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These data are probably the most widely encountered type of data, as temperature is generally regarded as the most frequently measured indicating and recording " instrumentsSIGNAL y sign

Transformation and conditioning of measuring signals are important in metrology (IOTech Inc., 1998 ; Lang, 1987) Nevertheless the critical element in all measuring channels is the sensor itself It is a widely accepted opinion that no amount of signal treatment can improve an inherently bad signal The significant developments, which can be observed in transformation and conditioning methods, are also causing fundamental changes

in the handling of temperature data These data are probably the most widely encountered type of data, as temperature is generally regarded as the most frequently measured

indicating and recording

" instrumentsSIGNAL

y

signalling, alarming and control devices Figure 12.1 Signal processing as a part of temperature measuring chain

ISBNs: 0-471-86779-9 (Hardback); 0-470-84613-5 (Electronic)

physical quantity both in laboratories and in industry Usually, the conditioning units, whichenhance the quality of the whole measuring system, allow more easy maintenance In thischapter the status and trends in temperature measurement channel design are presented This

is achieved by giving an overview and classification of contemporary methods andalgorithms used within measuring systems applied in the temperature field Such a broadertheoretical context allows further discussion of both traditional and contemporary solutionsused in temperature measurement and also shows the trends of development in this field.Typically, a temperature measuring chain consists of a number of different, discernableconditioning steps, which adjust the signal to the requirements of various instruments Therole of initial transformation of temperature into another physical quantity, performed bytemperature sensors, has been presented in Section 1 4 In the terminology of Figure 1 4these further conditioning steps belong to the group ofmodifiers Continuing this approach,further conditioning of measured temperature signals will be discussed in this chapter

At present there is no general agreement on the nomenclature which should be used forvarious methods of signal transformation However, an appropriate approach, allowingthese methods to be grouped and classified is presented by Sydenham (1983), where it isasserted that the three main groups of methods are:

" transformation of signal nature,

" transformation of signal scale,

" transformation of signal shape

Transformation of signal nature includes methods applied, when the physical quantity

or energy form of the signal does not suit the requirements of the measuring units orinstruments Initial transformation is always a transformation of signal nature but it can also

be found in many other steps ofsignal processing

Transformation ofsignal scale changes the proportional values of a processed signal by

an increase or decrease Temperature signals usually require amplification because they are

of low energy content This is true irrespective of whether the signal is obtained from a sustaining cross-converter or a modulating sensor

self-Transformation ofsignal shape includes algorithms influencing the time domain form ofthe signal It usually leads to modification of the spectral power distribution of the signal,elimination of certain harmonics, frequency modulation etc

Each of these methods of signal transformation can be found at various steps oftemperature signal processing Sometimes they are used within one integrated processingunit It should be noted however that all of them influence the final accuracy of atemperature measuring process

12.2 Methods of Signal Processing in Temperature

Measurement

Methods of temperature signal processing have been classified following the methodicalapproach presented in Section 1 4 The basis of this classification is a space of all physicalquantities in which both non-electric and electric quantities are distinguished In addition,analogue and digital forms of electric quantities are considered, as shown in Figure 12.2

Such an approach allows all types of signals which can be used for carrying informationabout measured temperature, within measuring systems, to be taken into account

Temperature is usually captured from the body, or object, under measurement byvarious sensors or transducers, which may be regarded as initial energy transformers orconverters This initial transformation leads to a change of the signal from the thermalenergy domain either to another non-electric form or to an electric form as shown inFigures 1 6, 1 7 and 1 8 This is usually accompanied by shape transformation due tononlinearity of the functional characteristics of temperature sensors The resulting signal israrely used directly by indicators, recorders or other measuring instruments Hence, itundergoes subsequent further transformation, called signal conditioning by Sydenham (1983).Signal conditioning used for temperature measurement is often a multistage process, whichcan be performed both for non-electric and electric signals Methods applied within varioustemperature measurement systems can be successfully grouped following the classificationproposed in Figure 12.2

Traditional temperature measurement equipment, which is still used widely in industry,often includes non-electric temperature sensors and/or electromechanical measuringinstruments These systems, which are mostly based on processing their inherently analoguetypes of non-electric quantities, exclusively use analogue signal conditioning methods Anexample of such a method is shown in Figure 12.3 Although these methods are not verypopular it is worthwhile to stress that they can also be classified following the generalapproach proposed in Section 12 l

Most of the conditioning performed within temperature measurement systems is nowbased on electric signals both in analogue and digital form These signals are commonlyregarded as a more convenient form This trend is also stimulated by the rapid development

of microprocessors and computers For these important reasons, methods of conditioning ofelectric signals used in temperature measurement systems are discussed in detail below

12.2.1 Transformation of signal nature

Transformation of analogue electric signals is an important group among the methods oftransformation of signal nature For example, it is evident that in determining the actualresistance, RT, of an RTD, based on measuring the voltage, VT, developed across it due to aknown current flowing in it, represents a transformation of resistance to voltage drop Also

at the final stage of this measuring channel, voltage or current can often be transformed intothe movement of the pointer using the electromagnetic torque in electromechanicalindicating instruments

Transformation between main groups ofphysical quantities, as shown in Figure 12.2,also represents an important group of methods of transforming the nature of analoguesignals It concerns mainly the transformation of non-electric to electric analogue signalsand the converse This group includes, for example, opto-isolating elements, used toprovide galvanic separation of different parts of a measuring system, as well aselectromechanical indicating instruments

Transformation ofdigital signal nature is illustrated by a change of digital signal code,which is often applied within microprocessors-based systems This operation often allowsthe elements of digital measuring systems to be simplified or to facilitate signaltransmission The manner of signal coding is determined by its type, values and also its

ANALOG INDICATING

SIGNAL NATURE TRANSFORMATION LINEAR-ROTATIONAL MOVEMENT SIGNAL SHAPE TRANSFORMATION BIMETALIC CORRECTION

OF AMBIENT TEMPERATURE SIGNAL NATURE TRANSFORMATION LIQUID VOLUME - SPRING SHAPE SIGNAL TRANSMISSION

INITIAL TRANSFORMATION TEMPERATURE -.CHANGE OF LIQUID

VOLUME

Figure 12.3 Transformation of non-electric signal in a liquid-filled manometric thermometer

origin and destination For example, the binary code,which represents numbers in radix 2,

is the basic code used to represent integer numbers within digital systems The integervalue, X, within the range 0<_ X<_ (2°-1) can be represented by a binary word of digitalinformation consisting of n-bits in the form:

an-1 an-2 ap aic10,1}

where the value of X may be written as:

X = 2°-l a-,+2°-Zan_Z+ +ao

Another very popular coding method is theBCD (Binary Coded Decimal)form in which

a 4-element binary number is determined for each digit of the decimal number This method

is very often used in digital indicators The comparison of these two digital coding methodsfor representing the decimal number 123 is given below

Themodification, or transformation, of the nature of a digital signal is performed bydigital-to-digital (DID) converters, which may be a part of an integrated processing unit or,rarely, a separate element

12.2.2 Transformation of signal scale

Amplification This is the most important example of the modification of signal scale in a temperature measurement system It can be performed both in analogue and in digital form Analogue amplifiers are usually based on integrated circuit operational amplifiers or specialised integrated circuits A good example of such a module is the AD 594/595 thermocouple amplifier by Analog Devices, which matches type J or K thermocouples It provides a high level signal sensitivity of 10 mV/°C and resolution of 0.5 °C Omega Inc offers the OMNI-AMP series of amplifiers for various signals including those for thermocouples (Omega Inc., 1999) The portable OMNI-AMP 1 amplifier, whose circuit diagram is shown in Figure 12 4, is dedicated for use in indicating and recording instruments It ensures seven fixed gains of ix, 2x, 5x, 10x, 25x, 50x and 100x set by a rotary selector switch The bandwidth of the amplifier, which ranges up to 100 Hz at the highest gain, increases at lower gains This amplifier can also be used when the input impedance of indicating or recording instrument limits the thermocouple lead length or the total circuit resistance When the amplifier gain is set at unity the input resistance of the thermocouple circuit is virtually independent of the indicating device Thermocouple leads

of length up to about 400 m can be used The amplifier is supplied by two batteries with a lifetime of 100 hours or more The OMNI-AMP HB, which is a laboratory amplifier model also designed for thermocouples, is equipped with a reference junction compensation circuit while the OMNI-AMP III'offers a gain up to 1000x The industrial model OMNI-AMP IV

is additionally epoxy encapsulated with a built-in supply.

Digital amplification is performed by software multiplication of digital signal values The multiplication factor, corresponding to the gain, which can easily be set and changed, is not influenced by thermal instability of electronic elements as are analogue circuits.

Unification The scale of a measured signal can also be adjusted to one of the unified ranges ofelectrical signals The most popular unified signals used in measurement systems are:

" unipolar current signals: 0-20 mA, 4-20 mA,

Since current signals are highly insensitive to disturbances, the information can becarried for quite long distances Voltage signals are more sensitive Moreover, it is veryconvenient to be able to detect any damage to the sensor, using signals of low limit valuedifferent from zero The unified 4-20 mA signal, which also allows the structure of ameasurement system to be simplified by applying a current loop, is described inSection 12 5.

Unification of signals, which can be performed both by analogue and digital methods, isusually the final element of various signal processing units It allows the wholemeasurement system to be configured easily and also allows easy reconfiguration byreplacing selected parts

12.2 3 Transformation of signal shape

This group of conditioning methods is characterised by the miscellaneous nature of itsnumerous members

Signal filtering The most popular type of transformation of signal shape is the rejection

of noise from measured temperature signals Stochastic, also called random, noise, whichmay strongly influence the accuracy of the measured temperature signal, is an inevitableinterference in any real temperature measurements It occurs in industrial applications wherestrong nearby electromagnetic fields exist, such as occur in induction heating appliances.The signal-to-noise ratio (SNR) can be improved by applying both analogue and digitalfilters which usually modify the spectrum of the measured signal

The simplest type of analogue noise filter is a passive I st order low-pass RC-filtershown in Figure 12.5(a) The bandwidth of such a filter is determined by the cornerfrequency,f,= 1/(21rRC), and by the asymptotic attenuation, which is 20 dB/decade beyond

f, More effective filtering of high frequency components can be obtained using higherorder filters such as Butterworth, also called maximally flat, Bessel filters, also calledThomson or maximally linearphase, or Tchebishev In some cases notch filters can be veryeffective when narrow band disturbances from power supply units need to be rejected Anexample of an integrated circuit active filter, which can also be used, is shown inFigure 12.5(b) However, when choosing the type of filter both its amplitude and phasecharacteristic should be taken into consideration so that no undesired amplitude or phase

distortion of the measured temperature signal is introduced This applies especially to the measurement of rapidly changing temperature signals.

Digital methods of noise filteringare also often used in temperature measuring systems They usually process the measured signal in the time domain Anaveraging filter,whose output is calculated as an average value from a set of neighbouring temperature samples, as illustrated in Figure 12.5(c), is a simple but effective type of digital filter Such a filter, which is usually supplemented by stochastic analysis of the measured signal, can be a convenient tool for a typical temperature measurement system The comparison of the effectiveness of analogue and digital noise filtering in a computerised temperature measurement system is presented in Section 13.2.2

Additionally, in temperature measuring systems, the problem ofaliasing may occur during the conversion from an analogue to a digital signal form.Aliasing errors,also called

fold-over errors,are caused by the appearance of high frequency components as false low frequency components in the signal spectrum They occur when the bandwidth of a measured analogue signal is above half the sampling frequency It will be recalled that the Nyquist frequency, which equals one half of the sampling frequency is the theoretical maximum frequency which can be captured in a sampling system These errors are often hard to detect and difficult to remove in software Henceanti-abasing analogue filterscan

be placed before the A/D converter to eliminate the high-frequency signal components which would be folded over Thus, they are prevented from causing aliasing errors

Numerical example

A temperature signal is acquired by an 8 channel data acquisition system with a sampling rate

100 samples/second Calculate the sampling rate for one channel, specify the Nyquist rate and comment.

Solution:

The sampling rate for one channel is 100/8 =12 5samples/second.

In this case the Nyquist frequency can be calculated as 12.5/2 = 6.25 Hz

Any signal with a component above6.25 Hz willcause aliasing errors It should be filtered out Figure 12 6 shows the amplitude frequency response of a typical anti-aliasing filter There are three commonly used types of filters: Butterworth, Bessel and elliptic filters Elliptic filters have the sharpest cut-off, but their transient response is not good Bessel filters, which have the slowest attenuation above the cut-off frequency, have the best transient response As a general conclusion it can be stated that the steeper the attenuation

of thefilterthe slower is its dynamic response.

It should also be noticed that analogue-to-digital conversion introduces some filtering

especially in the case ofdual slope, or double integrating, converters The influence of noise can also be reduced by shielding, earthing and channel separation (Omega, 1999).

In recent years more and more sophisticated methods, including artificial intelligence, are applied for data and signal filtering (Russo, 1996) An example of the application of fuzzy logic for noise rejection from the temperature signal has been described by Kucharski (1999).

CUT-OFF FREQUENCY CUT-OFF CHARACTERISTIC

w J

a a

" MI thermocouple of type Kand diameter 3 mm,

" infrared temperature sensor IRt/c by Omega Inc (Omega Inc., 1999) whose output is a voltagesignal corresponding to the characteristic ofa type K thermocouple

It can be noticed that the infrared sensor is more sensitive to noise than the MI thermocouplebecause of its higher input resistance (ranging about kQ) Thus the signal from the IR sensorrequires shielding as well as analogue low-pass filtering In the case of the thermocouple suchfiltering is usually effective on its own

The problem of shielding becomes of paramount importance in industrial applications wherestrong electromagnetic fields exist as, for example, in induction heating processes

IRt/c SENSOR IRt/c SENSOR*ANALOGUE FILTERING

r-~IP~ITII

PPrP ,_°

III, r'tl+o

Linearisation of sensor characteristics Temperature sensors are usually non-linearelements Even if this non-linearity is insignificant it should be corrected to realise orsimplify the whole measuring system This can be done using the inverse characteristic ofthe sensor as shown in Figure 12 8 Linearisation procedure in principle leads to thedetermination of the real temperature value but usually an electric signal proportional to thistemperature is obtained as a result.

Analogue linearisation can be performed by an electronic circuit whose input-outputcharacteristic corresponds to the inverse characteristic of a given temperature sensor Theaccuracy of such a linearisation depends on the quality of the approximation of the realcharacteristic and on the thermal stability of the electronic elements An example of such acircuit is the XTR103 module by Burr-Brown (information from Burr-Brown website)whose block diagram is presented in Figure 12.9

The XTR103 circuit, which is dedicated for RTD Pt-100 temperature sensors, ensuresthe linearisation of a second order polynomial characteristic It is based on two operationalamplifiers with a linearisation procedure performed by changing the measuring currentpassing through the sensor and producing a 4-20 mA unified output signal

Digital linearisation is performed by a calculation procedure in which the true sensor characteristic is approximated by a number of straight lines or polynomials as shown in Figure 12.10 The accuracy of digital linearisation depends only on the quality of the approximation method used Approximation by polynomials is more accurate than by a set

of straight lines but it is also more time consuming The approximating polynomials for standardised thermocouples can be found in Table XI However, RTDs are more linear devices than thermocouples, the Callendar-Van Dusen equation discussed in Chapter4 can

be used for their linearisation as follows:

3Rtg = Ro + Roa "-,5(

where R,9 is the sensor resistance at temperature 9, R,, is the sensor resistance at the reference temperature 9 = 0 °C, a is the temperature coefficient at 9 = 0 °C, S is typically 0.003 92 for Pt, and 8= 0 for 9 > 0 °C and /3= 0.11 for 9 < 0 °C The exact values for the above coefficients can be determined by testing the RTD at four temperatures and solving the resulting equations.

Thermistor characteristics can be very closely approximated by the equation:

1 =A+bInR+C(InR) 3 (12.1)

T where T is the temperature inK, R is the thermistor resistance in 0 and A, B and C are curve fitting coefficients evaluated in a similar way as for the RTD

Automatic compensation of thermocouple reference temperature If the actual reference temperature of a thermocouple differs from the nominal value of 0 °C, the readings require a correction as discussed in Chapter 3 This correction can be performed

compensation, or RJC, also misleadingly called coldjunction compensation, or CJC Both analogue and digital methods can be applied for this purpose.

Analogue compensation of the reference temperature, also called hardwarecompensation, consists of an additional serial connection of a do voltage source, whosevoltage cancels the offset voltage of the reference junction in the thermocouple circuit, asshown in Figure 12.11(a) Some ofthem are given in Chapter 3 Specialised electronic unitscalled "electronic ice-point references" are commercially available for use with anyvoltmeter and with a variety of thermocouples For example a miniature MCJ unit byOmega Inc (Omega Inc., 1999) converts the thermocouple voltage into a signal referenced

at 0 °C, guaranteeing an accuracy of ±0.5 °C for reference temperatures from 15 to 30 °Cand ±1 °C from 10 to 45 °C A special colour code used for MCJ modules allows differentthermocouple types J, K, T, E, R, S for which they are suitable, to be distinguished.Hardware compensation of the reference temperature is somewhat faster than a softwarecompensation procedure but its main drawback is that a different ice point reference circuit

is needed for each individual thermocouple type

Digital compensation ofthe reference temperature is also called software compensation

It can be performed by a microprocessor system based on the output voltage of thethermocouple and the actual reference junction temperature, as illustrated inFigure 12.11(b) This temperature can be measured by a resistance or semiconductor sensor.The idea of the software correction procedure is illustrated in Figure 12.12 The truereference temperature 90, can be evaluated from the actual resistance of the RTD using theinverse characteristic, 9=fRT) It then allows calculation of the equivalent voltage of thereference junction corresponding to 90 , using the thermocouple characteristic E =f(,9) Thevoltage correction value due to the difference between the nominal 90n and the actualreference temperature, 90, can also be determined using the same characteristic function.Finally this correction value is added to or subtracted from the output voltage of thethermocouple so that the correct value of the measured temperature can be determined usingthe inverse characteristic, 9 =f(E), ofthe thermocouple

During digital compensation of the reference temperature, it is evident that variouscharacteristics have to be referred to Hence, this method is a little more time consumingthan analogue compensation However, software compensation is a more flexible andversatile approach It enables the simultaneous correction of reference temperature ofthermocouples ofdifferent types whose reference junctions are at the same place

Correction of dynamic properties of temperature sensors This type of correction isalso a kind of transformation of signal shape and can be performed using both analogue anddigital methods The general block diagram of the correction is shown in Figure 12.13 Thistype of correction supplements the temperature measuring channel by a dynamic element ofequivalent structure and parameters chosen so that the inherent inertia of the sensor can be

METHODS OF SIGNAL PROCESSING IN TEMPERATURE MEASUREMENT 241

E

Figure 12.12 Idea of digital compensation ofreference temperature

CORRECTOR

Figure 12.13 General idea ofcorrection oftemperature sensor dynamics

compensated The theoretical background and methods of application of such a correctionfor various types oftemperature sensors are discussed in Chapter 15

Detection of characteristic values of measured signal This group of methods of signalshape transformation consists of several procedures, as shown in Figure 12 14:

" Peakpicker orpeak-holderdetects the highest instantaneous value of the signal in acertain time period Sometimes this value is held with a slow, adjustable decay rate Theoutput signal of this procedure follows the upper envelope of the measured signal

" Valleypicker or valley-holder exhibits a complementary inverse action to the picker procedure by holding the lowest readings ofthe signal

peak-" Averager presents the mean value of a varying signal It is mostly used when themeasured signal fluctuates periodically or stochastically An average value can beformed with an adjustable averaging time set

" Peak-to-peak holderdetects the peak-to-peak span or amplitude of signal variations

TEMPERATURE SENSOR

SIGNAL

AVERAGED SIGNAL

t (c)

of applications where they can be necessary are temperature measurement

of-o targets temporarily covered by dust, steam or smoke,

" a succession ofsmall parts to be viewed with spacing between them,

" moving surfaces covered by oxides,

" non-moving inhomogeneous surfaces

All of these procedures can be realised using both analogue and digital methods, asshown in Figure 12.15 for a peak-picker In the analogue circuit, shown in Figure 12.15(a),the capacitor, C,, is charged to a voltage proportional to the instantaneous measured signalvalue Diode, Dl, prevents the capacitor, CI, from discharging when the input signal isdecreasing Transistors, T1 and TZ, and amplifier, 2, convert the capacitor voltage into aproportional output current A capacitor discharge time can be set by a potentiometer, P1, sothat the decay rate for the maximum value can be adjusted