Brushless DC motor (BLDCM) is widely used in aerospace equipment. But the neuter port in conventional BLDCM windings could lead to torque ripples and low reliability. Each phase winding of the open winding BLDC is connected to an H-bridge inverter, and its phase voltage and phase current could be controlled independently, which has a certain fault tolerance. Firstly, the circuit topology and conduction mode of the open-winding brushless DC motor are introduced, and a Matlab simulation model is established according to the mathematical equation of the motor. Then, the operation characteristics of the open winding brushless DC motor with power device failure are analyzed, and the characteristics of two fault-tolerant control methods, compensation and reconstruction, are discussed. Finally, the open winding brushless DC motor and controller are processed, and the experimental platform is built to verify the operation characteristics of the open winding brushless DC motor with normal operation and compensation operation.

The on-chip extraction of dynamic information from a scene can be addressed with either frame-based CMOS vision, also called smart image sensors, or with dynamic vision sensors, also known as event cameras. When implemented with a pinned photodiode (PPD) as 4-transistor active pixel sensors (4T-APS) the former brings about the benefit of low temporal noise and dark current but without high dynamic range (HDR). The latter comes with the benefits of HDR and fast event detection rate at a low power consumption. The drawback is the background activity noise, which leads to additional hardware or algorithms to keep it low. In essence, the taxonomy of dynamic information extraction with image sensors is that of global shutter solutions and event cameras, each of which with their pros and cons. This paper digs in such differences and similarities focused on mismatch and noise through a global shutter 4T-APS implementation with local HDR incorporated in 180 nm CMOS technology vs conventional logarithmic event sensors found in the literature.

Power electronic circuits play an extremely important role in industrial systems. Parametric failure of components in these circuits are extremely prone to occur, which can easily evolve into catastrophic failures with time. Hence fault diagnosis of circuits can avoid catastrophic failures. In this paper, we propose a channel segmentation residual network (CSRN) for circuit fault diagnosis, in which Gram angular difference filed (GADF) is used to convert the original fault signal into a two-dimensional feature map for input into the CSRN. The channels of the input feature map are distinguished by selecting the main working channels through a channel segmentation mechanism and a Darwin selection mechanism for feature retention and deletion operations. The diagnostic performance of the proposed method is verified on the DC-DC circuit fault dataset, and the results show that the proposed CSRN achieves 97.92% fault classification accuracy for this circuit, which is 1.98%, 13.34% and 6.67% higher than other models, respectively.