- Email: [email protected]
Signal conditioning of thermocouple using intelligent technique
Available online at www.sciencedirect.com
ScienceDirect www.materialstoday.com/proceedings Materials Today: Proceedings 4 (2017) 10627–10631
ICEMS 2016
Signal conditioning of thermocouple using intelligent technique Mohammad Zeeshan1, Kashif Javed , Bharat Bhushan Sharma, Shahzad Ahsan Deptt of Electrical Engg,Jamia Millia Islamia, Maulana Mohammad Ali Jauhar Marg, Jamia Nagar,Delhi-110025,India
Abstract This paper implies using a neural network based technique for dispensation of a thermocouple signal [1]. At the stage of linearization, the thermocouple cold junction compensation and sensor transfer curve are in the same stride. For sensing the reference junction temperature a thermistor has been used and whose temperature tables are taken into consideration [2][3]. A multilayered Artificial Neural Network (ANN) has been trained using Levendberg-Marquadt algorithm [4]. For initializing the biases and weight of the ANN, to minimize measurement errors, and linearize the thermocouple by simulation methods this algorithm is as well very constructive [4]. Data of thermocouple for different material types may approach from the standard tables and must be interpolated for any readings not directly contained in these tables. The fluctuation in the hotness of the reference junction of the thermocouple have an effect on the repeatability of the thermocouple hence the reaction of the thermocouple is studied at different ambient temperatures from 0-45°C and the error due to increase in this temperature is also minimized simultaneously. As training of data increases the network becomes more capable of reducing the error close to zero. So a new technique is utilized which trains the data more and more along with increasing temperature leading to less errors than conventional or previous instances of the same program[5].
© 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016). Keywords: Artificial Neural Networks; Sensors ;Thermocouple; Signal Conditioning ; Linearization ; Cold Junction Compensation
* Corresponding author: Tel.: +91-9015461114; . E-mail address: [email protected]
2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016).
10628
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
1. Introduction Industrial temperature measurement systems require that the temperature of the device must be accurate. This task is a little devious and require a great deal of precision work. If the industrial data is not measured correctly, then it may lead to faults and failures and loss of capital. Various instruments are designed to measure the temperature correctly and with ease. The thermocouple sensor is a reliable instrument used to measure temperature for industrial purposes. The thermocouple has a very good accuracy, has a precise temperature range which is good for industrial work, response time is quick, ruggedness, highly reliable, cheap, and very beneficial for application purposes in that the thermocouple adapts itself to changing environmental conditions. However there is one problem i.e the accuracy of the thermocouples which is a very crucial parameter and it is very difficult to raise the accuracy levels using ordinary techniques [2][3]. The problem with most of the sensors is that the sensor output may be or may not be related to the parameters they measure and such is also in the instance of thermocouples. The thermocouple output voltage is non-linearly related to the inputs they measure i.e in this case the temperature. The output of the sensor needs some tuning for proper measurement. One more issue with thermocouples which relate to the non-linearity is compensation of cold junction [3]. The reference junction of the thermocouple should be maintained at 0ºC by any known method i.e by electronic stabilization etc. Various types of hardware and software are already present which deals with compensation of cold junction but these methods are outdated and could only reduce errors to a certain extent. The beauty with the human mind is such that as we grow up, our brain become more and more adept. For instance, we don’t know how to ride a bike but when we practice on the bike daily then our mind becomes more trained, 10628and at one point of time we become an expert in driving the particular vehicle. Our responses become much smoother and we can handle much more pressure. Our brain is made up of a large neural network which interacts to solve a problem and it is self adaptable and learns more as we grow up. Same is the case with the neural networks developed using artificial techniques also called the Artificial neural networks (ANN). The ANN can be trained for a various set of predefined values and used to learn a pattern and using the pattern the ANN is used to reduce the errors to a great extent. 2. Analysis of NIST data-ITS-90 (temperature-emf plots for different types of thermocouples) The temperature-emf data for different material types of thermocouples are taken from the website of NIST [6] for evaluation. Temperature ranges and equation coeffiecients are used to deduce the tables. The emf equations are of the form E=co+c1t1+c2t2+c3t3+…+cntn where E is the emf in millivolts and t is the temperature which is in degree Celsius (ITS-90) and co,c1,c2..cn are the predefined coefficients as shown in Fig. 1 (a). 2.1
Simulation
The program running and examination of the design has been performed using neural network toolbox found in MATLAB software R2013 in an Intel Core 2 duo processor based PC. 2.2 Training of thermocouple data For this technique, the multilayered perceptron (MLP) neural network composition has been used for linearization based device. The suggested method involves an artificial neural network to calculate the thermocouple temperature which is the output of the neural network when the type of
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
10629
thermocouple and it's output voltage are used as input values. We are performing training of the neural network structure with help of Levendberg-Marquardt’s algorithm [5] for computing the temperature involved giving it with various combinations of input values and correlating values which are measured. Dissimilarities between the objective output and the true output of the neural network is processed from the learning program for conformation of weights by backpropagation algorithm [5]. The exploratory data obtained from thermocouple sheets are to be used in the inspection [6]. 2.3 Simulation parameters
We have initialized the network with 2 hidden units of 8 and 9 (2*8*9*1) layers meaning number of neurons is 8 & 9 resp. The input and target features are normalized between -1.0 and 1.0 before training procedure to enhance the efficiency of training. The input and output layers contain the linear function of activation and the layers which are hidden contain the hyperbolic sigmoid function of activation. The no. of epochs is allocated to 1000 for training procedure. Performance goal is set to 10-9. Ambient temperatures are taken at 0,15,30,45 degree Celsius and corresponding mean square errors are calculated for the temperature range.
2.4 Thermocouple data usage The data is divided into 3 parts: 70% data is used for training, 15% data is used for validation, Rest of the 15% data is used as test data. 2.5 New methodology introduced We will use a new methodology to train the network which is an iterative method to train the network. As the network training increases, the network is more and more trained to reduce the error to a minimum.Ambient temperature also increases in the meantime and the training balances the inaccuracy due to deviation in the room temperature.
Fig. 1 (a) Temperature vs Emf plots for different material types of thermocouples
10630
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
(b) - Mean square error vs Temperature plot of J type thermocouple at different ambient temperatures (in order of 10-4)* (*Colour code: Red- 0◦C, Blue- 15◦C , Green 30◦C, Black 45◦C)
3. Results Observations/Conclusions The mean square errors for all thermocouple material types are calculated. Secondly, average error for all types of TC are calculated at different ambient temperatures (considering only +ve errors for calculations).The purpose was to reduce error to considerably low values using ANN shown in Table 1. We got errors less than 0.1% for all thermocouples in comparison to conventional techniques in which error is higher than 1-2% as shown in Fig. 1 (b).
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
10631
Table 1: The average mean square errors for different ambient temperatures
S.No.No
1 2 3 4 5 6 7 8
Thermocouple Material Types Thermocouple Types
J (Fe-Constantan) K(ChromelAlumel) N(Nicrosil-Nisil)
R(Pt-Pt+Rh-13%) S(Pt-Pt+Rh-10%) E(Cr-Constantan) T(Cu-Constantan) B(Pt:30%RhPt:6%Rh)
Temperature ranges of operation °C (( (° C)
Average mean square errors (scale of 10-4) at ambient temperatures of 0° C
15° C
30° C
45° C
0-750
10
3.62
2.92
0.0498
0-1250
8.69
3.06
2.60
1.31
0-390 0-1480 0-1480 0-900 0-350
4.86 11.3 3.98 11.4 2.04
1.70 3.61 3.04 10.4 1.49
2.73 2.71 5.02 12.9 0.79
2.26 2.83 3.21 9.46 1.51
0-1400
20.5
6.75
5.76
8.32
References [1]. Z. Y. Song, C. Y. Lu IEEE Proc, Second International Conference on. Machine Learning and Cybernetics, Xian, , 2003, pp 1404-1407 [2]. N. J. Cotton, B. M. Wilamowski, 24th IEEE International Conference. on Advanced Information Networking. and Applications, ,2010, pp 1210- 1217 [3] N. J. Cotton, IEEE Transactions on Industrial electronics., vol. 58, no. 3, 2011, pp 733-740 [4] D. K. Yuh, D. Ian, C. F. Von, Measurement- Elsevier, 43(10), 2010 p. 695-699 [5]. F. J. Ame, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley., Massachusetts, 1992. [6] srdata.nist.gov
ScienceDirect www.materialstoday.com/proceedings Materials Today: Proceedings 4 (2017) 10627–10631
ICEMS 2016
Signal conditioning of thermocouple using intelligent technique Mohammad Zeeshan1, Kashif Javed , Bharat Bhushan Sharma, Shahzad Ahsan Deptt of Electrical Engg,Jamia Millia Islamia, Maulana Mohammad Ali Jauhar Marg, Jamia Nagar,Delhi-110025,India
Abstract This paper implies using a neural network based technique for dispensation of a thermocouple signal [1]. At the stage of linearization, the thermocouple cold junction compensation and sensor transfer curve are in the same stride. For sensing the reference junction temperature a thermistor has been used and whose temperature tables are taken into consideration [2][3]. A multilayered Artificial Neural Network (ANN) has been trained using Levendberg-Marquadt algorithm [4]. For initializing the biases and weight of the ANN, to minimize measurement errors, and linearize the thermocouple by simulation methods this algorithm is as well very constructive [4]. Data of thermocouple for different material types may approach from the standard tables and must be interpolated for any readings not directly contained in these tables. The fluctuation in the hotness of the reference junction of the thermocouple have an effect on the repeatability of the thermocouple hence the reaction of the thermocouple is studied at different ambient temperatures from 0-45°C and the error due to increase in this temperature is also minimized simultaneously. As training of data increases the network becomes more capable of reducing the error close to zero. So a new technique is utilized which trains the data more and more along with increasing temperature leading to less errors than conventional or previous instances of the same program[5].
© 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016). Keywords: Artificial Neural Networks; Sensors ;Thermocouple; Signal Conditioning ; Linearization ; Cold Junction Compensation
* Corresponding author: Tel.: +91-9015461114; . E-mail address: [email protected]
2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016).
10628
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
1. Introduction Industrial temperature measurement systems require that the temperature of the device must be accurate. This task is a little devious and require a great deal of precision work. If the industrial data is not measured correctly, then it may lead to faults and failures and loss of capital. Various instruments are designed to measure the temperature correctly and with ease. The thermocouple sensor is a reliable instrument used to measure temperature for industrial purposes. The thermocouple has a very good accuracy, has a precise temperature range which is good for industrial work, response time is quick, ruggedness, highly reliable, cheap, and very beneficial for application purposes in that the thermocouple adapts itself to changing environmental conditions. However there is one problem i.e the accuracy of the thermocouples which is a very crucial parameter and it is very difficult to raise the accuracy levels using ordinary techniques [2][3]. The problem with most of the sensors is that the sensor output may be or may not be related to the parameters they measure and such is also in the instance of thermocouples. The thermocouple output voltage is non-linearly related to the inputs they measure i.e in this case the temperature. The output of the sensor needs some tuning for proper measurement. One more issue with thermocouples which relate to the non-linearity is compensation of cold junction [3]. The reference junction of the thermocouple should be maintained at 0ºC by any known method i.e by electronic stabilization etc. Various types of hardware and software are already present which deals with compensation of cold junction but these methods are outdated and could only reduce errors to a certain extent. The beauty with the human mind is such that as we grow up, our brain become more and more adept. For instance, we don’t know how to ride a bike but when we practice on the bike daily then our mind becomes more trained, 10628and at one point of time we become an expert in driving the particular vehicle. Our responses become much smoother and we can handle much more pressure. Our brain is made up of a large neural network which interacts to solve a problem and it is self adaptable and learns more as we grow up. Same is the case with the neural networks developed using artificial techniques also called the Artificial neural networks (ANN). The ANN can be trained for a various set of predefined values and used to learn a pattern and using the pattern the ANN is used to reduce the errors to a great extent. 2. Analysis of NIST data-ITS-90 (temperature-emf plots for different types of thermocouples) The temperature-emf data for different material types of thermocouples are taken from the website of NIST [6] for evaluation. Temperature ranges and equation coeffiecients are used to deduce the tables. The emf equations are of the form E=co+c1t1+c2t2+c3t3+…+cntn where E is the emf in millivolts and t is the temperature which is in degree Celsius (ITS-90) and co,c1,c2..cn are the predefined coefficients as shown in Fig. 1 (a). 2.1
Simulation
The program running and examination of the design has been performed using neural network toolbox found in MATLAB software R2013 in an Intel Core 2 duo processor based PC. 2.2 Training of thermocouple data For this technique, the multilayered perceptron (MLP) neural network composition has been used for linearization based device. The suggested method involves an artificial neural network to calculate the thermocouple temperature which is the output of the neural network when the type of
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
10629
thermocouple and it's output voltage are used as input values. We are performing training of the neural network structure with help of Levendberg-Marquardt’s algorithm [5] for computing the temperature involved giving it with various combinations of input values and correlating values which are measured. Dissimilarities between the objective output and the true output of the neural network is processed from the learning program for conformation of weights by backpropagation algorithm [5]. The exploratory data obtained from thermocouple sheets are to be used in the inspection [6]. 2.3 Simulation parameters
We have initialized the network with 2 hidden units of 8 and 9 (2*8*9*1) layers meaning number of neurons is 8 & 9 resp. The input and target features are normalized between -1.0 and 1.0 before training procedure to enhance the efficiency of training. The input and output layers contain the linear function of activation and the layers which are hidden contain the hyperbolic sigmoid function of activation. The no. of epochs is allocated to 1000 for training procedure. Performance goal is set to 10-9. Ambient temperatures are taken at 0,15,30,45 degree Celsius and corresponding mean square errors are calculated for the temperature range.
2.4 Thermocouple data usage The data is divided into 3 parts: 70% data is used for training, 15% data is used for validation, Rest of the 15% data is used as test data. 2.5 New methodology introduced We will use a new methodology to train the network which is an iterative method to train the network. As the network training increases, the network is more and more trained to reduce the error to a minimum.Ambient temperature also increases in the meantime and the training balances the inaccuracy due to deviation in the room temperature.
Fig. 1 (a) Temperature vs Emf plots for different material types of thermocouples
10630
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
(b) - Mean square error vs Temperature plot of J type thermocouple at different ambient temperatures (in order of 10-4)* (*Colour code: Red- 0◦C, Blue- 15◦C , Green 30◦C, Black 45◦C)
3. Results Observations/Conclusions The mean square errors for all thermocouple material types are calculated. Secondly, average error for all types of TC are calculated at different ambient temperatures (considering only +ve errors for calculations).The purpose was to reduce error to considerably low values using ANN shown in Table 1. We got errors less than 0.1% for all thermocouples in comparison to conventional techniques in which error is higher than 1-2% as shown in Fig. 1 (b).
Zeeshan et al./ Materials Today: Proceedings 4 (2017) 10627–10631
10631
Table 1: The average mean square errors for different ambient temperatures
S.No.No
1 2 3 4 5 6 7 8
Thermocouple Material Types Thermocouple Types
J (Fe-Constantan) K(ChromelAlumel) N(Nicrosil-Nisil)
R(Pt-Pt+Rh-13%) S(Pt-Pt+Rh-10%) E(Cr-Constantan) T(Cu-Constantan) B(Pt:30%RhPt:6%Rh)
Temperature ranges of operation °C (( (° C)
Average mean square errors (scale of 10-4) at ambient temperatures of 0° C
15° C
30° C
45° C
0-750
10
3.62
2.92
0.0498
0-1250
8.69
3.06
2.60
1.31
0-390 0-1480 0-1480 0-900 0-350
4.86 11.3 3.98 11.4 2.04
1.70 3.61 3.04 10.4 1.49
2.73 2.71 5.02 12.9 0.79
2.26 2.83 3.21 9.46 1.51
0-1400
20.5
6.75
5.76
8.32
References [1]. Z. Y. Song, C. Y. Lu IEEE Proc, Second International Conference on. Machine Learning and Cybernetics, Xian, , 2003, pp 1404-1407 [2]. N. J. Cotton, B. M. Wilamowski, 24th IEEE International Conference. on Advanced Information Networking. and Applications, ,2010, pp 1210- 1217 [3] N. J. Cotton, IEEE Transactions on Industrial electronics., vol. 58, no. 3, 2011, pp 733-740 [4] D. K. Yuh, D. Ian, C. F. Von, Measurement- Elsevier, 43(10), 2010 p. 695-699 [5]. F. J. Ame, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley., Massachusetts, 1992. [6] srdata.nist.gov