Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

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.

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

“…These methods are supported by appropriate modifications, for example, bagging (which is short for Bootstrap AGGregatING), boosting or stacking. In the case of MBN, the machine learning algorithms have been used to assess surface quality [29], predict residual stress [20,45], detect Barkhausen large jumps [46] and predict hardness and case depth [45,47]. All these applications show the great importance of the applied ML approach on the performance of the MBN method, which confirms the need to consider the route in further applications.…”