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Article
Publication date: 27 September 2019

Huachen Zhu, Zhenghong Qian, Jiaofeng Zhang, Yucheng Sun, Ru Bai and Jianguo Zhu

It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement error in…

Abstract

Purpose

It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement error in applications where working temperature changes largely over seasons or times. This paper aims to investigate and compensate the temperature effect of the spin-valve sensor.

Design/methodology/approach

A spin-valve sensor is fabricated based on microelectronic process, and its temperature relevant properties are investigated, in which the transfer curves are acquired within a temperature range of −50°C to 125°C with a Helmholtz coil and temperature chamber.

Findings

It is found that the sensitivity of spin-valve sensor decreases with temperature linearly, where the temperature coefficient is calculated at −0.25 %/°C. The relationship between sensitivity of spin-valve sensor and temperature is well-modeled.

Originality/value

The temperature drift model of the spin-valve sensor’s sensitivity is highly correlated with tested results, which could be used to compensate the temperature influence on the sensor output. A self-compensation sensor system is proposed and built based on the expression modeled for the temperature dependence of the sensor, which exhibits a great improvement on temperature stability.

Details

Sensor Review, vol. 39 no. 6
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 26 April 2023

Aiyu Dou, Ru Bai, Huachen Zhu and Zhenghong Qian

The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as…

Abstract

Purpose

The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as well as two-channel cross-correlation. The single-channel method is easy to implement and is widely used in the noise measurement on MR sensors, whereas the two-channel method can only eliminate part of the system noise. This study aims to address two key issues affecting measurement accuracy: calibration of the measurement system and the elimination of system noise.

Design/methodology/approach

The system is calibrated by using a low-noise metal film resistor in that the system noise is eliminated through power spectrum subtraction. Noise measurement and analysis are conducted for both thermal noise and detectivity of magnetic tunnel junction (MTJ) sensor.

Findings

The thermal noise measurement error is less than 2%. The detectivity of the MTJ sensor reaches 27 pT/Hz1/2 at 2 kHz.

Originality/value

This study provides a more practical solution for noise measurement and system calibration on MR sensors with a bias voltage and magnetic field.

Details

Sensor Review, vol. 43 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 10 August 2010

Zhenghong Peng and Bin Song

The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid…

Abstract

Purpose

The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back‐propagation (BP) network and fuzzy genetic algorithm‐artificial neural network (FGA‐ANN) is used to power transformer fault diagnosis based on extracted pattern samples.

Design/methodology/approach

The existing manners (e.g. international electro technical commission triple‐ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA‐ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis.

Findings

If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis.

Research limitations/implications

Accessibility and availability of data are the main limitations which model will be applied.

Practical implications

This paper provides useful advice for power transformer fault diagnosis method based on DGA data.

Originality/value

The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

Details

Kybernetes, vol. 39 no. 8
Type: Research Article
ISSN: 0368-492X

Keywords

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