Linear compressors exhibit high compression efficiency and low noise characteristics, showcasing broad application prospects in various fields such as aerospace, medicine, household appliances, and more. However, due to the complexity of their structures and operation, the issue of vibration isolation in linear compressors has long been a research challenge within the industry. Addressing this challenge, this paper provides an overview of vibration isolation optimization methods for linear compressors. It delves into the discussion of different vibration sources in linear compressors and their respective measurement techniques. By integrating both single degree of freedom (SDOF) and multiple degree of freedom (MDOF) vibration isolation models, this paper describes both active and passive vibration isolation methods tailored to linear compressors. Furthermore, a feasible optimization approach is proposed. Finally, the paper offers insights into the developmental potential and feasibility of vibration energy recovery strategies.

A planetary gear fault generates periodic speed fluctuations, which significantly influence its vibration signal. It is a necessity to explore the vibration modulation features of gear faults to provide an effective indicator for fault detection. Therefore, a superior rigid-flexible coupling dynamics model of a planetary gearbox involving the fault-caused speed fluctuation is developed, where the meshing stiffness under the impact of fault-caused speed fluctuation is innovatively deduced utilizing the potential energy method; then, the meshing stiffness is substituted into the rigid dynamics model to calculate the excitation forces. Transfer path functions from excitation locations to the sensor installed on the housing are obtained by considering the modal parameters of the flexible housing. Finally, the excitation forces are combined with their transfer path functions to calculate the vibration signal. The fault modulation features of the cracked sun gear deduced by the superior dynamics model emerge surrounding the meshing frequency and its harmonics, as well as the resonance ranges, which can be a reliable sign for identifying faults. The experiment conducted on a single-stage planetary gearbox confirms the validity and superiority of the proposed model, which holds significant value for guiding fault detection and prognosis in planetary gearboxes.

The vibration modulation of the localized faults of sun gears is complicated because of the structure and motion features of planetary gearboxes. It is challenging to completely determine the vibration modulation mechanism. To address this issue, the influences of fluctuations in speed on the factors that affect vibration like the transfer path function, time-varying projection function, and meshing force were studied. Improved vibration amplitude and frequency modulation models were then established by combining the factors affecting vibration considering the speed fluctuations caused by the localized faults of sun gears. Regarding the features of vibration modulation of localized faults of sun gears, we concluded that both fault feature frequency and carrier rotational frequency modulate the harmonics of the meshing frequency, which form two groups of modulation sidebands. In addition, the harmonics of the fault feature frequency over the whole frequency range are modulated by the rotational frequency of the carrier, which is more dominant in the resonance domain. The experimental results of localized faults of sun gears strongly agree with those deduced using the proposed models. This research contributes an effective frequency indicator that can be used to confirm the condition of planetary gearboxes.

Unmanned Aerial Vehicles (UAVs) often face infrequent fault occurrences and high manual annotation costs, resulting in a critical shortage of valid fault samples for diagnostic research. Traditional fault diagnosis methods struggle with small sample sizes. This paper proposes a novel deep metric learning method, the Multi-Scale Convolutional Siamese Network (MSCSN), to address the few-shot learning problem in UAV rotor fault diagnosis. First, discrete wavelet transform (DWT) is used to compress and normalize the vibration signals, enhancing the prominence of signal features. Then, based on the multi-scale convolutional neural network (MS-CNN) model, the network automatically extracts multi-level features from rotor fault vibration signals, improving its adaptability to complex data. Finally, the Siamese network structure, with shared parameters and identical architecture, processes sample pairs and incorporates a small number of support samples for few-shot learning. Experimental results show that the proposed model achieves a highest accuracy of 94.42 % in few-shot tasks. In cross-domain transfer learning tests, the model achieves an average accuracy of 90.28 %, demonstrating its superior generalization ability and robustness across different environments. We also validated the model's stability using the publicly available MVS-UAV-BF dataset.

An abundant gear fault modulation signal is closely related to the gear fault type, especially to the gear fault severity. However, the modulation signal simultaneously includes coupled frequency modulation and amplitude modulation, which hinders the precise modulation separation and modulation-based gear fault severity assessment. Therefore, a new modulation separation method is proposed, which incorporates a rotation speed extraction technique based on the extreme value search, frequency modulation mechanism and Fourier series fitting. The rotational speed induced by the gear fault is first calculated by the extreme value search, which is then combined with a frequency modulation mechanism to solve the frequency modulation signal with the Fourier series fitting. Based on the vibration modulation signal model and Fourier series fitting, amplitude modulation is finally obtained. Simulation verifies the superiority of the proposed method in aspects of effectiveness and anti-noise performance compared with other modulation separation methods. The maximum relative errors of frequency modulation and amplitude modulation parameters under a signal-to-noise ratio of 0 dB are 2.9125% and 4.1143%, respectively. Two modulation intensity indicators regarding fault-induced frequency modulation and amplitude modulation signals are presented to assess gear faults. Experiment results also demonstrate the effectiveness of the proposed method in the severity assessment of misalignment and tooth breakage. Therefore, the research provides a new technique for gear fault severity assessment based on the frequency modulation or amplitude modulation signal.