Multi-load cells weighting systems are based on a platform supported by four or more load cells, normally in parallel inputting lhe same signal conditioning unit. Because of mechanical and electrical paralleling tuning the gain of a load cell affects lhe behavior of the others, making the calibration difficult and tedious, specially withweightbridges for cars and trucb, requiring lhemotion of heavy weights around /arge p/atforms

Correia, J. H.

Couto, Carlos

Tavares, Adriano

Universidade do Minho: RepositoriUM

Calibratioo of a Multi-Load-Cells WeightiogSystem Based 00 Neural NetworksTavares, A.Higino, J.Couto, C.Dep. Electrónica IndustrialUniversidade do Minho - 4709 Braga Codex, Portugal(FAX +351 53615046, phone +351 53615010)ABSTRA crMulti-load cells weighting systems are based on ap/atform supported by four or more load cells,normally in parallel inputting lhe same signalconditioning unit. Because of mechanical andelectrical paralleling tuning lhe gain of a load cellaffects lhe behavior of lhe others, making lhecalibration difficu/t and tedious, specially withweightbridges for cars and trucb, requiring lhemotion of heavy weights around /arge p/atforms. Aso.ftware calibration can be achieved by calculationof lhe multipiying coefficients, which are given by lhesolution of a seI of linear equations, operation easiiyperformed by any general purpose microcomputer.This paper shows that lhe weight output can a/so becalculated using neural networb techniques. Thisnew calibration system is based on a very simplebackpropagation neural network with as many inputneurons as lhe number of load cells (four in ourcase), six hidden neurons and one linear outputneurons. We trained lhe system byexamples (i.e. withexamples data pairs) to determine lhe synapseconnections.Key Words: NeuraI Networks, BackpropagationNetwork, Learning Process, Nguyen-Widrow method.INTRODUCnON~eo~ é . E> Y ':Cf~~YL~\J-Af; b 1\A L C"O'tJÇeVtg~ C& D lU:5> [1s-rJ Pr'l.. {{12:0 {!..e7" { 1\/ CJ f<tP(! \ \ t<- ~ -o 1'1" S A:~ 1>-1~:t~~(oG-7 Lr-.e_s..~1 'f!J5) . f;o~~rJ/ ~A-) vsA;The use of load ceUs with digital outputs (smart loadcells), i.e., with integrated signal processing, allowsthe gain adjustment to be a simple multiplication of theload ceU numerical output by a coefficient, operationwhich does not affect the other load ceUs outputs.Each smart load ceU uses a singie chip RISC typemicrocontroller with very few other active and passivecomponents around, taking advantage of theratiometric functioning of the load cells. Once themicrocontroller is needed, ali architecture should berethought in order to maximize its use, reducing thehardware and its specifications. The need for thermallystable circuits and components is minimized throughthe use of the salDe amplification chain for both signaland reference, together with software calibration anddigital filtering.However, this solution needs a cost effective signalprocessing circuit including amplification, A-to-Dconversion and networking capabilities.Having in rnind industrial weighting applications where6000 divisions are needed for the equipment (externaldivisions) a conversion resolution of at least 60000divisions (10 internal divisions for each extemal) with50 or more readings per second, at least for staticweighting applications. For dynamic weighting a fasterreading rate may be required but nonnaUy with lowerresolution.For the specifications referred there are alreadysuitable components in the rnarket, namely amplifiersand A-D converters [1,2], some of these even withnetworking facilities [2]. However some otherfacilities toward intelligent sensing [3,4] are alsodesirable: amplifier gain and offset adjustmentcontrolled by software, scaling and eventually digitalfiltering of the converter results.This contribution describes a feasibility study towardsa solution for the problem taking advantage of thehigh performance low cost microcontrollers availableof the ratiometric functioning of the loadtoday andcells [2].SIGNAL PROCESSING CIRCUITSmar1 Load CellFig. 1 shows lhe cooceptual idea of ooe Smart LoadCell:~~Fig.l- Circuit block diagramThe salDe amplifier chain is properly switched to dealwith lhe zero, lhe load ceU signal and lhe conversionreference. In this way lhe thennal stabilityrequirements for lhe amplifier can be relaxed as achange in gain wiU affect alI lhe three entities definingthe A-D conversion. A simpler architecture can beused just enough to ensure that lhe gain remainsconstant during lhe conversion period.A single ramp conversion was used because it requireslhe rninimum hardware and it aUows a higher rale ofconversions. However this simple conversiontechnique is not intrinsically compensated as dual rampconversion for example, requiring somepos-conversion processing specially in this case wherethe zero, the signal and the reference are alIowed tochange.The counting associated with lhe single ramp A-Dconversion, lhe control of lhe switches in lhe amplifierstage and lhe control of lhe ramping capacitordischarge are tasks to be performed by amicroprocessor architecture. Some number crunchingto work out lhe conversion result, which may includescaling, eventually some digital filtering and lhecommunication with the outside world, are lhe othertasks to the microprocessor.The Microcontroller: The circuit was developedaround ao 8 bit single chip Harvard architecturemicrocontroller with RISC-like features, thePI C 17C4 2, with interesting characteristics for thistype of applications:- operating speed: OC - 20 MHz clock input (200nsinstruction cycle).- Low cost.- Small size with EPROM.- Low power consumption.- Three 16-bit timer/counters.Fig.2 shows a modem solution for a Multi-Load-Cellsindustrial weighting system with a cable to traveI overalI Smart Load Cells.The conversion time obtained was around 18rns, timewhich can be reduced by increasing lhemicrocontroller clock frequency up to 25 MHz. Theresolution attained of 60000 divisions can also beincreased .1 L91MODERN SOLUTIONUnitFig. 2- Multi-Load-Cells weightingA SIMPLE CALmRA TION PROCESSThe calibration process means the calculation of lhemultiplying coeflicients, which are given by lhesolution of a set of equations, operation easilyperformed by any general purpose microcomputer.To test the software calibration method for themulti-Ioad cell weighbridges, it was decided to usestandard readout units instead of the prototypesabove referred. A local weighting equipmentmanufacturer made available two 4 load cellsplatforrns and 8 digital readout units withnetworking facilities. Load cells taking a rnaximumnominal weight of lOOkg, with 3000div resolutionand a sensitivity around 2m V N, were used. The4-load relI platforrns coupled to a singie readoutunit is rated to 200kg with a resolution of 1 OOgr.Each of the readout units were calibrated to givearound 60kg with a 20gr resolution.Two sets of tests were done, one for a 4 load cellsplatform, and another for a 8 load cells system,using two 4 load cells platforrns.The calibration method consists on doing Nreadings of weight on each load ceU obtained bymoving a mass with a known weight around lheplatform. The number of readings is lhe salDe as lhe~~~~- no summing box- simpler cabling and lessprone to interference- software calibration possiblesystemnumber of load cens under lhe platform. The bestresults are given by lhe N readings obtained,concentrating lhe weight as much as possible aboveeach one ofthe N load censoFor the 4 load cells platfonn 4 sets of 4 readingswere made, and lhe weigbts found were used toworkout the multiplying coefficients. These factorsaffecting each one of lhe readings, enables lhecorrect evaluation of lhe weight above the platfonn.A systern of 4 equations and 4 unknowns was built:KIWll + K2W12 +K3W13 +K4W14 = WKIW21 + K2W22 +K3W23 +K4W24 = WKIW31 + K2W32 +K3W33 +K4W34 = WKIW41 + K2W42 +K3W43 +K4W44 = WThe solution of this system gives lhe K factorsrequired to evaluate lhe weight of an unknown mass(with lhe W rc readings calculated with a calibratedmass positioned in four different piares). With thefollowing W rc readings calculated with a calibratedmass of 20kg positioned in four different piares:. W lc readings 3.86, 9.00, 6.82, 0.72;- W2c readings 1.74, 2.94, 10.88, 5.58;- W3c readings 4.50, 0.74,3.54, 13.26;- W 4c readings 13.30, 2.92, 1.48, 4.38;the Kc factors evaluated:1iJ,o;~Kl = 0.90025, K2 = 0.91580, KJ = 0.99196,K4 = 0.88685.Afier calibration lhe resolution of lhe next equationdetennines lhe weight of lhe mass on lhe platfonn(with Wi's lhe output's of each one Smart loadCell).Mass = Kt*Wt+ K2*W2+ KJ*W3+ K4*W4Using the K's factors several (25) weightingoperations were done, with different masses, locatedin different points of the platform, having beenrecorded very encouraging results, with errorsbelow SOgr (4000 divisions in 200kg).For the composite platform with 8 load cells the testwas repeated and the 8 multiplying coefficients werecalculated. The weighting tests done confirmed theapproach followed giving errors below lOügr, i.e.again 4000 divisions in 400kg.NEURAL NE1WORKS CALIBRA TIONThe reasons why another approach using a BPN(Back Propagation Network) network was tried,comes from the following facts:- Three-Iayer feedforward networks are universalaproximators, that unlike other systems offunctions such polynomials have associated astraightforward methods for adapting the net toapproximate a given function [7].- The generalization capability: The networktends to give reasonable answers whenpresented with inputs that they have never seen.That's to say, a new input willlead to an outputsimilar to the correct output for input vectorsused in training that are similar to the new inputbeing presented [8].- If an algorithm can be called a neural network,its chances of attracting attention are vastlyincreased (neural networks crazy) [6].The training of a BPN has three stages: thefeedforward of the input training pattern, thecalculation and backpropagation of the associatederror, and adjustment of the weights. After training,application of the net involves on1y the computationof the feedforward phase. Even if the training isslow, a trained net can produce its output veryrapidly.The action of the BPN is detennined by two things:lhe architecture - number of input neurons, hiddenneurons and outputs - and lhe value of lhe weights.The number of the inputs and outputs neurons aredetermined by the application. The system builtdictates 4 input neurons, one for each load cell, andone output neuron since the desired output is anumerical value in weighting range.Early it was used binary outputs each onerepresenting a bit of the desired numerical output.With this approach the network size increases a lotand slowing down the training and feedforwardphases.Having the number of input (n- input) and outputneurons (n_output), lhe kemel of the problembecomes the choice of the hidden neurons:- too few it will starve the network of theresources it needs to solve the problem [6].- too many will increase the training time,perhaps making it so long that it becomesimpossible to train adequately in a reasonableperiod of time ( and sometimes may lead tooverfitting problem) [6].A good starting point could be the use of 2 hiddenneurons (n_hidden) found by applying the geometricpyramid mie [6]:n_hidden = ~ n - input * n - outputWith this architecture the network did not convergeto any acceptable elTor probably because the1tj3problem is too So, a criterion forevaluating the performance (based onMSE-Mean Square ) was fixed, the number ofhidden neurons was then slightly increased, andnet trained and tested for an acceptableperformance. This process was repeated until lheerror was found acceptable, or no significantimprovement was noted. After fixing the number ofhidden neurons, the following strategy revealedgood to improve the network performance:- lnitially lhe BNP is trained with a goal errorslightly above lhe best one and final weightsand biases recorded.- Afterwards the training is repeated using therecorded values as the initial conditions, andthe goal error is decreased. This process canbe repeated till the fmaI solution.Applying lhe above ideas and lhe Nguyen-Widrowmethod to find lhe initial weights, lhe final setup forlhe training phase was:Learning rate: 0.02Error~oal: 10gMomentum: 0.92n hidden: 6Max- epoch: 10000EXPERIMENTAL RESUL TSBelow. the training and feedforwardfollowing reduced training setTraining Set:Nonnalized31141646381121261365356111365kg IOkg 20kglOkgThe optimal weights and biases for input/hiddenlayers were then found:wib0.4124 ~.2980 ~.4614 ~.69260.4508 0.0265 ~.1695 0.14330.9989 0.1822 0.0146 0.60480.1111 0.6920 ~.0642 ~.9339~.5336 ~.1158 ~.4256 0.0689~.3784 0.6830 ~.6433 ~.OO30b-th=[0.9107, 0.4966, 0.1092, 0.7815, 0..2497, 0.6841}The weights and biases for hidden/output layers(whooboo>:woo=(~.1327, 1.4564, ~.0933, ~.3123, 0.3769, 0.1365]~=O.9150In the feedforward phase, samples that do notparticipate in the training were tested giving a finalCITor :i:20gr.The software was implemented in c++ onWindows environment (using OWL). To test lhesoftware and validate lhe final results, NeuralNetwork Toolbox (MATLAB) was used tocompare the performance.CONCLUSIONSThe training was realized using few input samples(due the time consuming with toa large training set).The results obtained are very encouraging, since thefinal error is smaller than the one obtained with thefirst calibration processoOthers improvements were also registro:- The weighting measurement was found lesssensitive to the position of the load in lheplatfOTD1.- Better linearity ali over the weighting range.The modification of the training method is nowbeing tried in order to incorpora te an adaptiveresults over lheare presented:336115801761 'L~ ~learning - the leaming rate is changed according tothe convergence trend.ACKNOWLEDGMENTSWe wish to thank CEL-Cachapuz Electrónica Lda, aelectronic weigbting equipment manufacturer, forpromptly making available to us lhe a Multi-LoadCells weigbting system used in this work.REFEREN CES[I] "A20-bit(lppm)LinearConverter", ApplicationSemiconductor.Slope Integrating A/DNote 260, National[2] "lntelligent Signal Processing for RatiometricData Acquisition: A Low Cost Solution forLoad Cells", C. Couto, J. Higino, ProceedingsoflMEKO TC-4, Brussels, Belgium, 1993.[3] "Integrated Smart Sensors", I.H. Huijsing,Sensors and Actuators, Vol AJO, ppI67-174,1992.[4] "Modelling or Smart Sensors: Applicatioo to aSmart Temperature Transmitter", J. M. Riviére,M. Robert, C. Aubrun, Proceedings or IMEKOTC-4, Brussels, Belgium, 1993.[5] "Smart Load Cells Based 00 Switched-Capacitors", C. Como, J.Higioo, Proceedings orICSPAT' 94, Dallas, USA- 1994.[6] "Practical NeuralTirnothy Masters,Network Recipes inAcadernic Press, 1993.[7] "NeuraI Network and Fuzzy Logic Application inC/C++", S. Welstead,Wlley & Sons, 1994.[8] "Fundamentals of Neural Networks",F ausett, Prentice-HalI, 1994.[9] "Backpropagation in Perceptrons withFeedback", L. B. Alrneida, Neural Computers,Berlin. Spring-Verlag. pp. 199-208, 1988.[10] "A Method for Self-Detennination ofAdaptive Leaming Rates in Back Propagation",M. Weir, Neural Networks, 4:371-379, 1991.C++",Lauren1255

Calibration of a multi-load cells weighing system based on neural networks

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