Introduction to engineering experimentation 3rd edition pdf download

Introduction to engineering experimentation 3rd edition pdf download

Introduction to Engineering Experimentation (3rd Edition),What can I do?

WebThe book can serve as a reference for subsequent laboratory courses. Our introductory course in engineering experimentation is presented to all under­ graduate engineers in WebIntroduction to Engineering Experimentation 3rd edition We have solutions for your book! This problem has been solved: Problem 1P Chapter CH1 Problem 1P Step-by-step WebIntroduction To Engineering Experimentation 3rd Edition pdf Free Download Introduction to Engineering Experimentation, Third Edition provides a broad WebMeasurement and Control Basics 3rd Edition albert Ibañez Download Free PDF View PDF CHAPTER ONE INTRODUCTION TO INSTRUMENTATION john king Download Free Webdownload any of our books once this one. Merely said, the Introduction To Engineering Experimentation 3rd is universally compatible afterward any devices to read. ... read more

The flight data recorder in a commercial aircraft is a specialized data logger and provides important information after accidents. Recent automobiles often include a data logger that records a small amount of speed and other data just before and after an accident may be part of the airbag system. Many sys­ tems are commercially available, so a suitable solution for a particular application should be readily available. To take a data sample, for example, the following instructions must be executed: 1. Instruct the multiplexer to select a channel. Instruct the AID converter to make a conversion. Retrieve the result and store it in memory. In most applications, other instructions are also required, such as setting amplifier gain or causing a simultaneous sample-and-hold system to take data.

The software required depends on the application. Using selections from various menus, the operator can configure the program for the particular application. These programs can be configured to take data from transducers at the times requested, display the data on the screen, and use the data to perform required control functions. These systems are often con­ figured by technicians rather than engineers or programmers, so it is important that the software setup be straightforward. For complicated processing or control func­ tions, it is possible to include instructions programmed in a higher-level language such as C. There are a number of very sophisticated software packages now available for personal computer-based data-acquisition systems. These packages are very capable ­ they can take data, display it in real time, write the data to files for subsequent process­ ing by another program, and perform some control functions. The programs are configured for a particular application using menus or icons.

They may allow for the incorporation of C program modules. These software packages are the best choice for the majority of experimental situations. Instrumentation for Process Measurement and Control, Chilton, Radnor, PA. Intelligent Instrumentation, Prentice Hall, Englewood Cliffs, NJ. Instrumentation Reference Book, 3rd ed. Lab VIEW 8 Student Edition. Prentice Hall, Englewood Cliffs, NJ. AND MCCONNELL, K. Instrumentation for Engineering Measure­ ments, 2d ed. Design with Operational Amplifiers and Analog Integrated Circuits, McGraw-Hill, New York. IEEE Std IEEE Standard for Local and Met­ ropolitan Area Networks: Overview and Architecture. The Institute for Electrical and Electronic Engineers, Inc.

IEEE IEEE Standard for a High-Perfor­ mance Serial Bus. The Handbook ofPersonal Computer Instrumenta­ tion, Intelligent Instrumentation, Thcson, AZ. Introduction to Labview: 6-Hour Hands-On Tutorial. National Instruments Corporation, Austin, TX. Board-level systems set the trend in data acquisition, Computer Design, Apr. Analog Digital Conversion Handbook, Prentice Hall, Engle­ wood Cliffs, NJ. Semiconductor IC Data Book , AID, DIA Converters, Sony Corp, Tokyo, Japan. Computer-Based Data Acquisition, Instrument Society of America, Research 'ftiangle Park, NC. XYZs of Oscilloscopes, Tektronix, Beaverton, OR. Instrumentation for Engineers, Springer-Verlag, New York. PROBLE M S 4. Convert the decimal number to 8-bit simple binary. Convert the decimal number 1 to bit simple binary. Convert the decimal number to bit simple binary. Find the bit 2's-complement binary equivalent of the decimal number The number is an 8-bit 2's-complement number.

What is its decimal value? How many bits are required for a digital device to represent the decimal number 27, in simple binary? How many bits for 2's-complement binary? How many bits are required for a digital device to represent the decimal number 12, in simple binary? How many bits are required to represent the number in 2's-complement binary? A bit AID converter has an input range of ±8 V, and the output code is offset binary. Find the output in decimal if the input is a b c d 4. Find the output in decimal if the input is 4. Find the output in decimal if the input is o v. Find the output in decimal if the input is a b c d 6. The output of the AID converter is in 2's-complement format. Find the output of the AID converter if the input to the amplifier is a 1.

A bit AID converter has an input range of ±1O V and an amplifier at the input with a gain of Find the output of the AID converter if the input to the amplifier is a 0. Estimate the quantization error as a percent of reading for an input of 1. Estimate the quantization error as a percent of reading for an input of 2. If the input is 8. Estimate the quantization error as a percentage of reading for an input An amplifier is connected to the input and has selectable gains of 10, , and Select the best value for the gain to minimize the quantizing error. What will be the quantizing error as a percentage of the reading when the transducer voltage is 3. Could you attenuate the signal before amplification to reduce the quantizing error? The connected transducer has a maximum output of 7. Select the appropriate gain to minimize the quantization error, and com­ pute the quantization error as a percent of the maximum input voltage.

The connected transducer has a maximum output of 10 mV. Estimate the analog voltage output if the input is simple binary and has the decimal value of Problems 4. Simulate the successive-approximations process to determine the simple binary output. Specify whether these errors are of bias or precision type. List the questions that you want to discuss with an application engineer working for a supplier. D i screte Sa m p l i n g a n d CHAPTER Ana lys i s of Ti me-Va ryi n g Sig nals Unlike analog recording systems, which can record signals continuously in time, digital data-acquisition systems record signals at discrete times and record no information about the signal in between these times. Unless proper precautions are taken, this dis­ crete sampling can cause the experimenter to reach incorrect conclusions about the original analog signal.

In this chapter we introduce restrictions that must be placed on the signal and the discrete sampling rate. In addition, techniques are introduced to determine the frequency components of time-varying signals spectral analysis , which can be used to specify and evaluate instruments and also determine the required sam­ pling rate and filtering. For example, a reading sample may be taken every 0. The experimenter is then left with the problem of deducing the actual measurand behavior from selected samples. The rate at which measurements are made is known as the sampling rate, and incorrect selection of the sampling rate can lead to mis­ leading results. Figure 5. We are going to explore the output data of a discrete sampling system for which this continuous time­ dependent signal is an input. The important characteristic of the sampling system here is its sampling rate normally expressed in hertz.

Figures 5. To infer the form of the original signal, the sample data points have been connected with straight-line segments. In examining the data in Figure 5. However, we know that the sampled signal is, in fact, a sine wave. The amplitude of the sampled data is also 1 02 5. misleading -it depends on when the first sample was taken. This behavior a constant value of the output occurs if the wave is sampled at any rate that is an integer fraction of the base frequency fm e. The data in Figure 5. The frequency, 1 Hz, is the difference between the sampled-data frequency, 10 Hz, and the sampling rate, 11 Hz. The apparent frequency is 8 Hz, the difference between the sam­ pling rate and the signal frequency, and is again incorrect relative to the input fre­ quency. These incorrect frequencies that appear in the output data are known as aliases.

Aliases are false frequencies that appear in the output data, that are simply artifacts of the sampling process, and that do not in any manner occur in the origi­ nal data. It turns out that for any sampling rate greater than twice fm ' the lowest apparent frequency will be the same as the actual 1 04 Chapter 5 Discrete Sampling and Ana lysis of Time-Va rying Signals 1. This restriction on the sampling rate is known as the sampling-rate theo­ rem. This theorem simply states that the sampling rate must be greater than twice the highest-frequency component of the original signal in order to reconstruct the origi­ nal waveform correctly. The theorem also specifies methods that can be used to reconstruct the original signal.

The amplitude in Figure 5. The sampling-rate theorem has a well-established theoretical basis. There is some evidence that the concept dates back to the nineteenth-century mathematician Augustin Cauchy Marks, The theorem was formally introduced into modern technology by Nyquist and Shannon and is fundamental to communica­ tion theory. The theorem is often known by the names of the latter two scientists. A comprehensive but advanced discussion of the subject is given by Marks In the design of an experiment, to eliminate alias frequencies in the data sampled, it is neces­ sary to determine a sampling rate and appropriate signal filtering. This process will be discussed in some detail later in the chapter. Even if the signal is correctly sampled i.

For example, Figure 5. The sampled data are shown as the small squares. However, these data are not only consistent with a Hz sine wave but in this case, the data are also consistent with Actually, there are an infinite number of higher frequencies that are consistent with the data. If, however, the requirements of the sampling-rate theorem have been met perhaps with suitable filtering , there will be no frequencies less than half the sampling rate t�at are consistent with the data except the correct signal frequency. The higher frequencies can be eliminated from consideration since it is known that they don't exist. In some cases, the requirements of the sampling-rate theorem may not have been met, and it is desired to estimate the lowest alias frequency. The lowest is usually the most obvious in the sampled data.

A simple method to estimate alias frequencies involves the folding diagram as shown in Figure 5. This diagram enables one to predict the alias frequencies based on a knowledge of the signal fre­ quency and the sampling rate. To use this diagram, it is necessary to compute a fre­ quency iN called the folding frequency. iN is half the sampling rate, is. The use of this diagram is demonstrated in Example 5. Example 5. The lowest alias frequency is the difference between frequency. the sampling frequency and the signal frequency. In part b , the sampling frequency is less than the signal frequency. The folding diagram is the simplest method to determine the lowest alias frequency. In part c , the requirement of the sampling-rate theorem has been met, and the alias frequency is in fact the signal frequency. We will always find a lowest frequency using the folding diagram, whether it is a cor­ rect frequency or a false alias. To know that the frequency is correct, we must insure that the sampling rate is at least twice the actual frequency, usually by using a filter to remove any frequency higher than half the sampling rate.

How­ ever, the general time-varying signal does not have the form of a simple sine wave; Figure 5. As discussed below, complicated waveforms can be considered to be constructed of the sum of a set of sine or cosine waves of different fre­ quencies. The process of determining these component frequencies is called spectral analysis. There are two times in an experimental program when it may be necessary to perform spectral analysis on a waveform. The first time is in the planning stage and the second is in the final analysis of the measured data. In planning experiments in which the data vary with time, it is necessary to know, at least approximately, the frequency characteristics of the measurand in order to specify the required frequency response of the transducers and other instruments and to determine the sampling rate required. While the actual signal from a planned experiment will not be known, data from simi­ lar experiments may be used to determine frequency specifications.

In many time-varying experiments, the frequency spectrum of a signal is one of the primary results. In structural vibration experiments, for example, acceleration of the vibrating body may be a complicated function resulting from various resonant fre­ quencies of the system. The measurement system is thus designed to respond properly to the expected range of frequencies, and the resulting data are analyzed for the spe­ cific frequencies of interest. To examine the methods of spectral analysis, we first look at a relatively simple waveform, a simple Hz sawtooth wave as shown in Figure 5.

At first, one might think that this wave contains only a single frequency, Hz. However, it is much more complicated, containing all frequencies that are an odd-integer mUltiple of , such as , , and Hz. The method used to determine these component fre­ quencies is known as Fourier-series analysis. The lowest frequency, to, in the periodic wave shown in Figure 5. s disc�ssed by Den Hartog , Churchill , and Kamen , any penodlc functlon J t can be represented by the sum of a constant and a series of sine and cosine waves. Of course, Eq. Since J t can­ not, in general, be expressed in equation form, it is normal to evaluate Eqs. If J t is even, it can be represented entirely with a series of cosine terms, which is known as a Fourier cosine series. If fit is odd, it can be represented entirely with a series of sine terms, which is known as a Fourier sine series. Many functions are neither even nor odd and require both sine and cosine terms. If Eqs. bb b3 , bs, and � are the amplitudes of the first, third, fifth, and seventh harmonics of the function f t.

These have frequencies of , , , and Hz, respectively. It is useful to present the amplitudes of the har­ monics on a plot of amplitude versus frequency as shown in Figure 5. As can be 1 10 Chapter 5 Discrete Sa m p l i n g and Ana lysis of Ti me-Va ryi ng Signals 2 1. seen, harmonics beyond the fifth have a very low amplitude. Often, it is the energy con­ tent of a signal that is important, and since the energy is proportional to the amplitude squared, the higher harmonics contribute very little energy. As can be seen, the sum of the first and third harmonics does a fairly good job of representing the sawtooth wave.

The main problem is apparent as a rounding near the peak- a problem that would be reduced if the higher harmonics e. were included. Fourier analysis of this type can be very useful in specify­ ing the frequency response of instruments. If, for example, the experimenter considers the first-plus-third harmonics to be a satisfactory approximation to the sawtooth wave, the sensing instrument need only have an upper frequency limit of Hz. The · process of determining Fourier coefficients using numerical methods is demonstrated in Section A. t, Appendix A. Solution: The fundamental frequency for this wave is 10 Hz and the angular frequency, w is Also, by examination, we can conclude that it is an odd function and that the cosine terms will be zero and only the sine terms will be required.

Using Eq. S , the first harmonic coefficient can be computed from 1 0 1°0 2 [ 1°. os �[ rO. l Sawtooth wave. One problem associated with Fourier-series analysis is that it appears to only be useful for periodic signals. In fact, this is not the case and there is no requirement that f t be periodic to determine the Fourier coefficients for data sampled over a finite time. We could force a general function of time to be periodic simply by duplicating the function in time as shown in Figure 5. If we directly apply Eqs. However, if the resulting Fourier series were used to compute values off t outside the time interval O-T, it would result in values that would not necessarily and probably would not resemble the original signal. The analyst must be careful to select a large enough value of T so that all wanted effects can be represented by the resulting Fourier series.

An alternative method of finding the spectral content of signals is that of the Fourier transform, discussed next. The Fourier transform is a generalization of Fourier series. The Fourier transform can be applied to any practical function, does not require that the function be periodic, and for discrete data can be evaluated quickly using a modern computer technique called the Fast Fourier Transform. In presenting the Fourier transform, it is common to start with Fourier series, but in a different form than Eq.

This form is called the complex exponential form. These relationships can be used to transform Eq. L cn ejnwot n - oo 5. In Section 5. If a longer value of T is selected, the lowest frequency will be reduced. This concept can be extended to make T approach infinity and the lowest frequency approach zero. In this case, frequency becomes a continuous function. It is this approach that leads to the concept of the Fourier transform. The Fourier transform of a function. Once a Fourier transform has been deter­ mined, the original function. Such a signal is not well suited to analysis by the continuous Fourier transform. The increment of f, Af, is equal to lIT, and the increment of time the sampling period At is equal to TIN. The Fs are complex coefficients of a series of sinusoids with frequencies of 0, Af, 2Af, 3Af,.

The amplitude of F for a given frequency represents the relative contribution of that frequency to the original signal. Only the coefficients for the sinusoids with frequencies between 0 and N 12 - 1 Af are used in the analysis of signal. The coefficients of the remaining frequencies provide redundant information and have a special meaning, as discussed by Bracewell The requirements of the Shannon sampling-rate theorem also prevent the use of any 1 14 Chapter 5 Discrete Sa m p l i ng a n d Ana lysis of Ti me-Varying Signals frequencies above NI2! f ej 21Tkt f nt A sophisticated algorithm called the Fast Fourier Transform FFf has been developed to compute discrete Fourier transforms much more rapidly. This algorithm requires a time proportional to N log2 N to complete the computations, much less than the time for direct integration. The only restriction is that the value of N be a power of 2: for example, , , , and so on. Programs to perform fast Fourier transforms are widely available and are included in major spreadsheet programs.

The fast Fourier transform algorithm is also built into devices called spectral analyzers, which can discretize an analog signal and use the FFf to determine the frequencies. It is useful to examine some of the characteristics of the discrete Fourier transform. If we discretize one second of the signal into samples and perform an FFf we used a spreadsheet program as demonstrated in Section A. f I , versus the frequency, k! As expected, the magnitudes of F at f 10 and f 15 are dominant. However, there are some adjacent frequencies showing appreciable magnitudes. number of points used to discretize the signal. It can be noticed that the magnitude of I F I for the 10 Hz is different in Figures 5. This is a consequence of the definition of the discrete Fourier transform and the FFf algorithm. To get the correct amplitude of the input sine wave, I F I should be multiplied by 21N. In Figure 5. Similarly for Figure 5. In many cases finding a natural frequency for example , only the relative amplitudes of the Fourier compo­ nents are important so this conversion step is not necessary.

For Figure 5. The actual maximum frequency is Hz. For the FFTs shown in Figures 5. In general, the experimenter will not know the spectral composition of the signal and will not be able to select a sampling time T such that there will be an integral number of cycles of any frequency in the signal. This complicates the process of Fourier decomposition. To demonstrate this point, we will modify Eq. Although 15 complete cycles of the Hz components are sampled, The results of the DFT are shown in Figure 5. The first thing we notice is that the Fourier coefficient for It should be recognized that without a priori knowledge, the user would not be able to deduce whether the signal had separate lO-Hz and Hz components or just a single component at 1O.

An unexpected result is the fact that the entire spectrum outside of 10, 11, and Hz has also been altered, yielding significant coefficients at frequencies not present in the original signal. This effect is called leakage and is caused by the fact that there are a non-integral number of cycles of the 1O. Since one does not � 50 Frequency Hz FIGURE 5. The actual cause is that the sampled value of a particular frequency component at the start of the sampling interval is different from the value at the end. A common method to work around this problem is the use of a windowing function to attenuate the signal at the beginning and the end of the sampling interval.

A windowing function is a waveform that is applied to the sampled data. This equa­ tion is plotted in Figure 5. The sampled data is multiplied by this window function producing a new set of data with smoother edges. The Hann function is superimposed on top of the data with the sinusoidal shape apparent. The central portion of the signal is unaffected while the amplitude at the edges is gradually reduced to create a smoother transition. Compared to Fig 5. This should not come as a surprise as the windowing function clearly suppresses the average amplitude of the original signal.

This tradeoff between frequency resolution and amplitude is inherent for all window types. Windows that present good resolution in frequency but poor determination of amplitude are often referred to as being of high resolution with low dynamic range. A variety of window functions and their char­ acteristics have been defined in the literature. Each type of window has its own unique characteristics, with the proper choice depending on the application and preferences of the user. Some common window functions are rectangular, Hamming, Hann, cosine, Lanczos, Bartlett, triangular, Gauss, Bartlett-Hann, Blackman, Kaiser, Blackman­ Harris, and Blackman-Nutall. See Engelberg , Lyons , Oppenheim et. FIGURE 5. b Modified data. An additional consideration in spectral analysis is the types of plots used to dis­ play the data. In the previous figures, a linear scale has been used both for the ampli­ tude and frequency. It is common, however, to plot one or both axes on a logarithmic scale.

In the case of amplitude, it is common to plot the spectral power density, which is typically represented in units of decibels dB. The majority of signals encountered in practice will have record lengths much longer than the examples presented here. Consider a microphone measurement sampled at 40 kHz over a s period of time. The record length in this case would 5. In this case, the apparent frequency resolution, tlf, of the FFT would be 0. As already seen, spectral leakage is likely to limit the resolution to much higher values and it is unlikely that this level of resolution would practically be needed. Rather, it is more common to use methods such as Bartlett's or Welch's methods. In these methods, the sampled signal is divided into equal length segments with a window function applied to each segment. An FFT is then computed for each segment and then aver­ aged together to produce a single FFT for the entire signal.

For example, if one were to take the example above, it could be divided into approximately segments with values in each segment. The FFT of each segment would have a frequency resolution The major advantage to this form of analysis is that any uncertainty in the Fourier coefficients is reduced through the averaging of multiple FFT coefficients. This typically yields a much smoother curve than a single FFT. See Lyons and Oppenheim, et. In most cases the experimenter can deter­ mine the maximum signal frequency of interest, which we shall call fe. However, the signal frequently contains significant energy at frequencies higher than fe.

If the signal is to be recorded with an analog device, such as an analog tape recorder, these higher frequencies are usually of no concern. They will either be recorded accurately or attenuated by the recording device. If, however, the signal is to be recorded only at discrete values of time, the potential exists for the generation of false, alias signals in the recording. The sampling-rate theorem does not state that to avoid aliasing, the sampling rate must be twice the maximum frequency of interest but that the sampling rate must be greater than twice the maximum frequency in the signal, here denoted by fm ' As an example, consider a signal that has Fourier sine components of 90, , , and Hz. If we are only interested in frequencies below Hz, we might set the sampling rate at Hz. In our sampled output, however, we will see frequencies of Hz and 40 Hz, which are aliases caused by the and Hz components of the signal. Section 5.

twice fm, w� s�ould not only avoid aliasing but also be able to recover at least theoretically the ongm? l wave­ form. In the foregoing example, in which fm is Hz, we would select a samplIng rate, fs, greater than Hz. The first term after the summation, fe n aT}, represents the discretely sampled values of the function, n is an integer cor­ responding to each sample, and aT is the sampling period, 1Ifs. One important charac­ teristic of this equation is that it assumes an infmite set of sampled data and is hence an infinite series. Real sets of sampled data are finite. However, the series converges and discrete samples in the vicinity of the time t contribute more than terms not in the vicinity. Hence, the use of a finite number of samples can lead to an excellent recon­ struction of the original signal. As an example, consider a function sin 2'IT0. It is sampled at a rate of 1 sample per second and samples are collected.

Note that fs exceeds 2fm' so this requirement of the sampling-rate theorem is satisfied. A portion of the sampled data is shown in Figure 5. The sampled data have been connected with straight-line segments and do not appear to closely resemble the original sine wave. The original sine wave has been recovered from the data samples with a high degree of accuracy. Reconstructions with small sample sizes or at the ends of the data samples will, in general, not be as good as this example. Although Eq. In most cases, the use of very high sampling rates can eliminate the need to use reconstruction methods to recover the original signal. t Although it is normally desirable to select such a sampling rate, it is possible to relax this in some cases.

As we noted in Section 5. Assume that we have set the sampling rate such that the minimum alias frequency la has a value just equal to Ie. The highest frequency in the signal that could cause aliasing is 1m. All frequencies above 'm have zero amplitude. If this basis is used to select the sampling rate, there will be aliases in the sa�pled signal with frequencies in the Ie Is range but not in the 0 -Ie range. Digital filtering techniques USIng software can be used to eliminate these alias frequencies. If Ie has a value of Hz and 1m has A value o� 4OO �, we would select a sampling rate of Hz, significantly lower than twice 1m BOO Hz. For further diSCUSSion, see Taylor The lation is given by variance of the popu­ 6. a Calculate the expected life of the bearings.

b If we pick a bearing at random from this batch, what is the probability that its life x will be less than 20 h, greater than 20 h, and finally, exactly 20 h? Solution: a Using Eq. It is used to determine the probability that a random variable has a value less than or equal to a specified value. Example 6. Solu tion: a Using Eq. Either by substituting 15 into the equation or by reading from the graph, we find that the probability that the lifetime is less than 15 h is 0. b Similarly, the probability that the lifetime is less than 20 h is 0. In this sec­ tion, some of the most common are described briefly and their application is discussed. Binomial Distribution The binomial distribution is a distribution which describes discrete random variables that can have only two possible outcomes: "success" and "failure. The following conditions need to be satisfied for the binomial distribution to be applicable to a certain experiment: L Each trial in the experiment can have only the two possible outcomes of success 2.

The probability of success remains constant throughout the experiment. This prob­ or failure. ability is denoted by p and is usually known or estimated for a given population. The experiment consists of n independent trials. The binomial distribution provides the probability cesses in a total of n trials and is expressed as P of finding exactly r suc­ 6. l - pt-I. Determine the probability that in a batch of 20 computers, 5 will require repair during the warranty period. Success will be defined as not needing repair within the warranty period. Other assumptions underlying the application of this distribution are that all trials are independent and that the probabilities of success and failure are the same for all computers.

The problem amounts to determining the probability P of hav­ ing 15 successes r out of 20 machines n. Using Eqs. If we buy four of these bulbs, what are the probabilities of finding that four, three, two, one, and none of the bulbs are defective? Again, we can use the binomial distribution. The probability of having four, three, two, one, and zero defective light bulbs can be calculated by using Eq. Solution: We use Eq. Poisson Distribution The Poisson distribution is used to estimate the number of random occurrences of an event in a specified interval of time or space if the average number of occurrences is already known.

For example, if it is known that, on average, 10 customers visit a bank per five-minute period during the lunch hour, the Poisson distribution can be used to predict the probability that 8 customers will visit during a particular five-minute period. The Poisson distribution can also be used for spatial variations. For instance, if it is known that there are, on average, two defects per square meter of printed circuit boards, the Poisson distribution can be used to predict the probability that there will be four defects in a square meter of boards. The following two assumptions underline the Poisson distribution: 1. Curso de instrumentación. Nondestructive Test Methods for Evaluation of Concrete in Structures. Sorry, this document isn't available for viewing at this time.

In the meantime, you can download the document by clicking the 'Download' button above. RELATED PAPERS. EE Lecture Notes v4. Design, construction and testing of regenerative turbine pump. Principles of analog signal conditioning, pp. Review of Scientific Instruments Calibrator for microflow delivery systems. Measurement and Instrumentation Principles, 3rd Edition. Process - For Finals. Programmable Logic Controllers: Programming Methods and Applications. CHAPTER 3: SENSORS SECTION 3. Introduction to measurement. Measurement and Instrumentation Principles A.

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This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below! Home Add Document Sign In Register. Introduction to Engineering Experimentation 3rd Edition Home Introduction to Engineering Experimentation 3rd Edition. Author: Anthony J. Wheeler Ahmad R. DOWNLOAD PDF. R4 R 1 lbf Btu hp. h Btu dyn N N kWh Force kJ Ibf Thermal conductivity Btulh. fLF Heat transfer coefficient Btulh. F Length [t m cm m Power cm m mile km Ibm kg slug Ibm ton metric kg ton metric Ibm ton short Ibm Btulh W Btuls W hp Pressure W hp ft. K Volume cm3 1 Deg. R ft3 gallon US liter C to Oeg. K F to Ocg.

C Oeg. F to Oeg. R Oeg. Copyright © ,, by Pearson Higher Education. Upper Saddle River, New Jersey, All right reserved. Manufactured in the United States of America. This publication is protected by Copyright and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission s to use materials from this work, please submit a written request to Pearson Higher Education, Permissions Department, 1 Lake Street, Upper Saddle River, NJ I The authors and publisher of this book have used their best efforts in preparing this book.

These efforts include the i l development, research, and testing of the theories and programs to determine their effectiveness. The authors anq l publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentatiorl contained in this book. The authors and publisher shall not be liable in any event for incidental or consequentia i damages in connection with, or arising out of, the furnishing, performance, or use of these programs. I� troducti? Wheeler, Ahmad R. Ganji; wIth contnbutIOns by V. Krishnan, Brian S. Includes bibliographical references and index. ISBN alk. paper l. Experimental design. GanJ'i , A.. R Ahmad R eza II Title " TA W pearsonhighere d.

com ISBN ISBN-l0: 1 0 9 8 7 1 I I bl I I I I I I I I I I I I I l i i I Contents ix Preface CHAPTER 1 Introduction 1. In addition to de­ scriptions of common measurement systems, the book describes computerized data ac­ quisition systems, common statistical techniques, experimental uncertainty analysis, and guidelines for planning and documenting experiments. It should be noted that this book is introductory in nature. Many of the subjects covered in a chapter or a few pages here are the subjects of complete books or major technical papers. Only the most common measurement systems are included -there exist many others that are used in practice. More comprehensive studies of available literature and consultation with product ven­ dors are appropriate when engaging in a significant real-world experimental program.

It is to be expected that the skills of the experimenter will be enhanced by more advanced courses in experimental and measurement systems design and practical experience. The design of an experimental or measurement system is inherently an interdis­ ciplinary activity. For example, the instrumentation and control system of a process plant might require the skills of chemical engineers, mechanical engineers, electrical engineers, and computer engineers. Similarly, the specification of the instrumentation used to measure the earthquake response of a large structure will involve the skills of civil, electrical, and computer engineers.

Based on these facts, the topics presented in this book have been selected to prepare engineering students and practicing engineers of different disciplines to design experimental projects and measurement systems. This third edition of the book involves a general updating of the material and the enhancement of the coverage in a number of areas. Brian S. Thurow, Auburn University, contributed in the area of general instrumentation and V. Krishnan, San Francisco State University, contributed material in statistics. The book first introduces the essential general characteristics of instruments, electrical measurement systems, and computerized data acquisition systems.

This intro­ duction gives the students a foundation for the laboratory associated with the course. The theory of discretely sampled systems is introduced next. The book then moves into statistics and experimental uncertainty analysis, which are both considered central to a modem course in experimental methods. It is not anticipated that the remaining chap­ ters will necessarily be covered either in their entirety or in the presented sequence in lectures-the instructor will select appropriate subjects. Descriptions and theory are provided for a wide variety of measurement systems. There is an extensive discussion of dynamic measurement systems with applications. Finally, guidance for planning ex­ periments, including scheduling, cost estimation, and outlines for project proposals and reports, are presented in the last chapter.

ix x Preface There are some subjects included in the introductory chapters that are frequent­ ly of interest, but are often not considered vital for an introductory experimental meth­ ods course. These subjects include the material on circuits using operational amplifiers Sections 3. Any or all of these sections can be omitted without significant impact on the remainder of the text. The book has been designed for a semester course of up to three lectures with one laboratory per week. Depending on the time available, it is expected that only selected topics will be covered. The material covered depends on the number of lectures per week, the prior preparation of students in the area of statistics, and the scope of included design project s. The book can serve as a reference for subsequent laboratory courses. Our introductory course in engineering experimentation is presented to all under­ graduate engineers in civil, electrical, and mechanical engineering.

The one-semester format includes two lectures per week and one three-hour laboratory. In our two­ lecture-per-week format, the course content is broken down as follows: L General aspects of measurement systems 2 lectures 2. Electrical output measurement systems 2 lectures 3. Computerized data acquisition systems 3 lectures 4. Fourier analysis and the sampling rate theorem 4 lectures 5. Statistical methods and uncertainty analysis 10 lectures 6. Selected measurement devices 4 lectures 7. Dynamic measurement systems 3 lectures Additional measurement systems and the material on planning and documenting ex­ periments are covered in the laboratory.

The laboratory also includes an introduction to computerized data acquisition systems and applicable software; basic measurements such as temperature, pressure, and displacement; statistical analysis of data; the sam­ pling rate theorem; and a modest design project. A subsequent laboratory-only course expands on the introductory course and includes a significant design project. There is sufficient material for a one-semester, three-Iecture-per-week course even if the students have taken a prior course in statistics. Areas that can be covered in greater detail include qperational amplifiers, analog-to-digital converters, spectral analysis, un­ certainty analysis, measurement devices, dynamic measurements, and experiment design. ACKNOWLE DG M E NTS The authors would like to acknowledge the many individuals who reviewed all or por­ tions of the book. We would like to thank Sergio Franco, Sung Hu, and V. Particular thanks go to reviewers of the complete book: Charles Edwards of the University of Missouri, Rolla, and David Bog­ ard of the University of Texas, Austin.

ANTHONY 1. WHEELER AHMAD R. In engineering, carefully designed experiments are needed to conceive and verify theoretical concepts, develop new methods and products, commission sophisticated new engineering systems, and evaluate the performance and behavior of existing products. Experimentation and the design of measurement systems are major engineering activities. In this chapter we give an overview of the applications of experiments and measurement systems and describe briefly how this book will prepare the reader for professional activities in these areas.

The first of these is measurement in engineering experimentation, in which new information is being sought, and the second is measurement in operational devices for monitoring and control purposes. Such experimentation falls broadly into three categories: 1. Research experimentation 2. Development experimentation 3. Performance testing The primary difference between research and development is that in the former, con­ cepts for new products or processes are being sought often unsuccessfully , while in the latter, known concepts are being used to establish potential commercial products. Carbon-fiber composites represent a relatively recent example of the research and development process.

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Mechanical Measurements 2nd Edition by S. Another measure of how well the best-fit line represents the data is called the standard error of estimate, given by 6. As the number of samples increases, the t-distribution approaches the normal distribution. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Some buildings are permanently instrumented using data loggers to obtain acceleration and other data if and when an earthquake occurs.

This effect is called leakage and is caused by the fact that there are a non-integral number of cycles of the 1O. Some more expensive op-amps have higher values of GBP. Causality should be determined from other knowledge about the problem, introduction to engineering experimentation 3rd edition pdf download. Phase distortion is demonstrated in Figure 3. Solution: a Using Eq. Find the output in decimal if the input is a b c d 6. If the mean of X is m and its standard deviation is s, then Y is normally distrib­ uted with a mean f.