One of the greatest challenges to power embedded devices using magnetically coupled resonant wireless power transfer (WPT) system is that the amount of power delivered to the load is very sensitive to load impedance variations. Previous adaptive impedance-matching (IM) technologies have drawbacks because adding IM networks, relay coils, or other compensating components in the receiver-side will significantly increase the receiver size. In this paper, a novel frequency-tracking and impedance-matching combined system is proposed to improve the robustness of wireless power transfer for embedded devices. The characteristics of the improved WPT system are investigated theoretically based on the two-port network model. Simulation and experimental studies are carried out to validate the proposed system. The results suggest that the frequency-tracking and impedance-matching combined WPT system can quickly find the best matching points and maintain high power transmission efficiency and output power when the load impedance changes.

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Monitoring the vibrations of high-speed rotating blades is significant to the security of turbomachineries. Blade tip timing (BTT) is considered as a promising technique for detecting blade vibrations without contact online. However, extracting blade vibration characteristics accurately from undersampled BTT signals measured at varying rotational speed (VRS) has become a big challenge. The existing two methods for this issue are restricted within the order bandwidth limitation and require prior information and precise sensor installation angles, which is often unpractical. To overcome these difficulties, a compressed sensing-based order analysis (CSOA) method was proposed. Its feasibility comes from the sparsity of BTT vibration signals in the order domain. The mathematical model for the proposed method was built, and the optimizing principles for sensor number and sensor arrangement were given. Simulated and experimental results verified the feasibility and advantages of the proposed method that it could extract order spectrum accurately from BTT vibration signals measured at VRS without the drawbacks in the existing two methods.

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