Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification

K. V. Santhosh

doi:10.1007/978-981-13-9680-9_11

Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Automated calibration of temperature measurement technique is proposed in this work. The objective of the work is to design a technique which will be able to automatically calibrate the temperature sensor (thermocouple) output to obtain higher sensitivity and can also detect fault in sensor if any. The signal from the thermocouple is amplified using an instrumentation amplifier. The output of instrumentation amplifier is acquired on to the system using a general-purpose voltage data acquisition card (USB 6008). Based on user-defined range, the sensor output is calibrated to produce an output which has the highest sensitivity using neural network algorithms. Designed support vector algorithm is also trained to identify fault in sensor. The trained system is tested for evaluating its performance with both simulated and practical data. Results produced show successful achievement of set objective.

Original language	English
Title of host publication	Smart Computing Paradigms
Subtitle of host publication	New Progresses and Challenges - Proceedings of ICACNI 2018
Editors	Atilla Elçi, Pankaj Kumar Sa, Chirag N. Modi, Gustavo Olague, Manmath N. Sahoo, Sambit Bakshi
Publisher	Springer Paris
Pages	141-147
Number of pages	7
ISBN (Print)	9789811396793
DOIs	https://doi.org/10.1007/978-981-13-9680-9_11
Publication status	Published - 01-01-2020
Event	6th International Conference on Advanced Computing, Networking, and Informatics, ICACNI 2018 - Silchar, India Duration: 04-06-2018 → 06-06-2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	767
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	6th International Conference on Advanced Computing, Networking, and Informatics, ICACNI 2018
Country/Territory	India
City	Silchar
Period	04-06-18 → 06-06-18

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-981-13-9680-9_11

Cite this

Santhosh, K. V. (2020). Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification. In A. Elçi, P. K. Sa, C. N. Modi, G. Olague, M. N. Sahoo, & S. Bakshi (Eds.), Smart Computing Paradigms: New Progresses and Challenges - Proceedings of ICACNI 2018 (pp. 141-147). (Advances in Intelligent Systems and Computing; Vol. 767). Springer Paris. https://doi.org/10.1007/978-981-13-9680-9_11

Santhosh, K. V. / Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification. Smart Computing Paradigms: New Progresses and Challenges - Proceedings of ICACNI 2018. editor / Atilla Elçi ; Pankaj Kumar Sa ; Chirag N. Modi ; Gustavo Olague ; Manmath N. Sahoo ; Sambit Bakshi. Springer Paris, 2020. pp. 141-147 (Advances in Intelligent Systems and Computing).

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title = "Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification",

abstract = "Automated calibration of temperature measurement technique is proposed in this work. The objective of the work is to design a technique which will be able to automatically calibrate the temperature sensor (thermocouple) output to obtain higher sensitivity and can also detect fault in sensor if any. The signal from the thermocouple is amplified using an instrumentation amplifier. The output of instrumentation amplifier is acquired on to the system using a general-purpose voltage data acquisition card (USB 6008). Based on user-defined range, the sensor output is calibrated to produce an output which has the highest sensitivity using neural network algorithms. Designed support vector algorithm is also trained to identify fault in sensor. The trained system is tested for evaluating its performance with both simulated and practical data. Results produced show successful achievement of set objective.",

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Santhosh, KV 2020, Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification. in A Elçi, PK Sa, CN Modi, G Olague, MN Sahoo & S Bakshi (eds), Smart Computing Paradigms: New Progresses and Challenges - Proceedings of ICACNI 2018. Advances in Intelligent Systems and Computing, vol. 767, Springer Paris, pp. 141-147, 6th International Conference on Advanced Computing, Networking, and Informatics, ICACNI 2018, Silchar, India, 04-06-18. https://doi.org/10.1007/978-981-13-9680-9_11

Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification. / Santhosh, K. V.
Smart Computing Paradigms: New Progresses and Challenges - Proceedings of ICACNI 2018. ed. / Atilla Elçi; Pankaj Kumar Sa; Chirag N. Modi; Gustavo Olague; Manmath N. Sahoo; Sambit Bakshi. Springer Paris, 2020. p. 141-147 (Advances in Intelligent Systems and Computing; Vol. 767).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Automated calibration of temperature measurement technique is proposed in this work. The objective of the work is to design a technique which will be able to automatically calibrate the temperature sensor (thermocouple) output to obtain higher sensitivity and can also detect fault in sensor if any. The signal from the thermocouple is amplified using an instrumentation amplifier. The output of instrumentation amplifier is acquired on to the system using a general-purpose voltage data acquisition card (USB 6008). Based on user-defined range, the sensor output is calibrated to produce an output which has the highest sensitivity using neural network algorithms. Designed support vector algorithm is also trained to identify fault in sensor. The trained system is tested for evaluating its performance with both simulated and practical data. Results produced show successful achievement of set objective.

AB - Automated calibration of temperature measurement technique is proposed in this work. The objective of the work is to design a technique which will be able to automatically calibrate the temperature sensor (thermocouple) output to obtain higher sensitivity and can also detect fault in sensor if any. The signal from the thermocouple is amplified using an instrumentation amplifier. The output of instrumentation amplifier is acquired on to the system using a general-purpose voltage data acquisition card (USB 6008). Based on user-defined range, the sensor output is calibrated to produce an output which has the highest sensitivity using neural network algorithms. Designed support vector algorithm is also trained to identify fault in sensor. The trained system is tested for evaluating its performance with both simulated and practical data. Results produced show successful achievement of set objective.

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Santhosh KV. Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification. In Elçi A, Sa PK, Modi CN, Olague G, Sahoo MN, Bakshi S, editors, Smart Computing Paradigms: New Progresses and Challenges - Proceedings of ICACNI 2018. Springer Paris. 2020. p. 141-147. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-13-9680-9_11

Self-calibrating Thermocouple Using Neural Network Algorithm for Improved Sensitivity and Fault Identification

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