Characterics of measuring system.pptx

Basic Characteristics of Measuring
Instruments
Instruments performance characteristics can be broadly classified:
(a) Static Characteristics. Instrument desired input may be static constant or varying
slowly with respect to time. Static performance parameters like, accuracy, precision, resolution,
sensitivity, linearity, hysteresis, drift , over- load capacity, impedance loading etc are usually good.
(b) Dynamic Characteristics. In certain situations, where the desired input is not
constant but varies very rapidly with reference to time. In such situations are represented by the
relations between input and output parameters that are generated by the relevant differential
equations applicable in the given situation.
Thus, in general, the overall quantitative performance qualities of the instruments are represented by
both their static and dynamic characteristics. However, for time independent signals, only the static
characteristics need be considered.

Instruments
Accuracy in Measuring Instruments.
• In the fields of science and engineering, the accuracy of a measurement system is the degree of
closeness of measurements of a quantity to that quantity's true value.
• The precision of a measurement system, related to reproducibility and repeatability, is the
degree to which repeated measurements under unchanged conditions show the same
results. Although the two words precision and accuracy can be synonymous in colloquial use, they
are deliberately contrasted in the context of the scientific method.
• The field of statics, where the interpretation of measurements plays a central role, prefers to use
the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy
and variability is the amount of imprecision.
• A measurement system can be accurate but not precise, precise but not accurate, neither, or
both. For example, if an experiment contains a systematic error, then increasing the sample size
generally increases precision but does not improve accuracy. The result would be a consistent yet
inaccurate string of results from the flawed experiment. Eliminating the systematic error
improves accuracy but does not change precision.

Instruments
• A measurement system is considered valid if it is both accurate and precise.
Related terms include bias (non-random or directed effects caused by a factor or
factors unrelated to the independent variable) and error (random variability).
• The terminology is also applied to indirect measurements—that is, values
obtained by a computational procedure from observed data.
• In addition to accuracy and precision, measurements may also have a
measurement resolution, which is the smallest change in the underlying physical
quantity that produces a response in the measurement.
• In numerical analysis, accuracy is also the nearness of a calculation to the true
value; while precision is the resolution of the representation, typically defined by
the number of decimal or binary digits.
• In military terms, accuracy refers primarily to the accuracy of fire (justesse de tir),
the precision of fire expressed by the closeness of a grouping of shots at and
around the centre of the target

Instruments

Instruments
• In industrial instrumentation, accuracy is the measurement tolerance, or
transmission of the instrument and defines the limits of the errors made when
the instrument is used in normal operating conditions.
• Ideally a measuring device is both accurate and precise, with measurements all
close to and tightly clustered around the true value. The accuracy and precision
of a measurement process is usually established by repeatedly measuring some
traceable reference standard . Such standards are defined in the international
system of units and maintained by national standard organizations such as the
(NIST) National Institute of Standards and Technology in the United States.
• This also applies when measurements are repeated and averaged. In that case,
the term standard error is properly applied: the precision of the average is equal
to the known standard deviation of the process divided by the square root of the
number of measurements averaged. Further, the central limit theorem shows
that the probability distribution of the averaged measurements will be closer to a
normal distribution than that of individual measurements

Instruments
• With regard to accuracy we can distinguish:
• The difference between the mean of the measurements and the reference value, the bias. Establishing
and correcting for bias is necessary for calibration.
• The combined effect of that and precision.
• A common convention in science and engineering is to express accuracy and/or precision implicitly by
means of significant figures. Here, when not explicitly stated, the margin of error is understood to be
one-half the value of the last significant place. For instance, a recording of 843.6 m, or 843.0 m, or
800.0 m would imply a margin of 0.05 m (the last significant place is the tenths place), while a
recording of 8436 m would imply a margin of error of 0.5 m (the last significant digits are the units).
• A reading of 8,000 m, with trailing zeroes and no decimal point, is ambiguous; the trailing zeroes may
or may not be intended as significant figures. To avoid this ambiguity, the number could be represented
in scientific notation: 8.0 × 103 m indicates that the first zero is significant (hence a margin of 50 m)
while 8.000 × 103 m indicates that all three zeroes are significant, giving a margin of 0.5 m.

Instruments
• Similarly, it is possible to use a multiple of the basic measurement unit: 8.0 km is
equivalent to 8.0 × 103 m. In fact, it indicates a margin of 0.05 km (50 m).
However, reliance on this convention can lead to false precision errors when
accepting data from sources that do not obey it. For example, a source reporting
a number like 153,753 with precision +/- 5,000 looks like it has precision +/- 0.5.
Under the convention it would have been rounded to 154,000.
• Precision includes:
• repeatability — the variation arising when all efforts are made to keep conditions
constant by using the same instrument and operator, and repeating during a
short time period; and
• reproducibility — the variation arising using the same measurement process
among different instruments and operators, and over longer time periods.

Instruments
Sensitivity. A good sensor obeys the following rules:
• It is sensitive to the measured property
• It is insensitive to any other property likely to be encountered in its application, and
• It does not influence the measured property.
• Most sensors have a linear transfer function. The sensitivity is then defined as the ratio
between the output signal and measured property. eg; if a sensor measures temperature
and has a voltage output, the sensitivity is a constant with the units (V/K).
• The sensitivity is the slope of the transfer function. Converting the sensor's electrical
output in electrical signal to the measured units in degree K requires dividing the
electrical output by the slope.
• For an analogue sensor signal to be processed, or used in digital equipment, it needs to
be converted to a digital signal, using an ADC.

Measuring Instruments Deviations
Sensor deviations:
• Since sensors cannot replicate an ideal transfer function, several types of deviations can occur
which limit sensor accuracy:
• Since the range of the output signal is always limited, the output signal will eventually reach a
minimum or maximum when the measured property exceeds the limits. The full scale range
defines the maximum and minimum values of the measured property.
• The sensitivity may in practice differ from the value specified. This is called a sensitivity error. This
is an error in the slope of a linear transfer function.
• If the output signal differs from the correct value by a constant, the sensor has an offset error or
bias. This is an error in the y-intercept of a linear transfer function.
• Nonlinearity is deviation of a sensor's transfer function from a straight line transfer function.
Usually, this is defined by the amount the output differs from ideal behaviour over the full range
of the sensor, often noted as a percentage of the full range.
• Deviation caused by rapid changes of the measured property over time is a dynamic error. Often,
this behaviour is described with a bode plot showing sensitivity error and phase shift as a
function of the frequency of a periodic input signal.

• If the output signal slowly changes independent of the measured property, this is defined
as drift. Long term drift over months or years is caused by physical changes in the sensor.
• Noise is a random deviation of the signal that varies in time.
• A hysteresis error causes the output value to vary depending on the previous input
values. If a sensor's output is different depending on whether a specific input value was
reached by increasing vs. decreasing the input, then the sensor has a hysteresis error.
• If the sensor has a digital output, the output is essentially an approximation of the
measured property. This error is also called quantization error.
• If the signal is monitored digitally, the sampling frequency can cause a dynamic error, or
if the input variable or added noise changes periodically at a frequency near a multiple of
the sampling rate, aliasing errors may occur.
• The sensor may to some extent be sensitive to properties other than the property being
measured. For example, most sensors are influenced by the temperature of their
environment.

• All these deviations can be classified as systematic errors or random errors.
Systematic errors can sometimes be compensated for by means of some
kind of calibration strategy. Noise is a random error that can be reduced by
signal processing, such as filtering, usually at the expense of the dynamic
behaviour of the sensor.
Resolution.
• The resolution of a sensor is the smallest change it can detect in the
quantity that it is measuring. The resolution of a sensor with a digital
output is usually the resolution of the digital output. The resolution is
related to the precision with which the measurement is made, but they are
not the same thing. A sensor's accuracy may be considerably worse than its
resolution.

Errors & Uncertainties in Performance
Parameters
• The errors depends upon the type of instrument & the nature of
applications. Some salient static performance parameters are
periodically checked by means of a static calibration.
• This is establishes by means of imposing constant values of ‘unknown’
inputs and observing the resulting outputs.
• Quite often, we experience difficulty in obtaining known constant
values of the input quantity. Further, we also come across the
following difficulties:
(a) Change in sensitivity of instruments due to certain perturbations resulting all output values,
generally equally by particular quantity. It happens due to wear & tear effect of change in
environment on the equipment or the user fatigue.
(b) Repeativity failure due to random variations in parameters or in the system of measurement.

Types of Errors in Measuring Instruments
Errors in instruments can be broadly categorized as; (a) Systematic errors
(b) Cumulative errors getting accumulated over the period of time also
called instrument bias. These are caused due to the following:
• Instrument Errors. Certain errors are inherent in the instrument systems.
May be caused due to poor design/processing of the instruments. Eg; such
as errors in deviations of graduated scales, inequality of the balance arms,
irregular springs tensions, etc, cause such errors. It can be
eliminated/reduced by:
(i) Selecting a suitable instruments for a given application.
(ii) Applying suitable corrections after determining the amount of errors.
(iii)Calibrating the instruments against a suitable standards.

• Environmental Errors. It is caused due to variations of conditions externally
to the measuring instrument. Effect of change in ambient temperature,
buoyant effect of the wind cause errors on weights of chemical balance.
• Loading Errors. Such errors are caused by the act of measurement on the
physical system being tested.
(i) Introduction of additional resistance in the circuit of millimetre
(ii) Obstruction in follow type meter
Systematic errors can be corrected by properly calibrating the instrument.
Static errors are mainly due to instrument error and short comings in the
measuring process.
Dynamic errors are caused due to not responding fast enough to follow the
changes in measured variable. Response time is > rate at which signal is
changing.

• Inconsistencies associated with accurate measurement of small qualities.
• Presence of certain system defects; such as large dimensional tolerances in
mating parts and the presence of friction contribute to errors that are either +ve
or –ve depending upon the direction of motion. The former causes backlash error
and the later causes slackness in the meter bearing. The procedure based on the
method of symmetry is used for detecting and correcting such errors(increasing
and decreasing)
• Effect of uncertain and randomly varying parameters, uncontrolled disturbances
influences the instrument output i.e., supply voltage fluctuations
• Miscellaneous Type of Gross Errors: Certain errors can’t be strictly classified as
either systematic or random as they are partly systematic and partly random.
These are caused by personal or human senses such as parallax errors

• Error due to faulty components/adjustments:
(a) Sometimes there is a misalignment of moving parts, electrical leakage,
poor optics etc in the measuring system, eg zero drift which are
systematic or random errors respectively.
(b) Repeating a measurement for a sufficiently large number of times by feeding a
standard signal to the instrument. Difference between mean value of the signal
and the standard signal fives the best estimate of systematic error.
(c) Further the estimate of uncertainly which represents the random error in
measurement is evaluated from the dispersion of data
• Improper application of the instrument:
(a) Errors caused due to the use of instrument in condition which don’t conform to
the desired design/operating conditions eg extreme vibrations, mechanical
shock or pick up due to electrical noise could introduce so much gross error as to
mask the test information.

Measures of Central Tendency and Dispersion
• Statistics are numerical values used to summarize and compare sets of data. Two
important types of statistics are measures of central tendency and measures of
dispersion.
• A measure of central tendency is a number used to represent the center or
middle of a set of data values. The mean, median, and mode are three
commonly used measures of central tendency.
The mean or 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑜f n numbers is the sum of the numbers divided by n.
• Measures of Dispersion A measure of dispersion is a statistic that tells you how
dispersed, or spread out, data values are. One simple measure of dispersion is
the range, which is the difference between the greatest and least data values.
• Standard Deviation Another measure of dispersion is standard deviation, which
describes the typical difference (or deviation) between a data value and the
mean.

Normal Distribution & Standard Distribution
• The normal distribution is the most common type of distribution assumed
in technical stock market analysis and in other types of statistical analyses.
• The standard normal distribution has two parameters: the mean and the
standard deviation. For a normal distribution, 68% of the observations are
within +/- one standard deviation of the mean, 95% are within +/- two
standard deviations, and 99.7% are within +- three standard deviations.
• The normal distribution model is motivated by the Central Limit
Theorem. This theory states that averages calculated from independent,
identically distributed random variables have approximately normal
distributions, regardless of the type of distribution from which the variables
are sampled (provided it has finite variance).
• Normal distribution is sometimes confused with symmetrical distribution.
Symmetrical distribution is one where a dividing line produces two mirror
images, but the actual data could be two humps or a series of hills in
addition to the bell curve that indicates a normal distribution.

Normal Distribution & Standard Distribution
• In statics, the standard deviation is a measure of the amount of variation or dispersion of
a set of values. A low standard deviation indicates that the values tend to be close to the
mean (also called the expected value) of the set, while a high standard deviation indicates
that the values are spread out over a wider range.
• Standard deviation may be abbreviated SD, and is most commonly represented in
mathematical texts and equations by the lower case Greek letter sigma σ, for the
population standard deviation, or the Latin letter s, for the sample standard deviation.
• The standard deviation of a random variable, sample statistical , sample, statistical
population, data set, or probability distribution is the square root of its variance.
• It is algebraically simpler, though in practice, less robust than the average absolute
deviation. A useful property of the standard deviation is that unlike the variance, it is
expressed in the same unit as the data.
• The standard deviation of a population or sample and the standard error of a statistic
(e.g., of the sample mean) are quite different, but related.

Standard Deviation
• The sample mean's standard error is the standard deviation of the set of means that
would be found by drawing an infinite number of repeated samples from the population
and computing a mean for each sample.
• The mean's standard error turns out to equal the population standard deviation divided by
the square root of the sample size, and is estimated by using the sample standard
deviation divided by the square root of the sample size. eg; a poll's standard error, is the
expected standard deviation of the estimated mean if the same poll were to be conducted
multiple times.
• Thus, the standard error estimates the standard deviation of an estimate, which itself
measures how much the estimate depends on the particular sample that was taken from
the population.
• When only a sample of data from a population is available, the term standard deviation of
the sample or sample standard deviation can refer to either the above-mentioned quantity
as applied to those data, or to a modified quantity that is an unbiased estimate of
the population standard deviation (the standard deviation of the entire population).

Professional Competence
Professional Competence is the habitual and judicious use of:
• Communication
• Knowledge,
• Technical skills,
• Clinical reasoning,
• Emotions,
• Values, and reflection in daily practice for the benefit of the individual and
community being served.
• Competence builds on a foundation of basic clinical skills, scientific
knowledge, and moral development.

Professional Competence
1.Professional competence includes:
(i) A cognitive function: acquiring and using knowledge to solve real-
life problems
(ii) An integrative function: using biomedical and psychosocial data in
clinical reasoning
(iii) A relational function: communicating effectively with patients and
colleagues
(iv) An affective/moral function: the willingness, patience, and
emotional awareness to use these skills judiciously and humanely
2.Competence depends on habits of mind, including attentiveness,
critical curiosity, self-awareness, and presence.
3.Professional competence is developmental, impermanent, and

Characterics of measuring system.pptx

Recommended

Recommended

More Related Content

Similar to Characterics of measuring system.pptx

Similar to Characterics of measuring system.pptx (20)

Recently uploaded

Recently uploaded (20)

Characterics of measuring system.pptx