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During the working process of the turbine, some types of faults can cause changes in the vibration characteristics of the blades. The real-time vibration monitoring of the blades is of great significance to the stable operation and state-based maintenance. Torsional vibration is a kind of blade vibration modes and results in failures such as cracks easily. Thus, it is important to measure it due to the harmfulness of torsional vibration. Firstly, the principle of blade tip timing (BTT) is introduced, and the models of the blade are built to analyze the characteristics of torsional vibration. Then, the compressed sensing theory is introduced, and its related parameters are determined according to the measurement requirements. Next, based on the condition that the blade rigidity axis is not bent and bent, respectively, the layout method of sensors is proposed and the numerical simulation of the measurement process is performed. The results of the above two types of numerical simulation verify the proposed measurement method. Finally, by analyzing the influencing factors of measurement uncertainty, the optimization method of sensors’ layout is further proposed. This study can provide important theoretical guidance for the measurement of blade torsional vibration.

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.

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.

Cracks are common failures of aeroengine rotated blades. Online monitoring of rotated blades through their vibration to identify cracks early in various working conditions is significant for operational safety. Breathing crack is a practical form of early cracks and results in nonlinear vibration response. Tenon connection and shroud contact are common structures in aeroengine rotated blades, which can also lead blades to vibrate nonlinearly and seriously interfere online identification of early cracks. Thus, it is important to extract vibration features due to breathing crack considering these two structures. Firstly, a blade with tenon and shroud is simplified and a lumped parameter model of the bladed disk is built. Then, dry friction and coupling force on a blade are analyzed and dynamics equations of the lumped parameter model are established. Next, the stiffness of the blade trunk with a breathing crack is analyzed. Finally, the vibration response of blade trunks with the occurrence of breathing crack is analyzed in time and frequency domains by numerical simulation. Effective features due to breathing crack for online identification are extracted. 2x components of spectrums can be the criterion to judge whether breathing crack occurs. Besides, by comparing the changes in vibration amplitudes with 1x component peaks of spectrums, the cracked blade trunk can be distinguished. These findings can provide important theoretical guidance for online identification of early cracks in aeroengine rotated blades.

The multi-transmitter single-receiver wireless power transfer (MTSR-WPT) system has good tolerance for coil misalignment because the magnetic fields generated by multiple transmitters can be shaped to adapt to position changes in the receiver coil. In order to achieve magnetic field shaping of the MTSR-WPT system and increase power transfer efficiency, accurately estimating the position of the receiver coil is a key issue that needs to be addressed. In this article, a receiver position estimation method based on long short-term memory (LSTM) is proposed, which utilizes a data-driven approach to establish a neural network model. By learning the relationship between the measured time-series voltage data of the transmitter coils and the position of the receiver coil, the proposed model can achieve accurate position estimation of the receiver. Compared with previous works, the proposed method does not require communication between the transmitter and receiver, which is conducive to simplifying the system structure and reducing costs. In addition, the proposed LSTM-based method requires less derivation of complex formulas and the internal mechanism analysis of the system. Finally, a MTSR-WPT prototype is built to verify the proposed method. The experimental results show that the proposed LSTM-based method can achieve high-accuracy position estimation of the receiver. When the receiver moves within a range of 160 mm × 160 mm, the average error between the estimated receiver coil position using the proposed method and the actual receiver coil position is less than 2.40 mm.

Virtual training is increasingly used in equipment maintenance. However, conventional haptic devices often suffer from limited force accuracy and poor wearing comfort in limb-interaction scenarios. This study presents an electro-actuated wearable force-feedback device for forearm interaction based on a compact direct current (DC) servo motor and cam mechanism. A simplified mathematical model is established to relate cam rotation angle to output feedback force, enabling quantitative force control. To handle dynamic load variations, a phase-shift–torque dual closed-loop control strategy is implemented by coordinating position and current regulation. The system is validated through physical experiments and virtual maintenance training, demonstrating rapid collision response and stable performance. Experimental results show a maximum feedback force of 32.37 N and consistently low errors. Overall relative errors were maintained within approximately 4.6 % and there was minimal variance across repeated trials. These results indicate that the proposed device can reliably enhance realism and immersion in virtual training applications.