This study aims to develop an easy-impement method for bearing health condition monitoring and predicting. Unlike traditional diagnostic methods with vibration sensor signals, this study utilizes motor current signature analysis (CSA) as a condition monitoring method to avoid cost and installation issues. Furthermore, the proposed anomaly detection approach can diagnose with just healthy samples, addressing the issue of insufficient fault samples in fault model training and classifying. This method introduced the complex autoregressive (CAR) model to identify the inherent pattern of health current. It can separate the nonsteady part in motor current caused by irregular vibration of damaged bearing. In addition, to enhance the robustness and accuracy of prognosis, this study uses the projected recursive least squares (P-RLS) for real-time prediction. It is shown to have better performance compared with traditional Bayesian-based methods by limiting the boundary of estimation. To verify the effectiveness of the proposed method, two different data sets are used for evaluation.

This paper examines the management of motor torque in electric bikes (E-bikes) while accounting for various external resistances. In the case of assist electric bikes, the electromagnetic torque generated by the permanent magnet motor can be manipulated to lessen the amount of pedaling torque required from cyclists. However, the overall torque required during riding is influenced by external resistances such as the cyclist's weight, air resistance, friction from the road, and the incline of the road. It's important to regulate the motor torque based on these riding conditions. The primary motion characteristics of an electric bike are analyzed in this paper to determine motor assistance torque. To address the dynamic effects of both pedaling torque and wheel acceleration, four torque control methods are proposed. By aligning the motor torque in the opposite phase to the pedaling torque, fluctuations in acceleration can be minimized. These torque control methods are evaluated through simulations of electric bikes across various resistances. Additionally, an experimental setup is constructed to validate the findings from these cycling simulations.

This paper designs a CubeSat reaction wheel for high-precision angular momentum control. For aerospace applications, the reaction wheel (RW) must provide sufficient momentum range under a rigorous weight limitation. To minimize the motor weight, a permanent magnet (PM) motor is developed with several design considerations. A novel four phase spherical motor structure and the vector control algorithm is proposed in this paper. The novel spherical motor design aims to replace three single axis 1 DOF RW motors by one 3 DOF spherical RW motor on the satellite attitude determination and control system (ADCS). According to the simulation result, the vector control can provide continuous torque through proposed current regulation, and the rated momentum of 60 mNm-sec and rated torque 5 mNm can be achieved to satisfied the demand of 6~30U satellite.