be referred to as a filter since it
will remove, i.e., filter out, some frequencies. However, in measurements the term filter is
usually applied to a specific type of instrument that is used to remove either high or low
frequency noise or a combination of both. Some amplifiers have built in filters that remove both
low and high frequency noise (AC Amplifiers). Noise is a term that is used to describe any
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component of a signal that we did not want to measure. Unfortunately noise is always present in
measurements, a good measurement is one where the level of the noise is small compared to the
level of the signal we wish to measure. In Chapter 11 the sources of noise in measurement
systems, and how we may reduce them, are described. Filters are used to remove noise
components that have frequencies that are not present in the desired signal, i.e., the information
we wish to measure. We can design filters using op-amp circuits; this is described in Chapter 7.
Your choice of measurement system components will be limited by what is available within
your cost range. Sometimes these components will not combine to give you the desired
frequency response characteristics. Hence for you to measure all the frequencies you wish, you
will have to modify the system. You can do this by designing system compensators, or by using
feedback. Feedback is often used in vibration experiments. Shakers attached to structures
interact with them and it is difficult to maintain a constant force into the structure as the signal
driving the shaker changes frequency. To deal with this problem the force signal is fed back into
the signal generator and used to adjust the input to the shaker to maintain a constant force level
into the structure. Feedback control is treated in depth in ME 475 and introduced at the end of
ME 375.
Electronic instrumentation is often plagued with low frequency noise problems (Chapter 11).
Because of this, many measurement systems are designed to avoid taking measurements in this
low frequency region. However, the quantity that you are measuring may well be fluctuating at a
very slow rate. Instrumentation is designed to move this low frequency information to a higher
frequency region. Electronic instrumentation can then be used to amplify the signal and filters
used to remove the low frequency noise the amplifiers introduce. This process of moving a
signal's frequency content is called modulation and is described in Chapter 10. After the noise
has been removed, the signal is demodulated (shifted back to the original low frequency region)
just prior to being recorded. Examples of measurement systems that use modulation are ones
that include transducers whose impedance (resistance, capacitance or inductance) changes when
the quantity we wish to measure changes. To convert these impedance changes into voltage
changes we use bridge circuits (Chapter 10), which have a power supply. When the power
supply is AC, it has the effect of modulating the signal being measured.
Modulation is also used in signal transmission (e.g., AM and FM stations on your radio).
Many signals can be shifted to different frequency bands and transmitted at the same time. At the
receiver the signal is filtered into different frequency bands and each frequency band is
demodulated to retrieve the original signals. Some measurement systems use modulation to
transmit signals over distances where the use of long cables would lead to signal degradation and
the introduction of noise. Also, in rotating machinery connecting wires may be impractical. In
Chapter 10 amplitude modulation (AM) and demodulation is described in detail, along with the
mathematics of the modulation process. This chapter builds on the frequency analysis introduced
in Chapter 8.
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Summary
From reading this chapter, you should get the impression that to be able to do measurements
well you need to understand a lot of apparently diverse subjects. You will bring this information:
statistics, frequency analysis, system identification, system compensation, filtering, loading and
op-amp circuits, noise rejection, modulation and digital coding, together to make the best
measurements possible, given the constraints under which you are operating. Because of the
diverse nature of the problems you come across when taking measurements and your need to
understand them, this course is quite different in feel from other courses that you will take in
Mechanical Engineering. Sometimes it will seem that the subjects we study are not connected
and we will need to remind ourselves of the strong theme that runs through this course; every
subject you will learn about is relevant to making good measurements.
Measurement is also a unifying subject within Mechanical Engineering because all the areas
within the school are involved in measurements of some kind. Measurements to confirm
theoretical models, measurements to increase our understanding of mechanical systems,
measurements to determine material properties, measurements to control mechanical, thermal
and fluid systems, measurements to assess the effectiveness of design changes, measurements for
diagnostics and failure detection, and measurements for quality control. Good experimentalists
are rare and are a great asset to industry. This is your first step in gaining the required knowledge
that will enable you to become a first class experimentalist. Also, because there may be many
reasons why your measurements may not be good, you will certainly get to exercise and improve
your trouble shooting skills in this course, particularly in the Laboratory. This ability to logically
approach a problem, debug and solve it will be useful to you when facing other types of
problems.
As a lead in to the next chapter on static instrument characteristics, we must remind ourselves
that, in this course, we often assume that the measurement system that we design is linear. This
means that if we input a sine wave to the system of a particular frequency the output will be a
sine wave of the same frequency, and a gain in the input will lead to exactly the same gain in the
output. No real system will behave linearly for all inputs. There will be a range of inputs for
which the system is approximately linear, hopefully this coincides with the range of input values
you wish to measure! Part of the reason for instrument calibration (Chapter 2) is to determine
this linear range of operation, and to confirm manufacturers' specifications. As an instrument
ages it may drift away from the manufacturer's specifications and hence you will need to perform
a calibration every time that you start a new set of measurements. It will also serve to determine
whether the instrument is still usable. A lot of time can be wasted taking measurements with
broken instrumentation. Calibration takes time, but if the measurements are poor, how much use
are they to you?
So in ending this introduction I will give you the one golden rule of measurements. Calibrate
at the start of a series of measurements, calibrate at intervals during the series of measurements,
calibrate every time you return to your instrumentation set up (you will be amazed at how many
people cannot resist turning dials on instrumentation), calibrate at the end of the test to confirm
that everything is still working. How do you calibrate? Turn to Chapter 2.
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