Design of a high precision temperature measurement system based on artificial neural network for different thermocouple types

Measurement ◽  
2006 ◽  
Vol 39 (8) ◽  
pp. 695-700 ◽  
Author(s):  
K. Danisman ◽  
I. Dalkiran ◽  
F.V. Celebi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Measurement ◽  
High Precision ◽  
Measurement System ◽  
Precision Temperature ◽  
Artificial Neural ◽  
Temperature Measurement System

The Design of High-Precision Temperature Measurement System Based on C8051F350 and PT100

2013 ◽  
Vol 423-426 ◽  
pp. 2559-2562 ◽  
Author(s):  
Long Liu ◽  
Yun Cui Zhang ◽  
Shen Hua ◽  
Jun Xiao ◽  
Li Jia Huang
Keyword(s):  
Temperature Measurement ◽  
High Precision ◽  
Measurement System ◽  
Current Source ◽  
Constant Current ◽  
Constant Current Source ◽  
Interface Circuit ◽  
Software Algorithms ◽  
Precision Temperature ◽  
Temperature Measurement System

This article describes the design of High-Precision Temperature Measurement System based on C8051F350 and PT100. The system consists of the control circuit, temperature sensors, signal amplification circuit, field bus interface circuit. The system adopts constant current source method and a differential measurement circuit. A 24-bit A/D built in the CPU is used to sample the data and Non-linear compensation by software algorithms for it. The design improves the accuracy of the temperature measurement system.

Design of High Precision Temperature Sensor Based on Platinum Resistance

2014 ◽  
Vol 539 ◽  
pp. 177-180 ◽  
Author(s):  
Yi Zhen Nie
Keyword(s):  
Temperature Measurement ◽  
High Precision ◽  
Measurement System ◽  
Temperature Sensor ◽  
Platinum Resistance ◽  
Control Center ◽  
Precision Temperature ◽  
Nonlinear Compensation ◽  
Temperature Measurement System ◽  
Hardware Circuit

for Pt100 platinum resistance temperature measurement system has low accuracy, duplicate hardware circuit design and other issues, the articles design a high-precision temperature measurement system with Pt100 platinum resistance. With Pt100 temperature sensor, article takes STM32F103 as the control center through filtering, amplification and other signal conditioning circuit and a method of combining software look-up table to compensate the nonlinear compensation, thus achieving high-precision temperature measurement. Research results show that the system has a measurement accuracy high stability, scalability, and other characteristics.

High-Precision Temperature Measurement System for Magnetic Resonance Imaging

2020 ◽  
Vol 63 (5) ◽  
pp. 401-406
Author(s):  
D. S. Semenov ◽  
V. A. Yatseev ◽  
E. S. Akhmad ◽  
Yu. A. Vasilev ◽  
K. A. Sergunova ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Temperature Measurement ◽  
High Precision ◽  
Measurement System ◽  
Resonance Imaging ◽  
Precision Temperature ◽  
Temperature Measurement System

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

2011 ◽  
Vol 18 (2) ◽  
pp. 261-274 ◽  
Author(s):  
Stanisław Chudzik ◽  
Waldemar Minkina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Conduction ◽  
Measurement System ◽  
Heat Insulation ◽  
Heat Conduction Problem ◽  
Thermal Parameters ◽  
Insulation Materials ◽  
Hot Probe ◽  
Artificial Neural

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation MaterialsThe article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

Sign in / Sign up

Export Citation Format

Share Document