ELECTRONICS INSTRUMENTATION AND MEASUREMENT PDF

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Electrical and Electronics. Measurements and Instrumentation. Prithwiraj Purkait. Professor. Department of Electrical Engineering and. Dean, School of. Electronic Instruments for Measuring Basic Parameters: Amplified DC meter, Elements of Electronics Instrumentation and Measurement-3rd Edition by Joshph . Modern Electronic Instrumentation and Measurement Techniques: Helfrick & Cooper Electrical Measurement and Measuring Instruments - Golding & Waddis.


Electronics Instrumentation And Measurement Pdf

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PDF | On May 16, , Ramaprasad Panda and others published Electrical & Electronics Measurements & Measuring Instruments. Text book Electronic Instrumentation and Measurements David A bell 2nd edition .pdf. Wajeeh Rehman. Loading Preview. Sorry, preview is currently unavailable. Here. – α, the Seebeck coefficient, is a measure of the tendency of electric currents to carry heat and for heat currents to induce electrical currents. – = µ – eφ.

A rotating disc b.

A hollow aluminium drum c. A single flux producing winding d. High resistances in series with windings of both the magnets b. High resistance in series with the winding of one magnet and an inductive coil in series with the windings of other magnet c.

An inductive coil in series with the winding of one magnet and a capacitance in series with the windings of other magnet d. Same frequency with which the instrument was calibrated b. High frequency compared with which the instrument was calibrated c.

Low frequency compared with which the instrument was calibrated d. Visitor Kindly Note: EasyEngineering team try to Helping the students and others who cannot afford downloading books is our aim.

Electrical and Electronics Measurement & Instrumentation Expected MCQ PDF 8 For VIZAG Exam 2017

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Electronic Instrumentation and Measurements

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Bell Book Free Download. Other Usefu l Links. Your Comments About This Post. Is our service is satisfied, Anything want to say? Cancel reply. Thus 0. This is more easily seen if it is written as 3.

For numbers with decimal points, zeros to the right of a non zero digit are significant. Thus 2. For this reason it is important to keep the trailing zeros to indicate the actual number of significant figures. For numbers without decimal points, trailing zeros may or may not be significant. Thus, indicates only one significant figure. To indicate that the trailing zeros are significant a decimal point must be added.

For example, Exact numbers have an infinite number of significant digits. For example, if there are two oranges on a table, then the number of oranges is 2. Defined numbers 14 are also like this.

For example, the number of centimeters per inch 2. There are also specific rules for how to consistently express the uncertainty associated with a number.

In general, the last significant figure in any result should be of the same order of magnitude i. Also, the uncertainty should be rounded to one or two significant figures. Always work out the uncertainty after finding the number of significant figures for the actual measurement. For example, 9. But in the end, the answer must be expressed with only the proper number of significant figures. After addition or subtraction, the result is significant only to the place determined by the largest last significant place in the original numbers.

After multiplication or division, the number of significant figures in the result is determined by the original number with the smallest number of significant figures. For example, 2.

Refer to any good introductory chemistry textbook for an explanation of the methodology for working out significant figures. What is and what is not meant by "error"?

A measurement may be made of a quantity which has an accepted value which can be looked up in a handbook e. The difference between the measurement and the accepted value is not what is meant by error. Such accepted values are not "right" answers. They are just measurements made by other people which have errors associated with them as well. Nor does error mean "blunder.

Obviously, it cannot be determined exactly how far off a measurement is; if this could be done, it would be possible to just give a more accurate, corrected value.

Error, then, has to do with uncertainty in measurements that nothing can be done about.

If a measurement is repeated, the values obtained will differ and none of the results can be preferred over the others. Although it is not possible to do anything about such error, it can be characterized.

For instance, the repeated measurements may cluster tightly together or they may spread widely. This pattern can be analyzed systematically. Classification of Error : Generally, errors can be divided into two broad and rough but useful classes: systematic and random. Systematic errors are errors which tend to shift all measurements in a systematic way so their mean value is displaced.

This may be due to such things as incorrect calibration of equipment, consistently improper use of equipment or failure to properly account for some effect. In a sense, a systematic error is rather like a blunder and large systematic errors can and must be eliminated in a good experiment.

But small systematic errors will always be present. For instance, no instrument can ever be calibrated perfectly. Other sources of systematic errors are external effects which can change the results of the experiment, but for which the corrections are not well known.

In science, the reasons why several independent confirmations of experimental results are often required especially using different techniques is because different apparatus at different places may be affected by different systematic effects. Aside from making mistakes such as thinking one is using the x10 scale, and actually using the x scale , the reason why experiments sometimes yield results which may be far outside the quoted errors is because of systematic effects which were not accounted for.

They yield results distributed about some mean value. They can occur for a variety of reasons. They may occur due to lack of sensitivity. For a sufficiently a small change an instrument may not be able to respond to it or to indicate it or the observer may not be able to discern it. They may occur due to noise. There may be extraneous disturbances which cannot be taken into account. They may be due to imprecise definition.

They may also occur due to statistical processes such as the roll of dice. Random errors displace measurements in an arbitrary direction whereas systematic errors displace measurements in a single direction.

Some systematic error can be substantially eliminated or properly taken into account. Random errors are unavoidable and must be lived with. Many times you will find results quoted with two errors. The first error quoted is usually the random error, and the second is called the systematic error.

If only one error is quoted, then the errors from all sources are added together. In quadrature as described in the section on propagation of errors. A good example of "random error" is the statistical error associated with sampling or counting. For example, consider radioactive decay which occurs randomly at a some average rate.

If a sample has, on average, radioactive decays per second then the expected number of decays in 5 seconds would be Behavior like this, where the error, , 1 is called a Poisson statistical process. Typically if one does not know it is assumed that,, in order to estimate this error. Gaussian Error , Suppose an experiment were repeated many, say N, times to get, ,17 N measurements of the same quantity, x.

Electronic instrumentation and measurement techniques

If the errors were random then the errors in these results would differ in sign and magnitude. So if the average or mean value of our measurements were calculated, , 2 some of the random variations could be expected to cancel out with others in the sum.

This is the best that can be done to deal with random errors: repeat the measurement many times, varying as many "irrelevant" parameters as possible and use the average as the best estimate of the true value of x.

It should be pointed out that this estimate for a given N will differ from the limit as the true mean value; though, of course, for larger N it will be closer to the limit.

In the case of the previous example: measure the height at different times of day, using different scales, different helpers to read the scale, etc.

Doing this should give a result with less error than any of the individual measurements. But it is obviously expensive, time consuming and tedious. So, eventually one must compromise and decide that the job is done.

Nevertheless, repeating the experiment is the only way to gain confidence in and knowledge of its accuracy. In the process an estimate of the deviation of the measurements from the mean value can be obtained.

Root Sum Squares Formulae, There are several different ways the distribution of the measured values of a repeated experiment such as discussed above can be specified. Maximum Error The maximum and minimum values of the data set, and , could be specified. In these terms, the quantity, , 3 is the maximum error. And virtually no measurements should ever fall outside.

Average Deviation The average deviation is the average of the deviations from the mean,. Standard Deviation For the data to have a Gaussian distribution means that the probability of obtaining the result x is, , 5 where is most probable value and , which is called the standard deviation, determines the width of the distribution.

Because of the law of large numbers this assumption will tend to be valid for random errors. And so it is common practice to quote error in terms of the standard deviation of a Gaussian distribution fit to the observed data distribution. This is the way you should quote error in your reports. It is just as wrong to indicate an error which is too large as one which is too small.

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Certainly saying that a person's height is 5' 8. Standard Deviation The mean is the most probable value of a Gaussian distribution. In terms of the mean, the standard deviation of any distribution is,.The specialized instruments investigated in the latter half of the book include analog oscilloscopes, digital storage oscilloscopes, signal generators, waveform analyzers and graphic recording instruments. Get New Updates Email Alerts Enter your email address to subscribe this blog and receive notifications of new posts by email.

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Oliver and J. Bell is a professional engineer who was employed as a circuit design specialist in the electronics industry for many years before becoming a professor at Lambton College of Applied Arts and Technology in Sarnia, Ontario, Canada. And so it is common practice to quote error in terms of the standard deviation of a Gaussian distribution fit to the observed data distribution.

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