Deep Neural Network-Based Linearization and Cold Junction Compensation of Thermocouple Ramya Anandanatarajan, Umapathy Mangalanathan, Uma Gandhi IEEE Transactions on Instrumentation and Measurement, 2023 In this proposed work, temperature sensors, namely, a thermocouple and thermistor were linearized using deep neural networks. The deep feedforward neural network (DFNN) technique was proposed to linearize the K-type thermocouple’s output, in the given temperature range −100 °C to 1372 °C, while nonlinearity was reduced from 2.03% to 0.002% full scale span (FSS). Deep layer recurrent neural network (DLR-NN) was used to reduce the nonlinearity of a negative temperature coefficient (NTC) thermistor from 84.63% to 0.13% FSS. The linearized thermistor was used for cold junction compensation (CJC) of the thermocouple. In both the thermocouple and thermistor, linearization was achieved in a single stage for a wide range digitally using deep neural networks alone. There were no analog pre-signal conditioning circuits, unlike the existing neural network-based linearization techniques in literature. A hardware setup of a stand-alone module for linearization was designed using the Raspberry pi microcontroller consisting of two soft modules, one for thermocouple linearization and the other for thermistor linearization. The proposed system was experimentally tested using a K-type thermocouple on a thermal calibrator in a 0 °C–300 °C range. The cold junction compensated output of the thermocouple had a maximum absolute error of 0.34 °C when ambient temperature varied from 0 °C to 40 °C. The results were satisfactory and better than the existing National Institute of Standards and Technology (NIST) standard. This linearization technique can be extended to other thermocouple types as well as other nonlinear sensors.
Enhanced microcontroller interface of resistive sensors through resistance-to-time converter Ramya Anandanatarajan, Umapathy Mangalanathan, Uma Gandhi IEEE Transactions on Instrumentation and Measurement, 2020 Systems using microcontrollers make the interface of a transducer with the digital world much easier. They build smart, lucid, compact, cheap, and less power consuming electronic interfaces. Limited acquisition time is critical for many industrial applications. Single-element resistive sensors are used on a large scale. An attempt has been made to reduce the acquisition time of the measuring system compared to the existing work which converts the change in sensor resistance to the change in time with three charge–discharge cycles. Efforts have been made in this paper to reduce this to two charge–discharge cycles. The proposed work also compensates the errors due to lead wire resistance, the change in lead wire resistance, port pin resistances, and varying ambient temperature with reduced acquisition time. The mean of absolute errors, standard deviation, integral squared error (ISE), nonlinearity, and hysteresis of the proposed method have been computed. Simulation and experimentation study of the proposed system provided encouraging results. The mean of absolute errors and standard deviation of the proposed method in the simulation were 0.07 and 0.08 $\\Omega $ , respectively. Furthermore, during experimentation, the values of the mean of absolute errors and standard deviation were 0.11 and 0.11 $\\Omega $ , respectively. ISE was found to be 0.09 and 0.19 in simulation and experimentation, respectively. The proposed two-cycle method has an acquisition time of only 60% of the three-cycle method with no reduction in performance.
RECENT SCHOLAR PUBLICATIONS
Deep Neural Network-Based Linearization and Cold Junction Compensation of Thermocouple R Anandanatarajan, U Mangalanathan, U Gandhi IEEE Transactions on Instrumentation and Measurement 72, 1-9 , 2023 2023 Citations: 23
Linearization of Temperature Sensors (K‑Type Thermocouple) Using Polynomial Non‑Linear Regression Technique and an IoT‑Based Data Logger Interface R Anandanatarajan, U Mangalanathan, U Gandhi Experimental Techniques , 2022 2022 Citations: 30
Performance enhancement and fault identification using Kalman filter in a resistive temperature sensor interface R Anandanatarajan, U Mangalanathan, U Gandhi Measurement 183, 109836 , 2021 2021 Citations: 5
Enhanced microcontroller interface of resistive sensors through resistance-to-time converter R Anandanatarajan, U Mangalanathan, U Gandhi IEEE Transactions on Instrumentation and Measurement 69 (6), 2698-2706 , 2019 2019 Citations: 50
MOST CITED SCHOLAR PUBLICATIONS
Enhanced microcontroller interface of resistive sensors through resistance-to-time converter R Anandanatarajan, U Mangalanathan, U Gandhi IEEE Transactions on Instrumentation and Measurement 69 (6), 2698-2706 , 2019 2019 Citations: 50
Linearization of Temperature Sensors (K‑Type Thermocouple) Using Polynomial Non‑Linear Regression Technique and an IoT‑Based Data Logger Interface R Anandanatarajan, U Mangalanathan, U Gandhi Experimental Techniques , 2022 2022 Citations: 30
Deep Neural Network-Based Linearization and Cold Junction Compensation of Thermocouple R Anandanatarajan, U Mangalanathan, U Gandhi IEEE Transactions on Instrumentation and Measurement 72, 1-9 , 2023 2023 Citations: 23
Performance enhancement and fault identification using Kalman filter in a resistive temperature sensor interface R Anandanatarajan, U Mangalanathan, U Gandhi Measurement 183, 109836 , 2021 2021 Citations: 5
