An Included Angle-Based Multilinear Model Technique for Thermocouple Linearization
01 Jul 2020-IEEE Transactions on Instrumentation and Measurement (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 69, Iss: 7, pp 4412-4424
TL;DR: A novel and systematic data-driven approach using an included angle method to determine optimal LMs for thermocouple decomposition and results show that the proposed technique reveals satisfactory performance indices compared with other popular methods.
Abstract: The thermocouple is widely used in industries for precise temperature measurement. The primary constraint is its nonlinearity. Sensor linearization techniques based on multilinear model approach are popular due to the simplicity and transparency of local linear models (LMs). However, the decomposition of the nonlinear range into suitable LMs is essential for the correct representation of its nonlinear characteristic. This article presents a novel and systematic data-driven approach using an included angle method to determine optimal LMs. Later, the Takagi–Sugeno (T-S) fuzzy interpolation technique is effectively used to combine the LMs. The efficacy of the proposed method is demonstrated in a simulation environment using J-type thermocouple for a wide range of 0 °C–760 °C and real-time linearization of K-type thermocouple over a scale of 0 °C–320 °C using Arduino platform. The results show that the proposed technique reveals satisfactory performance indices compared with other popular methods.
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TL;DR: In this paper , a piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature is proposed to solve the problem of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.
Abstract: The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.
1 citations
TL;DR: A translinear method with recommended processes can correct the non-linearity of sensors in all aspects and is compared to a proposed field programmable gate array (FPGA)-based digital computation methodology.
Abstract: Linear response characteristics of sensors are affected by ageing and changes in material properties. However, existing methods for improving these characteristics are disadvantaged by recurrent fu...
Cites background or methods from "An Included Angle-Based Multilinear..."
...The software linearisation of the thermocouple is achieved by decomposing static characteristics such as static gain and slope angle into the optimal linear models (LM) using a data driven based included angle method proposed in Srinivasan & Sarawade (2019)....
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...The percentage linearity improvement reported in Srinivasan & Sarawade (2019) is � 0:12% and � 0:41% for J- and K-type thermocouples over a limited temperature range....
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01 Oct 2022
TL;DR: In this article , an algorithm model based on the combination of adaptive gene crossover and local search in the improved stochastic programming algorithm based on genetic algorithm was established to realize the accurate and reliable monitoring and control of high-temperature polymer bodies.
Abstract: In recent years, with the continuous development of industrial technology, the traditional thermocouple detection methods have been unable to meet the needs of thermoelectric EMF tracking, dynamic temperature control and other aspects. In order to realize the accurate and reliable monitoring and control of high-temperature polymer bodies, this paper establishes an algorithm model based on the combination of adaptive gene crossover and local search in the improved stochastic programming algorithm based on genetic algorithm. In this paper, the experimental method and comparison method are used to compare the error results before and after compensation, and analyze the performance of the thermocouple. The experimental results show that the thermal drift error of the spindle along the Y direction after compensation is reduced by 52.8%, indicating that the machining accuracy of the system has been improved.
TL;DR: In this paper , a deep feed-forward neural network (DFNN) was proposed to linearize the K-type thermocouple 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).
Abstract: 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.
References
More filters
01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Abstract: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented. The premise of an implication is the description of fuzzy subspace of inputs and its consequence is a linear input-output relation. The method of identification of a system using its input-output data is then shown. Two applications of the method to industrial processes are also discussed: a water cleaning process and a converter in a steel-making process.
18,803 citations
"An Included Angle-Based Multilinear..." refers methods in this paper
...Step 11: T-S fuzzy interpolation technique is used for the smooth transfer of models due to simplicity [21], [24]....
[...]
Book•
17 Aug 1993
TL;DR: Signal Conditioning for Resistive Sensors Reactance Variation and Electromagnetic Sensors and Signals for Self-Generating Sensors Signal conditioning for self-Generation Sensors Digital Sensors Telemetry and Data Acquisition.
Abstract: Resistive Sensors Signal Conditioning for Resistive Sensors Reactance Variation and Electromagnetic Sensors Signal Conditioning for Reactance Variation Sensors Generating Sensors Signal Conditioning for Self-Generating Sensors Digital Sensors Other Sensing Methods Telemetry and Data Acquisition General Bibliography Appendix Index.
432 citations
01 Mar 2001
TL;DR: A gentle and short introduction to uncertainty of measurement for beginners, including laboratories preparing for UKAS accreditation, illustrates how to estimate uncertainty in real measurement situations, showing a detailed uncertainty calculation step by step.
Abstract: A gentle and short introduction to uncertainty of measurement for beginners, including laboratories preparing for UKAS accreditation. The guide explains the concept and importance of measurement uncertainty, using examples from everyday life. It illustrates how to estimate uncertainty in real measurement situations, showing a detailed uncertainty calculation step by step.
265 citations
"An Included Angle-Based Multilinear..." refers background in this paper
...The uncertainty in the estimated output is represented as standard deviation [26]...
[...]
TL;DR: In this paper, the authors take up the challenge of making a sensor work in a measurement system by providing excitation, if required, and then performing the preliminary actions needed to obtain a signal that can be processed.
Abstract: The challenge we take up in this fourth installment in a series of tutorials in instrumentation and measurement is to consider how to make a sensor work in a measurement system. Signal conditioning broadly includes the steps needed to make the sensor an active part of a measurement system by providing excitation, if required, and then performing the preliminary actions needed to obtain a signal that can be processed. What's done to and with that signal is the subject of future parts of this tutorial series. Luckily, we don't have to wait that long to get results, because the output of the signal conditioning stage can be used for something as simple as driving a display subsystem so that we see results. Signal conditioning is a critical step in a measurement system but so is each element as emphasized by the serial model we have been using so far to depict the basic elements of an instrument. However, it is important to keep in mind that many overall performance limits of a measurement are strongly influenced by what happens in the signal conditioning stage. For example, linearity, accuracy, noise rejection, and long-term drift behaviors will be strongly affected by decisions made here.
132 citations
"An Included Angle-Based Multilinear..." refers background in this paper
...It is suitable for several applications, even in hostile industrial environments [1], [2]....
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