Measurement of current distribution using infrared thermography

Abstract

Current distribution measurement methods are widely used in medical examinations, predicting faults in semiconductor devices and assessing structural integrity. Several methods for measuring current distribution are available, such as electrode arrays, coils, and magnetic sensors. However, these measurement methods are unable to obtain images of current distribution with high spatial resolution. Therefore, there is a need to develop a non-contact method to measure current distribution that is capable of capturing images with high spatial resolution. In this study, a non-contact current distribution measurement method based on infrared thermography is proposed. The method uses thermal variations to quantify the current amplitude and reconstructs the current direction based on the passivity of the electric field. For quantification of low frequency current amplitude, the experimental results show that the method can provide accurate current measurement results, for example, at the power frequency (50 Hz), in the range of 1.05–3.45 A, its relative error can be improved to ±3.66% when the calibration fitting method is used. For the high-frequency current, an effective estimate of the current amplitude is obtained using the first-order derivative of temperature variation. When applied to the eddy current detection (256 KHz), it achieves a high-resolution image of the current distribution, and the effectiveness of the method is verified through simulation experiments. The experimental results show that the proposed method not only measures the current amplitude accurately but also improves the spatial resolution in acquiring two-dimensional current distribution images.

Theoretical and Numerical Study of Eddy Current Pulsed Thermography to Detect Damage of Deep-Sea Manned Pressure Hull

“…In addition, Ren et al [23] used thermal variations to quantify the current amplitude and reconstruct the current direction based on the passivity of the electric field. Xie et al [24] developed a local region-based strategy to identify these crack regions from the ECPT inspection data using a supervised classification procedure. Zhang et al [25] investigated thermography data compression using several unsupervised learning (UL) algorithms to compress different data types.…”