Machine Learning Detection Algorithm for Large Barkhausen Jumps in Cluttered Environment

TL;DR

This paper introduces machine learning algorithms, including support vector machines and neural networks, to detect large Barkhausen jumps in magnetic sensor data amidst noise and clutter, improving detection accuracy over traditional methods.

Contribution

The work develops and compares two novel machine learning algorithms for detecting Barkhausen jumps in noisy magnetic field data, enhancing robustness and generalization.

Findings

Machine learning algorithms outperform classical methods in detection accuracy.

Neural network approach shows rapid convergence and high robustness.

Support vector machine provides competitive detection performance.

Abstract

Modern magnetic sensor arrays conventionally utilize state of the art low power magnetometers such as parallel and orthogonal fluxgates. Low power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened during production, the conventional noise measurement does not always catch the dc jump because of its sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps can be easily detected in a shielded environment, when deployed in presence of natural noise and clutter, it can be hard to positively detect them. This work fills this gap and presents algorithms that distinguish dc jumps embedded in natural magnetic field data. To improve robustness to noise, we developed two machine learning algorithms that employ temporal and statistical physical-based features of a pre-acquired and well-known experimental data set. The first algorithm employs a support vector machine classifier, while the second is based on a neural network architecture. We compare these new approaches to a more classical kernel-based method. To that purpose, the receiver operating characteristic curve is generated, which allows diagnosis ability of the different classifiers by comparing their performances across various operation points. The accuracy of the machine learning-based algorithms over the classic method is highly emphasized. In addition, high generalization and robustness of the neural network can be concluded, based on the rapid convergence of the corresponding receiver operating characteristic curves.