Kuala Lumpur, Malaysia, March 8-10, 2016 Virtual ...ieomsociety.org/ieom_2016/pdfs/568.pdf · Keywords— virtual, instrument, pressure, artificial, intelligence. I. INTRODUCTION

Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

Virtual Instrument for Monitoring Process Parameters: A Versatile Tool for Engineering Education

Amar Partap Singh Pharwaha, Professor Department of Electronics & Communication Engineering,

Sant Longowal Institute of Engineering & Technology (SLIET)-Deemed University, Longowal-148106 (District: Sangrur), Punjab, India

[email protected]

Abstract—With the advent of interactive multimedia programming languages and world-wide-web (www), it has now become possible to develop virtual laboratories that can be accessed globally. Experiment-oriented problems may be offered without the overhead incurred on comprehensive hardware workshop or laboratory. Based on these considerations, the present paper reports the development of a novel Virtual Instrument (VI) for monitoring process parameters. The proposed VI can also serve as an excellent educational tool as well. This is designed using object-oriented, fourth-generation programming technology. The executed user-interactive VI ensures efficient data acquisition, management and presentation. It is developed as an integrated system using five different Virtual Instruments (Sub-VIs). Each of the five Sub-VIs, was executed as a standalone Virtual Instrument for a particular application. The dedicated VIs were integrated to execute the proposed VI for different applications of transducer modeling. These applications include estimation of nonlinearity of transducers, transducer linearization, self-compensation in transducers and fault detection in transducer based systems. Such applications are frequently required in Data Acquisition System (DAS) connected transducers. In order to evaluate the performance of the proposed VI, Data Acquisition Based pressure transducer has been chosen in the present work because these transducers find wide applications in process control.

Keywords— virtual, instrument, pressure, artificial, intelligence.

I. INTRODUCTION

Web-enabled instrumentation has facilitated real time monitoring and control of any data [1] from anywhere at any time. It is perhaps one of the cutting-edge expansions that have occurred in the last two decades or so in the field of electronics and instrumentation. Probably, it is bound to become a common-man tool invariably in the near future. The days are not far away for the common man to use this technology as a plug and play toy [2, 3]. This scenario has, in fact, forced the researchers to work hard in the area of distributed, multiplatform, internet-enabled virtual instrumentation [1, 4, 5, 6]. On account of these facts, computer-augmented instrumentation has in fact, attained an important position. In addition to this, when simultaneous monitoring of several parameters is needed, the computer-augmented instrumentation serves as an extremely good choice [7]. In fact, it has become the most dominating and frontier field of research in the present scenario. However, this area is really very much dynamic as well as challenging too. The reason for the same is that a number of problems are already prevailing with this system. In addition to this, a lot of new problems is expected to be generated in the coming years. Thus, the need of the hour is to devise intelligent instrumentation [8] using artificial intelligence. In recent years, application of Artificial Neural Networks (ANNs) has proved to be an emerging area of research under the realm of artificial intelligence since these systems have the potential of modeling nonlinear systems accurately on account of their adaptive nature [9, 10]. In this context, function approximation is one of the most important applications of ANNs. However, there are number of applications of adaptive ANN algorithms in function approximation. In the field of electronic-instrumentation, these applications include line-enhancement [10], channel equalization [11] and system identification [12]. Two algorithms, Least Mean Squares (LMS) and Recursive Least Squares (RLS), are used frequently for function approximation in these applications [11, 13]. However, the core of applications are implemented using LMS algorithms instead of RMS? Based on these considerations, LMS algorithm has been used in the present work. The reason for choosing the same is its easy implementation and robustness [10, 13]. Now, the question arises-why to choose ANN based regression in place of conventional numerical interpolation/regression methods? A lower order polynomial interpolation, using conventional numerical methods, (Lagrange or Newton interpolation or LMS regression) is usually an optimal solution for most linear characteristics [14]. However, when nonlinearity rises, it is problematic to bargain the best polynomial degree that minimizes interpolation error with conventional approaches [15]. Moreover, increasing polynomial degree produces numerical instability sometimes due to well-known problem of oscillations associated with the conventional polynomial interpolation [14, 15]. Also,

1969© IEOM Society International


when extrapolation, multivariable and nonlinearity characteristics are under investigation in the occurrence of noise and environmental errors, as prevalent in the instance of transducers, it is difficult to estimate the intermediate value from polynomial coefficients [15]. Sometimes, it is also prevalent that some fictitious behaviors may be added and very large errors can occur if the number of values used for interpolation is large, which is generally required in the case of conventional numerical interpolation [15]. ANN interpolation achieves the better results with lower calibration points [15]. It has also been established that ANN interpolation is more precise and supple than polynomial interpolation based on classical methods [15, 16, 17, 18]. In the context of the present work, it has been also observed that a number of process parameters can be monitored using Wheatstone bridge oriented transducers attached with specialized mechanical elements. These parameters include pressure, load, vacuum, flow rate, temperature, physiological parameters, etc. This indicates versatile applications of these transducers in science, engineering and technology. Based on these observations, the performance of the proposed VI has been evaluated for Wheatstone bridge oriented pressure transducer. The output signals from such transducers are connected to four input channels of the Data Acquisition System (DAS). On the basis of a survey made in 1989, it has been established that pressure transducers capture nearly 60% of the complete transducer market [19]. The typical areas of application of pressure transducers include biomedical instrumentation, missile testing, rocketry, monitoring of pressure in process industries, depth study in oceanography, satellite applications and wind tunnel experiments, [20]. The object-oriented integrated programming technology permits higher level of software modularity and provide the features needed for data acquisition, analysis, testing, simulation and modeling. All these features are required invariably to implement the proposed Virtual Instrument (VI). Based on these considerations and as a part of the proposed Virtual Instrument (VI), in the first instance, development of the Virtual Estimator (VE) [21, 22] is examined as an independent standalone VI for the estimation of transducer static non-linearity as well as its input-output static response. Here, Multilayer Feed-forward Back-propagation Artificial Neural Network (MLFFBP-ANN), trained with Levenberg-Marquardt learning rule, is executed as signal processing component of the proposed Virtual Estimator. After the successful analysis of the executed VE, the performance of the Virtual Linearizer (VL) [23] is reported for linearizing nonlinear static response characteristics of transducers. The soft-linearizing component of theproposed VL is executed in the form of neural model designed on the architecture of MLFFBP-ANN trained with Levenberg-Marquardt learning rule. Next, the performance of the Virtual Curve Tracer (VCT) [24] is studied for accurate fitting oftransducer characteristics under the effect of a disturbing variable using MLFFBP-ANN trained with Levenberg-Marquardtlearning algorithm as its signal processing component. Similarly, the performance of the executed Virtual Compensator (VC)[25] is also achieved for compensating the effect of transducer nonlinearity under the influence of a disturbing variable usingMLFFBP-ANN as a soft compensator. In the last, the performance of the Virtual Fault Detector (VFD) [26] is examined fordetecting and identifying various faults in Wheatstone-bridge oriented transducers.

II. ARTIFICIAL NEURAL NETWORKS

The human brain is able to do complex tasks by its ability to build up its own rules through experience since childhood. Artificial neural networks (ANNs) have been studied extensively with the objective of attaining human like performance in the areas of pattern recognition [27]. These are also known as neural nets, connectionist models, parallel distributed models or neuromorphic systems [12]. In its most general form, an artificial neural network is a mathematical paradigm that is used to model the way in which the brain performs some particular task. The advantage inherent in the parallelism of neural networks, together with the simplicity of the constituting computing nodes, makes these networks highly attractive for the solution of a number of pattern recognition problems [28]. The DARPA Neural Network Study Report [29] lists various commercial applications of neural networks, including a process monitor, adaptive channel equalizer, word recognizer, a risk analysis system and a sonar classifier. A general characteristic of ANNs is the ability to classify streams of input data without knowing the explicit knowledge of rules of classification. Further, these networks make the use of arbitrary patterns of weights to represent the memory of categories.

Several ANN architectures were described for signal processing applications in the field of instrumentation [30]. These applications include signal interpretation, calibration of devices, fault diagnosis, fault isolation, generation of tuning sets and interpretation of measurement results, etc. Based on network architectures, three classes of ANNs are defined including Single Layer Feed-Forward (SLFF) networks, the Multi-Layer Feed-Forward (MLFF) networks, the Recurrent Networks [12]. However, the multilayer feed-forward networks are mainly applied in different applications of transducer modeling in computer based instrumentation. Moreover, under this category, the core of neural processing applications used ANN architectures designed on the topologies of multilayer feed-forward back-propagation (MLFFBP) networks [31, 32] for such applications. Multilayer feed-forward back-propagation (MLFFBP) ANN has been applied effectively to a number of different problems since the introduction of the gradient descent back-propagation training algorithm. This network architecture comprises of an input layer, an output layer and one or more hidden layers of computational nodes.

An emblematic MLFFBP-ANN standard is designed in the form of layers of neurons as illustrated in Fig.1. The input signal (s) circulates layer-by-layer through the depicted network in the forward direction. The action of the gradient



Hidden layer-2

Fig.1 Typical multilayer feed-forward artificial neural network

1x

2x

3x

Nx

1y

My

Input layer

Hidden layer-1

Output layer

descent back-propagation learning rule comprises of a double phase. The first phase is generally called as forward-pass and the next phase is known as the backward-pass. In the forward-pass, an input vector is presented to the network that leads to an output, y, at the output layer. The synaptic weights are kept fixed during this phase. In the backward-pass, the weights are adjusted on the basis of an error between the actual and desired outputs [12]. The MLFFBP-ANN has achieved a grand success as a universal approximator, that is, this algorithm can approximate any continuous, multivariate and multimodal function to the desired degree of accuracy subject to the condition that sufficient number of hidden neurons exist [33]. However, MLFFBP-ANN trained with basic gradient-descent learning algorithm suffers from the drawback of slow convergence, that is, longer training time and higher probability of getting converged to the local minima. A number of methods are reported to improve upon the basic gradient descent algorithm using heuristic techniques. These techniques include adaptive learning rate and momentum based techniques [34, 35]. Similarly, a number of other learning algorithms have also been reported for the faster learning of MLFFBP-ANNs using standard numerical optimization methods [36]. The most popular classes of faster learning algorithms include conjugate-gradient methods and on Newton’s methods. The latter, however, includes Levenberg-Marquardt learning algorithm [34] as an extremely important learning algorithm.

III. IMPLEMENTATION STRATEGY In the present work, the implementation of the proposed VI is carried out by adding layers of software and hardware to personal computer as a custom designed instrument. The details of experimental setup is given below along with the execution of necessary software components:

A. Experimental Setup The hardware and software modules of the proposed VI are integrated resulting in the synthesis of proposed virtual

instrument that manipulates data in real time from transducers. The block diagram of the experimental setup is illustrated in the operational phase as depicted in Fig.2. The major equipment includes Personal Computer, General purpose signal conditioning board (EXP-GP Board of Keithley), Data Acquisition System (DAS-1601 Board of Keithley), Primary standards calibration workstation (Test Bench-1100 from Nagman’s Instruments) and Wheatstone Bridge Oriented Pressure Transducer (SenSym-S×100 DN) of Industrial Grade. The input to the pressure transducer is applied from the standard source available with the calibration workstation. Output signals from the sensor/signal conditioner are digitized by a data-acquisition card and transferred to personal computer after suitable processing, where actual data manipulations are performed. The proposed virtual instrument comprises of a highly customized user-interactive Front-panel and five sub-panels, as illustrated in Fig.3. The software component of the proposed VI comprises of the execution of two separate modules [37]. The first module is called Data Acquisition and Management Software (DAMS) and the second one is known



Fig.3 Conceptual Illustration of the Proposed Virtual Instrument (VI)

Front Panel

Virtual Estimator

Virtual Linearizer

Virtual Compensator

Virtual Fault Detector

Virtual Curve Tracer

as Signal Processing Software (SPS). The DAMS is composed of the proposed Front-panel and five Sub-panels. Each sub-panel is synthesized as a standalone VI (Sub-VI) to serve the particular application for which it has been executed. In fact, the proposed Front-panel and five sub-panels are synthesized in the form of two integrated modules [4, 15, 37] as detailed below:

B.Execution of Data Acquisition & Management Software (DAMS) Module Data Acquisition & Management Software (DAMS) of the proposed VI was developed in the form of different panels. This module performs the function of data acquisition and management including inter-platform data exchange, inter-panel data exchange and intra-panel data exchange. In this work, the integrated programming language, Test-point software (Version 3.3) was used to design the DAMS module [38]. Test-point employs object-oriented, drag-and-drop technology, to bring both power and simplicity to data acquisition and management. The user-interactive Front-panel of the proposed VI is synthesized using Test-point software [38]. The executed Front-panel is shown in Fig.4. Is highly customized for the present application. After deciding the various functions to be executed in the proposed VI, an Algorithm of Control & Computation (ACC) was developed in a Test-point environment. The different Test-point objects are carefully researched, configured and interlinked to synthesize different modules of the Front-panel with clear nomenclature. Help Menu is designed on a separate panel, which is invoked by having a mouse click on the pushbutton (Help) available at Front-panel. Front-panel is activated by

Transducer under Calibration

Primary Standards Calibration Workstation

Parameter Indicator

Data Acquisition System

Transducer Signal Conditioner

Front-panel (User-interactive

Interface) Personal

Computer

Fig.2 Experimental Setup in Operational Phase



Fig.4 Front Panel of the proposed Virtual Instrument (VI)

pushing the ‘Switch’ button to its ‘ON’ position. With the pushing of ‘Swtich’ button to its ‘ON’ position, the current date and time are displayed automatically. The source code for the action of the Front-panel and sub-panels was written as an integrated module to perform different tasks in a coordinated fashion. A runtime disc of the Test-point Source Code of nearly 2.00 MB for the DAMS module was prepared for the purpose of experimental evaluation of the proposed VI. The input-output data obtained from transducer under calibration using VI is exchanged with the MATLAB and Excel environments through dynamic data exchange (DDE) method [38, 39, 40]. For having a user-interactive measurement interface for the human operator, each of the five Sub-VIs was synthesized in the form of a separate panel as an integrated module. Each of the Sub-VIs includes several areas which correspond to the main functions of the system. These include subsystem configuration, communications between different system modules, data measurement, control, presentation [41, 42].

C. Execution of Signal Processing Software (SPS) Module

The Signal Processing Software (SPS) Module of the system involves the implementation of a number of ANN models, each is designed to serve a particular function. These ANN models extract information, stored on the numerical values of the acquired voltages, delivered by the transducer under calibration. This software is responsible for performing the functions of transducer modeling, self-linearization, self-compensation for environmental variations and generation of residuals for fault detection & isolation in transducer elements of the VI. The signal processing software that performs these functions in the present work used MATLAB Programming & Neural Network Toolbox. Neural network toolbox facilitated the implementation of various algorithms, for these functions using Multilayer Feed-forward Back-propagation (MLFFBP) ANN [41. 42]. Training a neural network is, in fact, an application of numerical optimization of a non-linear error function in general. However, for the optimization of non-linear functions, no single best method is available. The characteristics of the optimization problem determine the appropriate method to be chosen as a potential solution to the given problem. For objective functions with continuous second derivatives, which include feed-forward networks with differentiable activation and error functions, three general types of algorithms have been suggested as effective solution for most of the practical problems [35]. Stabilized Newton and Gauss-Newton algorithms have been suggested for small number of weights. These include various trust-region algorithms and Levenberg-Marquardt algorithm. Similarly, for the applications with moderate number of weights, different quasi-Newton algorithms are effective. Similarly, However, various conjugate-gradient algorithms are found to be good choice for applications with large number of weights. In practice, Levenberg-Marquardt training algorithm usually finds an optimal solution for a number of problems [34, 36]. Based on these considerations, this algorithm has been applied in the present work for the execution of different neural models of the transducers. It has already been established that Levenberg-Marquardt algorithm (like the Quasi-Newton method), approaches second-order training speed without computing the Hessian matrix (second derivatives) of the performance index at prevalent values of the weights and biases [34]. Instead of this, the Hessian matrix is approximated as

H = JTJ (1) Similarly, the gradient is computed using the following expression:



g = JTe (2) where, e, is a vector of network errors and J is the Jacobian matrix containing first derivatives of the network errors with respect to the weights and biases. The Jacobian matrix is calculated using standard back-propagation technique. The standard back-propagation technique is comparatively much less complex as compared to the usual computation of the Hessian matrix. Thus, Levenberg-Marquardt learning algorithm based optimization of a non-linear activation function employs following Newton-like update:

Wi+1 = Wi - [ JTJ + μ I ]-1 JTe (3)

IV. RESUTS AND DISCUSSION

A. Neural Modeling of Transducers In the first instance, the performance of the proposed VI is examined for the estimation of transducer static non-linearity as well as its input-output static response using neural modeling [21, 22]. Neural modeling of strain-gauge based pressure transducer is executed on the architecture of MLFFBP-ANN trained with Levenberg-Marquardt back-propagation learning algorithm that comprises of two phases. In the first phase, the use of proposed neural modeling technique [21, 22] is examined to approximate an optimal direct model of transducers under static conditions using proposed VI. The Virtual Estimator [21] is invoked with mouse-click of the corresponding Pushbutton (Estimator) provided on the Front-panel of the proposed VI for this very purpose. The executed neural modeling technique provides an extremely fast speed of convergence with smaller computational load and lower architectural complexity with a desired degree of accuracy. In the second phase, the performance of the proposed VI is evaluated for linearizing nonlinear input-output static response of transducers using MLFFBP-ANN, trained with Levenberg-Marquardt learning algorithm. The Virtual Linearizer [22] is invoked with mouse-click of the corresponding Pushbutton (Linearizer) provided on the Front-panel of the proposed VI for this very purpose. The inverse model of the transducer is evolved in a manner that the combined transfer function of the transducer and its inverse model becomes unity in an iterative manner. As a result, the effect of associated nonlinearity is neutralized automatically and the measurand is estimated accurately at the output of the inverse model irrespective of the associated static nonlinearity of the transducer. The performance of the proposed VI is summarized in Table-1 including the results of neural modeling [21, 22] of Strain-gauge based pressure transducers. From the results indicated in Table-1, it has been established that the proposed VI is very much effective for estimation of transducer static non-linearity as well as for automatic compensation of this parameter without the use of any extra hardware. This results in drastic reduction in the extra hardware required invariably for the purpose of linearizing the transducers. Moreover, same technique can be used effectively for other type of transducers as well. This adds the feature of flexibility as well as versatility of the proposed VI.

B. Neural Modeling of Transducers under the Influence of a Disturbing Variable In this section, the performance of the proposed VI is analyzed for the estimation of transducer response characteristics influenced by a disturbing variable. The Virtual Curve Tracer [23] is invoked with mouse-click of the corresponding Pushbutton (Curve Tracer) provided on the Front-panel of the proposed VI for this very purpose. For this purpose, the ANN is trained in a parallel mode in system identification configuration [41, 42] to estimate the direct response of transducers. In this context, two dimensional (2-D) problem of transducer direct modeling is addressed using MLFFBP-ANN [23]. The purpose of direct modeling is to obtain an ANN model of the transducer such that that outputs of transducer and its ANN model match closely. The fastest learning algorithm, Levenbeg-Marquardt, which is a variation of the Gradient Descent Back-propagation learning algorithm, is used to train the proposed MLFFBP-ANN based model [23, 24]. In addition to this, the performance of the proposed VI is also examined for correcting the effect of a disturbing variable in DAS-connected transducers. The Virtual Compensator [24] is invoked with mouse-click of the corresponding Pushbutton (Compensator)

Table-1 Neural Modeling of Strain-gauge Based Pressure Transducer (SenSym-S×100DN) [21, 22]

Neural Modeling

Number of hidden neurons

Achieved target Maximum error between actual and estimated response

Epochs MSE Absolute error Error (%Full Span) Training

data Validation

Data Training data Validation

data Direct

Modeling 2 91 3.06e-06 0.021 0.116 0.416 2.389

Inverse Modeling

2 69 7.89e-06 0.035 0.148 0.497 2.248



provided on the Front-panel of the proposed VI for this very purpose. The correction is carried out by a nonlinear two dimensional inverse model of the transducer, designed on the architecture of MLFFBP-ANN trained with Levenbeg-Marquardt learning algorithm [23, 24]. By training the neural model suitably, the digital readout of the applied input (measurand) is estimated, which is independent of the disturbing variable. The performance of the proposed VI is summarized in Table-2 including the results of neural modeling [23, 24] of Strain-gauge based pressure transducers under the influence of a disturbing variable. From the results indicated in Table-2, it has been established that the proposed VI is very much effective for estimation of transducer non-linearity under the influence of a disturbing variable as well as its automatic compensation without the use of any extra hardware. This results in drastic reduction in the extra hardware required invariably for the purpose of linearizing the transducers. Moreover, same technique can be used effectively for other type of transducers as well. This adds the feature of flexibility as well as versatility of the proposed VI.

C. Transducer Fault Detection and Isolation In this section, the performance of the proposed VI is examined for fault detection and isolation [43] in Wheatstone bridge oriented transducers. The Virtual Fault Detector [25] is invoked with mouse-click of the corresponding Pushbutton (Fault Detector) provided on the Front-panel of the proposed VI. The faults frequently encountered in Wheatstone bridge oriented pressure transducers are summarized in Table-3 [25]. Nearly, 300 input-output patterns obtained from the Wheatsone Bride Connected Transducers are used to evaluate the performance of the proposed VI. The heart of the proposed VI was executed in the form of Signal Processing Software (SPS) having an efficient inbuilt logic executed on the basis of logic illustrated in Table-3. The SPS, in fact, acts as a residual generator in the proposed VI. The residues [43] so generated are evaluated automatically by another inbuilt module (Algorithm of Control & Computations) that is developed as an integrated component of the DAMS. The performance of the proposed VI is expressed in terms of the success rate in percentage as indicated in Table-4. Experimental results shown in Table-4 indicate that the executed VI is very efficient in auto-fault detection & isolation in such transducers.

Table-4 Virtual Fault Detector Success Rate in Percentage

Fault Detection

Fault Location

Missed Fault

Detection

Missed Fault

Location

False Alarms

Sensor F-1 F-2 F-3 F-4 F-5 F-6 F-7

100 100 100 100 100 100 100 100 0 0 0

Table-2 Neural Modeling of Strain-gauge based Pressure Transducer under the Influence of a disturbing variable

(SenSym-S×100DN) [23, 24] Data set Direct Modeling (2-4-2-1 ANN Structure) Inverse Modeling (2-4-3-1 ANN Structure)

Maximum absolute error Maximum Absolute error (% FS)

Maximum absolute error

Maximum Absolute error (% FS)

Training data 0.034 0.661 0.012 1.774 Validation with trained

disturbing variable 0.123 2.511 0.197 3.079

Validation with untrained disturbing variable

0.178 5.045 0.185 2.810

Table-3 Common faults in DAS-connected Wheatstone bridge oriented transducers [25]

Fault Description of the Identified Cause F-1 Disconnection or grounding of bridge positive excitation F-2 Disconnection of bridge earth terminal F-3 Disconnection or grounding of bridge positive sense terminal F-4 Disconnection or grounding of bridge negative sense terminal F-5 Short-circuiting of bridge positive sense terminal to its positive excitation terminal F-6 Short-circuiting of bridge negative sense terminal to its positive excitation terminal F-7 Short-circuiting of bridge positive sense terminal to its negative sense terminal



D. Limitations Careful choice of data acquisition system and the software used for the execution of SPS and DAMS modules is an extremely important consideration that is required to be taken into account while designing Virtual Instruments. Artificial Neural Networks are nonparametric models. These can be used to approximate any function with desired degree of accuracy provided sufficient number of artificial neurons exist for the same. The back-propagation learning algorithm is the most popular algorithm for optimization problems. However, its action depends largely on nonlinearity function associated with the connection weights. This usually results in longer learning periods. It may also lead to the possible convergence to local minima. However, in order to circumvent these difficulties, a number of variants of the basic steepest descent algorithms are reported. When making a choice among the variants of the back-propagation algorithm, care must be taken to consider the characteristics of the function to be optimized, amount of training data available and amount of time required to train the network to generate acceptable results. Excepting the theoretical developments, some loopholes develop in the execution of the proposed VI on account of inadequacies of the underlying theoretical principles. However, there, is no cut and paste solution to these problems, which results in somewhat lower performance of Artificial Neural networks as an efficient signal processing component in the field of Virtual Instrumentation. However, each and every care has been taken to explore appropriate ANN architecture, learning algorithms and the required training parameters from the experience of the author while working with the Artificial Neural Networks. Training of ANN for implementation of transducer models is required invariably, which is in fact some time consuming process and cannot be avoided. However, efforts have been made in the present work to explore the adequacy of using faster learning algorithms for training the ANN. It is also true that once the Artificial Neural Networks are trained appropriately, then these algorithms are extremely fast in production (operational) phase due to parallel processing capability.

V. CONCLUSIONS In this work, a novel virtual instrument was designed and implemented for different applications of transducer modeling. These applications include estimation of static non-linearity associated with transducers and its self-compensation, correction for reducing the effect of a disturbing variable on the response of transducers and auto-fault detection & isolation in transducer based systems. The proposed virtual instrument was developed on the architecture of five different standalone Virtual Instruments, each is dedicated to serve a particular application. These instruments were also validated experimentally for their performance. Ultimately, these were integrated to synthesize the proposed Virtual Instrument for versatile applications. Careful choice of Data Acquisition System, software used for the purpose of Data Acquisition & Management as well as execution of Signal Processing Software are important aspects that require special attention for the development of the proposed Virtual Instrument. However, for the purpose of experimental demonstrations, the proposed Virtual Instrument also serves as an excellent tool in the field of engineering education.

ACKNOWLEDGMENT The author is very much thankful to the Department of Electrical & Instrumentation Engineering at SLIET, Longowal for

extending excellent lab facilities for experimental validation of the present work. He also expresses his deepest gratitude to Prof. (Dr.) T. S. Kamal and Prof. (Dr.) Shakti Kumar for their untiring guidance, constructive criticism and valuable suggestions throughout the present work.

REFERENCES [1] R.G.Chougule, and B.Ravi, “Web based preliminary process planning of cast components,” Proc. of the International Conference on e-Manufacturing

held at Maulana Azad National Institute of Technology, Bhopal, November 17-19, 2002, Phoenix Publishing House Pvt. Ltd., New Delhi. [2] M.Dunbar, “Plug-and-Play Sensors in Wireless Networks”, IEEE Instrum. Measure. Mag., vol.4, pp.19-23, March2001 [3] D.Potter, “Smart Plug and Play Sensors,” IEEE Instrum. Measure. Mag., vol. pp.28-30, March, 2002 [4] P.Bilski and W.Winiecki, “Virtual spectrum analyzer based on data acquisition card”, IEEE Trans. Instrum. Meas., vol 51, no.1, pp.82-87, Feb., 2002. [5] L.Wells and J.Travis, LabView for everyone, Prentice-Hall Inc., pp.366-369, 1997. [6] R.H. Bishop, “LabView Student Edition, Prentice Hall, 2001. [7] A.R.Hambley, “Electrical Engineering: Principles and Applications, PHI, New Delhi, pp. 390-420, 2002. [8] O.Postolache, P. Girao, and M.Pereira, “Neural Networks in Automated Measurement Systems: State of the Art and New Research Trends”,

Proceedings of IEEE International Joint Conference on Neural Networks-IJCNN’01, vol.3, pp. 2310-2315, Washington, DC, USA, July, 15-19, 2001 [9] B.Widrow and M.A.Lehr, “30 Years of Adaptive Neural Networks: Perceptron, Madaline and Back-propagation,” Proc. IEEE, vol. 78, pp. 1415-1442,

Sept. 1990. [10] S.A. Khan, A.K. Agarwala, D.T. Shahani, “Artificial Neural Network (ANN) based Nonlinearity Estimation of Thermistor Temperature Sensors”, in

Proc. 24th National systems Conference”, Banglore, India, pp.296-302, Dec. 2000.



[11] J.C.Patra and A.V. Bos, “Modeling and development of an ANN-based smart pressure sensor in a dynamic environment”, Measurement, viol.26, pp.249-262, 1999

[12] S.Haykin, “Neural networks: A comprehensive foundation”, pp.118-120, Pearson Education Asia, 2001. [13] J.C.Patra , G.Panda, and R.Baliarsingh, “Artificial Neural Network-based Nonlinearity Estimation of Pressure sensors” IEEE Trans. Instrum. Meas,,

vol. 43, No.6, pp. 874-881, Dec.1994 [14] D.Patranabis , “Sensors and Transducers”, Wheeler Publishing Co., Delhi, pp. 249-254, 1997. [15] J.M.D. Pereira, O.Postolache, M.Cretu, “Minimizing Temperature drift Errors of conditioning circuits using artificial neural networks”, IEEE Trans.

Instr. Meas., vol.49, no.5,pp.1122-1127, Oct.2000. [16] M.Attari, F.Boudjema and M.Heniche , “Linearizing a Thermistor Characteristics in the Range of Zero to 100 Degree C With Two Layers Artificial

Neural Networks”, IEEE Inst. Meas. Tech. Conference, IMTC’95, Waltham (USA), pp. 119-122, April1995. [17] M.Attari, F.Boudjema and M.Heniche, “An artificial neural network to linearize a G (Tungsten vs. Tungsten 26% Rhenium) thermocouple

characteristic in the range of zero to 2000°C, Proc. of International Symposium on Industrial Electronics (ISIE’95), vol.1, pp.176-180, 10-14 July, 1995.

[18] M. Attari, “Methods for linearization of Non-linear sensors”, Proc. CMMNI-4, Fourth Maghrebin Conference on Numerical Methods of Engineering, Algiers (Algeria), vol.1, pp.344-350, 1993.

[19] M. Yamada, T. Takebayayashi, S-I Notoyama, and K. Watanabe, “A switched capacitor interface for capacitive pressure sensors,” IEEE Trans. Instrum. Meas., vol. 41, pp. 81-86, Feb. 1992.

[20] M.M.Nayak , N.Gunasekaran, K. Rajanna, S.Srinivasulu , and S.Mohan, “The Strain Gauge Pressure Transducers- An Overview”, IETE Technical Review, pp. 170-177, vol.9, no.2, 1992.

[21] Amar Partap Singh, Shakti Kumar and Tara Singh Kamal, “Fitting transducer characteristics to measured data using a virtual curve tracer,” Sensors & Actuators: A Physical, vol.111/2-3, pp.145-153, 2004.

[22] Amar Partap Singh, T. S. Kamal and S. Kumar, “Virtual curve tracer for estimation of static response characteristics of transducers,” Measurement, vol. 38, pp. 166-175, 2005.

[23] Amar Partap Singh,T.S.Kamal and Shakti Kumar, “Development of a virtual linearizer for correcting transducer static non-linearity,” ISA Transactions, vol. 45, no. 3, pp. 319-328, 2006.

[24] A. P. Singh, Tara Singh Kamal and Shakti Kumar, “Development of a virtual curve tracer for estimation of transducer characteristics under the influence of a disturbing variable,” Sensors and Actuators: A Physical, vol.20/2, pp.518-526, 2005.

[25] Amar Partap Singh, Shakti Kumar and Tara Singh Kamal, “Virtual compensator for correcting the disturbing variable effect in transducers,” Sensors and Actuators: A Physical, vol.116/1, pp.1-9, 2004.

[26] Amar Partap Singh, Tara Singh Kamal and Shakti Kumar, “Development of ANN based Virtual Fault Detector for Wheatstone Bridge-Oriented Transducers,” IEEE Sensors Journal, vol.5, no.5, pp.1043-1049, 2005.

[27] K. Saha, “Ph.D. Thesis: Studies in analog VLSI implementation of Neural Networks,” IIT Delhi, 1996. [28] B.Yegnanarayana, “Artificial Neural Networks,” PHI, 2001 [29] DARPA, Neural Network Study, Lexington, MA: MIT Lin-coln Laboratory, 1988. [30] L. F. Pau and F. S. Johansen, “Neural Network Signal Understanding for Instrumentation,” IEEE Trans. Instrum. Meas., vol. 39, no.4, pp. 558-564,

August 1990. [31] S. Marco, A. Ortega, A.Pardo, J.Samitier, “Gas identification with Tin oxide sensor array and self organization maps: Adaptive correction of sensor

drifts”, IEEE Trans. Instr. Meas., vol.47, no.1,pp.316-320, Feb.1998. [32] S.W. Moore, J.W. Gadner, E.Hines., W. Gopel , U.Weimar, “A modified multiplayer perceptron model for gas mixture analysis”, Sensor and

Actuators: B, vol. 15-16, pp. 344-348, 1993. [33] K.Hornik, M. Stinchcombe and H.White, “Multilayer feed-forward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359-

366, 1989. [34] M.T.Hagan, M. Menhaj, “Training feed-forward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no.6, pp.

989-993, 1994. [35] M.T.Hagan , H.B.Demuth, and M.Beale, “Neural Network Design,” Thomson Asia Pte. Ltd., Singapore, 2002 [36] R.Beale, and T. Jackson, “Neural Computing: An Introduction, 2nd Edition, Adam Hilger, 1991. [37] J.M.D.Pereira, O. Postolache, P.S.Girao, “A temperature compensated system for magnetic field measurement based on artificial neural networks”,

IEEE Trans. Instr. Meas., vol.47, no.2, pp. 494-498, April 1998. [38] Capital Equipment’s Test Point Software-User’s Manual for Version 3.3, Capital Equipment Corporation, Billerica, 1997. [39] H.Demuth. and M.Baele, Neural network toolbox for use with MATLAB-User’s Guide. Natick, M.A., The MathsWorks Inc., 1993. [40] MATLAB Application Program Interface Guide User Manual (Version 5), pp.7.32-7.42, 1998. [41] A. K. Narula and A. P. Singh, “Fault Diagonalog Circuit using Artificial Neural Networks,” International Journal of Intelligent Systems and

Applications (IJISA), vol.7, pp.11-17, 2015. [42] A. Singh and A. P. Singh, “Virtual Grader for Assessing Apple Quality using Shape features, Wulfenia Journal, Vol 21, No.7, pp 232-241, 2014. [43] Ashwani Kumar, A.P.Singh, “Fuzzy classifier for fault diagnosis in analog electronic circuits,” ISA Transactions (Elsevier), pp.816-24, 2013

BIOGRAPHY Amar Partap Singh Pharwaha, FIE, FIETE, Life Member (IAE, ISI, MSI, ISTE & PAS), received B. Tech. (Electronics Engg.) from GNDU, Amritsar, M. Tech. from REC (now NIT), Kurukshetra & Ph.D. in 2005 in Intelligent Systems. Having more than 24 years of professional experience, at present, he is Professor at SLIET, Longowal, Punjab, India. Earlier, he has also served as Professor & Joint



Director at YCoE, Punjabi University Guru Kashi Campus, Talwandi Sabo, Punjab, India. He has published more than 170 Research Papers in National/International Journals/Conferences & received IETE Students Journal Award & IEI awards including Certificate of Merit, KF Antia Award twice & Sir Thomas Ward Award. Dr. Singh has supervised six Ph.D. thesis and 07 more are under his guidance. More than 15 students were guided for Master Degree. He was elected to State Centre Committee of IEI for the third consecutive term. He has visited US, UK, France, Australia, Singapore & Pakistan.


Documents

Kuala Lumpur, Malaysia, March 8-10, 2016 Virtual ...ieomsociety.org/ieom_2016/pdfs/568.pdf · Keywords— virtual, instrument, pressure, artificial, intelligence. I. INTRODUCTION