Today, the sensing devices play an important role for various system automation and monitoring of different physical and chemical parameters. Nonlinearity is an important long-time issue for most of the sensors, so to compensate nonlinearity, various linearization schemes are reported in the literature. The accuracy of linearization schemes depends on the type and the nonlinearity value of the sensor output. Since it is difficult to find an exact polynomial equation or other functions to represent the response curve; it gives more error when the measurement parameter is determined from the inverse approximation functions. As many sensors are used for different applications, the linearized characteristics will simplify the design, calibration, and accuracy of the measurement. This paper presents a review of different methods applied to linearize sensor characteristics reported in the literature. Due to availability of high-performance analog devices, analog methods are still popular among many researchers. However, due to the advancement of IC technologies, hardware implementation of the software methods can be done easily with reduced time, cost, and more accuracy, so the digital methods combined with software techniques perform the job with better flexibility and efficiency.

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Linearization of the sensors characteristics: a review

This paper proposes a nonlinear optimal-function-based algorithm which can be utilized to replace electronic circuitry traditionally employed to linearize the characteristics of commonly used temperature transducers such as resistance temperature detectors, thermistors and thermocouples. The function exploits ratiometric-logarithmic operation for linearization. The optimal parameters of the function are determined using a covariance matrix adopted evolutionary strategy (CMAES) algorithm. Transducers' input–output data are derived from the Yokogawa handy calibrator model CA 150 and subjected to the proposed algorithm to evaluate the performance of the method. The performance measures such as full-scale error and mean square error are considered to compare the performance of the proposed technique with other methods reported for transducers. The present linearization algorithm was implemented using LabVIEW 7.1 Professional Development System in a personal computer that provides the facility to interface with the National Instruments data acquisition module NI DAQCard PCI-6221. Experimental results reveal that the proposed evolutionary optimized nonlinear function based software linearizer does its job efficiently in a better way than that of the conventional hardware and software methods. Also, the results obtained using the CMAES algorithm are compared with the results of a real-coded genetic algorithm. The comparison shows that the CMAES algorithm is more consistent in determining the best solution for the proposed ratiometric-logarithmic function with reasonable computation time.

https://iopscience.iop.org/article/10.1088/0957-0233/19/4/045103/pdf

An evolutionary optimized nonlinear function to improve the linearity of transducer characteristics

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.

An Included Angle-Based Multilinear Model Technique for Thermocouple Linearization

In this paper we present a high-performance, simple and low-cost method for simultaneous measurement of pressure and temperature using a single piezoresistive MEMS pressure sensor. The proposed measurement method utilizes the parasitic temperature sensitivity of the sensing element for both pressure measurement correction and temperature measurement. A parametric mathematical model of the sensor was established and its parameters were calculated using the obtained characterization data. Based on the model, a real-time sensor correction for both pressure and temperature measurements was implemented in a target measurement system. The proposed method was verified experimentally on a group of typical industrial-grade piezoresistive sensors. The obtained results indicate that the method enables the pressure measurement performance to exceed that of typical digital industrial pressure transmitters, achieving at the same time the temperature measurement performance comparable to industrial-grade platinum resistance temperature sensors. The presented work is directly applicable in industrial instrumentation, where it can add temperature measurement capability to the existing pressure measurement instruments, requiring little or no additional hardware, and without adverse effects on pressure measurement performance.

A method enabling simultaneous pressure and temperature measurement using a single piezoresistive MEMS pressure sensor

A simple software method to derive linearised output for constant temperature anemometer (CTA) is presented. The method uses a nonlinear ratiometric-logarithmic function, which consists of two parameters whose optimal values are determined by minimising the objective function (mean square error) to improve the linearity of CTA signal. Covariance matrix adopted evolutionary strategy algorithm, which generates optimal values consistently, is employed to determine the optimal values of linearisation parameters. The proposed linearisation algorithm was implemented using LabVIEW 7.1 Professional Development System in a personal computer that provides the facility to interface with the National Instruments data acquisition module PCMCIA-NI DAQCard-6024E. Experimental studies have been carried out using practical air-flow velocity measurement data obtained form Dantec Dynamics practical guide. The performance measures such as full-scale error and root mean square error are considered to compare the performance of the proposed method with the methods reported for linearisation of transducers. Experimental results reveal that the proposed evolutionary optimised nonlinear function-based software lineariser works well for CTA, and it can be suitable for computer-based flow measurement/control systems.

A. Movchan

Papers

Polynomial and piece-wise linear approximation of smart transducer errors