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The application of high-performance magnetic sensors and the parameter estimation of ferromagnetic target from magnetic anomaly data are two important parts in magnetic anomaly detection (MAD) technique. In this work, we proposed a detection approach using a self-designed high-sensitivity three-axis magnetoelectric (ME) magnetic sensor based on a hybrid algorithm of particle swarm optimization (PSO) and simulated annealed (SA) algorithm. The magnetic dipole model is utilized as a universal model in the parameter estimation. Combining the strong global exploration ability and fast convergence of PSO algorithm with the probabilistic acceptance criterion in SA algorithm, the hybridization entitled PSO-SA algorithm for magnetic anomaly detection is expounded in detail. The ME sensor was applied to construct a test platform with an equivalent magnetic noise (EMN) lower than 26.08pT $/ \surd $ Hz at 1Hz. The proposed method was evaluated by three controlled experiments performed on the test platform using the ME sensor. Based on the observed results for the designed experiments, a high fitting level has been demonstrated by the goodness indexes of the proposed algorithm as high as 0.9182, 0.9287 and 0.9320 for the three experimental data, respectively. Moreover, the characteristics of the estimated parameters are in good agreement with the experimental design, which proves the reliability of the proposed algorithm and reveals its universality in magnetic anomaly detection as well. [ABSTRACT FROM AUTHOR]

In this work, we establish a magnetoelectric antenna system. The receiver is an in-plane series sensor composed of Mn-PMNT crystal fibers and Metglas. It has excellent magnetic field detection ability, reaching 3.1 pT/Hz1/2 under 1 Hz. We fabricated an asymmetric ME antenna based on PZT-5 H and Metglas at the transmitting end. The transmitter has two resonance peaks at a very low frequency (VLF), and its radiation ability can reach 73 nT at 14 kHz and 6.3 nT at 5.63 kHz. Furthermore, a nonlinear magnetostrictive model is constructed, and the ME effects are discussed through finite element mode. This model indicates that the near-field radiation capability of the device is strongly influenced by the frequency and magnetic bias, with the maximum performance achieved when the ME composite operates at the resonant frequency and the optimal piezomagnetic coefficient. [Display omitted] • The nonlinear near-field acoustic excitation antenna model can predict the resonant frequency of ME composites. • The PZT/Metglas magnetoelectric antenna fabricated in this work has two resonant frequencies in the very low-frequency band. The transmission capacity reaches 73 nT at 14 kHz and 6.3 nT at 5.63 kHz. Under limited external environmental interference, the signal can be recognized at 12.4 m and 5.2 m, respectively. • The receiver is made of Mn-PMNT fiber single crystals and Metglas and has an equivalent noise magnetic field of 3.1 pT/Hz1/2 whose detection sensitivity is better than multi-push-pull (MPP) magnetoelectric sensor (5.3 pT/Hz1/2). [ABSTRACT FROM AUTHOR]

With the popularity of electric vehicles, the ever-increasing demand for high-capacity batteries highlights the need for monitoring the health status of batteries. In this article, we proposed a magnetic imaging technique (MIT) to investigate the health status of power batteries nondestructively. This technique is based on a magnetic sensor array, which consists of a 16-channel high-performance magnetoelectric sensor, and the noise equivalent magnetic induction (NEB) of each channel reaches 3–5 pT/Hz1/2@10 Hz. The distribution of the magnetic field is imaged by scanning the magnetic field variation of different positions on the surface. Therefore, the areas of magnetic anomalies are identified by distinguishing different magnetic field abnormal results. and it may be possible to classify the battery failure, so as to put forward suggestions on the use of the battery. This magnetic imaging method expands the application field of this high-performance magnetoelectric sensor and contributes to the battery's safety monitoring. Meanwhile, it may also act as an important role in other nondestructive testing fields. [ABSTRACT FROM AUTHOR]