Bearing incipient fault diagnosis is very crucial to the timely condition-based maintenance (CBM), but it is also relatively difficult because of the faint feature and environmental interference. In this article, an adaptive optimized time-varying filtering-based empirical mode decomposition (TVF-EMD) (AO-TVF-EMD) is put forward to extract more explicit and abundant incipient fault features by optimizing the parameter combination in conventional TVF-EMD. First, a novel integrated indicator termed sparsity-impact measure index (SIMI) constructed from dispersion entropy (DE) and envelope characteristic frequency ratio (ECFR) is designed as the parameter selection criterion. This index fully guarantees the optimal sparsity and impact properties of the obtained modes simultaneously, considering both time- and frequency-domain characteristics. Simulation verified that SIMI can not only sensitively characterize the variation of fault feature but also is robust to the environmental disturbance. Subsequently, we employ the Salp swarm algorithm (SSA) to realize the accurate search of parameter combinations where the minimum SIMI is defined as the fitness function. Moreover, comparative analysis in two runs to failure tests of bearings demonstrate that AO-TVF-EMD is more effective to extract incipient fault features than the maximum weighted Kurtosis (MWK) optimized TVF-EMD, the conventional TVF-EMD with fixed parameter, and some other advanced signal processing methods, which highlights the superiority of the proposed method.

A series arc fault can easily ignite surrounding flammable objects, leading to safety hazards. Therefore, accurately detecting the arc fault is essential. However, the fault characteristic information carried by the current when series arc faults occur is easily masked by the so-called screening load. As a result, series arc faults are not easily detected. To address this problem, this paper proposes an arc fault detection model based on L2/L1 norm and classification algorithm. L2/L1 norm is introduced to quantify the fluctuations in the current signal when an arc fault occurs, and then combined with some commonly used time domain indexes and frequency domain indexes to extract the features of the arc fault current. Next, the extracted features are filtered to create a high-quality data set. Subsequently, a random forest model is constructed and the data set is used to train and test the model. Finally, the parameters of the random forest model are optimized using the grid search method to obtain a highly accurate arc fault detection model. The effectiveness of the introduced feature is verified using the arc fault data under different loads. Meanwhile, the proposed method is tested and compared with seven commonly used machine learning methods, which reflects the superiority and accuracy of the proposed method.

Dependent degradation processes of performance characteristics are ubiquitous in engineered systems. The initial state of each degradation process is usually random and relates to the degradation rate. Unfort unately, most existing Wiener-based bivariate degradation models fail to consider these two features, which may limit the accuracy of system reliability assessment. In order to surmount this limitation, in this article, we develop a new bivariate degradation model, which involves a generalized Wiener process-based marginal degradation model that considers the random initial state and the correlation between the initial state and the degradation rate and a corresponding degradation increment-based dependence structure (DS). The proposed bivariate degradation model and its reliability function are derived first. Then, a two-stage statistical inference is introduced using the maximum likelihood estimation method. Furthermore, a simulation study investigates the performance of the statistical inference and the misspecification effects of DSs and marginal degradation models. Finally, an illustrative example demonstrates the effectiveness of the proposed model.

Contact bounce is a common phenomenon in the closing process of the electromagnetic relay under capacitive loads. The investigations on the contact characteristics are mainly conducted by experimental methods. However, the experimental method is extremely complicated and time-consuming in structural design, and it cannot clearly reveal the mechanism of contact bounce. Therefore, a new theoretical model is proposed to investigate the dynamic characteristics of the electromagnetic relay under capacitive loads. The model allows for simulating dynamic process and collecting bounce parameters in a simple and effective way compared with conventional simulation and experimental methods. The physical mechanism of contact bounce for the electromagnetic relay is presented. The nonlinear contact force between the fixed and moving contacts is evaluated according to the Kelvin-Voigt viscoelastic contact model. The equations of motion considering the coupling of electromagnetic and mechanical fields are established. The theoretical results obtained in this study are validated by comparison with experimental data. The influences of the structural parameters, coil current, surge current and contact resistance on the contact bounce are analyzed. The numerical results well demonstrate that the contact bounce can be effectively weakened by adjusting the parameters of contact spring and the mass of the moving contact.

Conventional electromagnetic acoustic transducers (EMATs) are generally only used to generate and detect guided waves with a single wavelength, which increases their sensitivity at that particular wavelength but limits their application scenarios and the accuracy of defect assessment. This article proposes a design method for multiwavelength EMATs based on spatial-domain harmonic control. First, the EMAT model is analyzed, where it is then outlined that the eddy-current density distribution of the specimen is equivalent to the spatial low-pass filtering of the coil-current density distribution. This shows that the multiwavelength guided waves can be achieved as long as the spatial distribution of the coil-current density contains those multiple harmonics that are desired. It is then proposed that the structure of the EMAT coil is equivalent to the spatial sampled pulse sequences of a spatial signal. The coil parameter design based on pulse modulation technology is proposed. Taking a dual-wavelength EMAT design for Lamb waves as an example, details of the coil parameter design are presented. The simulation and experiment with the dual-wavelength EMAT proved the correctness of the proposed method. Finally, an experiment with a three-wavelength EMAT demonstrated the feasibility of the proposed method in designing multiwavelength EMATs.

In this study, a new moving amplification matching algorithm was proposed, and then the temporal and spatial differences and correlation were analysed and evaluated by comparing the FengYun-4A Lightning Mapping Imager (FY-4A LMI) data and the China Meteorological Administration Lightning Detection Network Advanced TOA and Direction (CMA-LDN ADTD) system data of southwest China in July 2018. The results are as follows. Firstly, the new moving amplification matching algorithm could effectively reduce the number of invalid operations and save the operation time in comparison to the conventional ergodic algorithms. Secondly, LMI has less detection efficiency during the daytime, using ADTD as a reference. The lightning number detected by ADTD increased from 5:00 AM UTC (13:00 PM BJT, Beijing Time) and almost lasted for a whole day. Thirdly, the trends of lightning data change of LMI and ADTD were the same as the whole. The average daily lightning matching rate of the LMI in July was 63.23%. The average hourly lightning matching rate of the LMI in July was 75.08%. Lastly, the mean value of the spherical surface distance in the matched array was 35.49 km, and roughly 80% of the matched distance was within 57 km, indicating that the spatial threshold limit was relatively stable. The correlation between LMI lightning radiation intensity and ADTD lighting current intensity was low.