In order to detect tiny cracks in bearing rings, a high-sensitivity magnetic flux leakage (MFL) method based on magnetic induction head is proposed and investigated in this paper. First, the MFL sensing method based on magnetic induction head is theoretically illustrated. A magnetic core with an open gap is applied to guide leaked magnetic flux into a detection magnetic circuit, where an induction coil is placed to detect the change of the magnetic flux. To obtain an optimal gap width for higher sensitivity, its influence on MFL sensitivity is analyzed by building magnetic circuits. Then, to realize automatic inspection for bearing rings, a U-shape magnetizer is proposed and 3-D numerical simulations are conducted to investigate the signal characteristics of the proposed method and the sensitivity affected by gap width. Furthermore, MFL experiments for bearing rings were performed and it was found that the proposed method has high sensitivity for tiny cracks and when the gap width is approximately equal to the width of the crack, the greatest sensitivity will be obtained. Finally, the automatic MFL testing system for bearing rings is designed and employed in the production line. In-line tests show that the novel MFL system can fulfill the inspection requirement for bearing rings.

The performance of the independent vector analysis (IVA) algorithm depends on the choice of the source prior to better model the speech signals as it employs a multivariate source prior to retain the dependency between frequency bins of each source. Identical source priors are frequently used for the IVA methods; however, different speech sources will generally have different statistical properties. In this work, instead of identical source priors, a novel Student’s t mixture model based source prior is introduced for the IVA algorithm that can adapt to the statistical properties of different speech sources and thereby enhance the separation performance of the IVA algorithm. The unknown parameters of the source prior and unmixing matrices are estimated together by deriving an efficient expectation maximization (EM) algorithm. Useful improvement in the separation performance in different realistic scenarios is confirmed by experimental studies on real datasets.

Conventional corrosion monitoring techniques often require costly instrumentation and direct access to structures, limiting their suitability for long-term monitoring. This study presents a machine learning feasibility study for early-stage corrosion detection using Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) measurements. Machine learning algorithms were applied to a previously published RFID corrosion dataset obtained from steel specimens exposed to marine atmospheric corrosion for 0, 1, 3, and 6 months. RFID-derived features, including Analogue Identifier (AID), forward power, frequency, phase, and backscattered power, were analysed using unsupervised and supervised learning methods. For corrosion-stage discrimination, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) achieved an Adjusted Rand Index (ARI) of 1.00, a Normalised Mutual Information (NMI) score of 1.00, and a Silhouette score of 0.790. For nominal corrosion-thickness state estimation, a Random Forest regressor achieved an $R^2$ of 1.00, RMSE of 1.34 µm, and MAE of 0.21 µm under 15-fold cross-validation. Additional Leave-One-Sample-Out (LOSO) validation using readcount-based measurement-event groupings yielded an RMSE of 0.12 µm and an $R^2$ of 1.00. These results reflect nominal corrosion-thickness state estimation from repeated measurements on a single physical specimen per corrosion stage. SHAP analysis identified forward power and AID as the dominant predictive features. The results demonstrate the potential of RFID-enabled machine learning for early-stage corrosion assessment and provide a foundation for future experimental validation.