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Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.

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Owing to the superior properties of silicon carbide (SiC), such as higher breakdown voltage, higher thermal conductivity, higher operating frequency, higher operating temperature, and higher saturation drift velocity, SiC has attracted much attention from researchers and the industry for decades. With the advances in material science and processing technology, many power applications such as new smart energy vehicles, power converters, inverters, and power supplies are being realized using SiC power devices. In particular, SiC MOSFETs are generally chosen to be used as a power device due to their ability to achieve lower on-resistance, reduced switching losses, and high switching speeds than the silicon counterpart and have been commercialized extensively in recent years. A general review of the critical processing steps for manufacturing SiC MOSFETs, types of SiC MOSFETs, and power applications based on SiC power devices are covered in this paper. Additionally, the reliability issues of SiC power MOSFET are also briefly summarized.