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Bidirectional surface wave splitters excited by a cylindrical wire in the microwave frequency have been proposed and fabricated. Compared to the bidirectional subwavelength-slit splitter, the novelty of the proposed structure is the coupling mechanism from the cylindrical wire to the surface gratings. By designing the grating structures with different depths and the feeding wire, electromagnetic waves at the designed frequencies will be confined and guided in the predetermined opposite directions. The finite integral time-domain method is used to model the splitters. Experimental results are presented in the microwave frequencies to verify the new structure, which have very good agreements to the simulated results. Based on the same coupling mechanism, a bidirectional surface wave splitter excited by a cylindrical wire in the terahertz (THz) frequencies is further been proposed and modeled. The simulation results demonstrate the validity of the THz splitter.

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

This paper describes an analytical model for analyzing magnetic forces developed in permanent magnet motors of radial field topology. The slotting effect is taken into consideration by solving the boundary value problem of the air gap field. The solution is expressed in the form of Fourier series with which the analysis of the harmonic contents in the magnetic forces can be readily performed. In particular, the curvature effect or the flux focusing effect in the slot opening regions is taken into consideration. The results of magnetic forces obtained from this model are verified with numerical simulations using the finite element method and experimental results.

In a photovoltaic (PV) system, the serial arc is mainly due to the discontinuity in the current‐carrying conductor. Different from the AC arc, the DC arc does not have a periodic zero‐crossing and more easily blossoms into sustained arc, which is more likely to cause accidents. When a serial arc occurs in the DC system, it would lead to a steep drop in current or some unpredictable, irregular change of the current wave. The occurrence of the serial arc can be detected by analyzing the change of amplitude at different frequencies. An arc‐fault detection method based on wavelet packet (WP) and support vector machine (SVM) analysis is proposed in this paper. The threshold to distinguish arcing from normal operation is determined by analyzing the characteristics of the WP coefficients of the DC arc current collected from a real system. Compared to that of the fast Fourier transform (FFT), the effectiveness of the proposed algorithms has been validated with experiments in a 5‐kW grid‐connected PV inverter. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.

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