Sparse decomposition has been widely used in gear local fault diagnosis due to its outstanding performance in feature extraction. The extraction results depend heavily on the similarity between dictionary atoms and fault feature signal. However, the transient impact signal aroused by gear local defect is usually submerged in meshing harmonics and noise. It is still a challenging task to construct high-quality impact dictionary for complex actual signal. To handle this issue, a novel impact feature extraction method based on Empirical Mode Decomposition (EMD) and sparse decomposition is proposed in this paper. Firstly, EMD is employed to adaptively decompose the original signal into several Intrinsic Mode Functions (IMFs). The high-frequency resonance component is separated from meshing harmonics and part of the noise. Then, the IMF with the prominent impact features is selected as the Main Intrinsic Mode Function (MIMF) based on the kurtosis. Accordingly, the modal parameters required for impact dictionary are identified from the MIMF by correlation filtering. Finally, the transient impact component is extracted from the original signal by Match Pursuit (MP). The proposed method was adequately evaluated by a gear local fault simulation signal, and the single-stage gearbox and five-speed transmission experiments. The effectiveness and superiority of the proposed method is validated by comparison with other feature extraction techniques.

By virtue of their convenience, reasonable cost and high efficiency, Unmanned Aerial Vehicles (UAVs) have been widely applied in every aspect of life. However, complicated operating conditions are prone to causing mechanical failure in UAVs, especially the rotor fault. Therefore, a novel dual attention convolutional neural network based on multisensory frequency features is proposed for UAV rotor fault diagnosis in this study. Firstly, according to the collected multisensory acceleration vibration signals of UAV rotors, time and frequency features in different health states (normal, rotor broken and crack fault) are compared and analyzed in detail. Secondly, a novel dual attention mechanism is proposed to not only focus on the effect of different sensors but also different frequency features of UAV. Moreover, it could adaptively assign larger weight to more important features to improve the fault diagnosis accuracy. Finally, a one-dimension convolutional neural network is adopted to extract the feature of signals and implement rotor fault diagnosis of UAV. The results derived from experimental signals demonstrate the superiority of the proposed method by comparison study. Additionally, it is found that the fault diagnosis accuracy of frequency features as input is much higher than that of time features and single frequency features as input.

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.

Constrained layer damping (CLD) structures, which are one of the composite structures with a softer viscoelastic material (VEM) layer sandwiched between a elastic base layer and a relatively stiffer constraining layer, are widely used in engineering applications for reducing vibration and noise radiation. To accurately predict and effectively control vibration and properly and quickly determine the design parameters, optimal designs for the CLD structures are necessary. The optimal designs depend on thoroughly understanding the damping characteristics of the CLD structures. In addition, for some cases, CLD structures with multi‐constrained VEM layers are needed to suppress vibration more effectively. In this paper, an effective modeling method to accurately describe the damping properties and a quick optimization design method using COMSOL were proposed for CLD structures with multilayers in detail. The effects of nondimensional thickness ratios of the VEM and constraining layer to the base layer on the damping properties of CLD structures were analyzed. For CLD structures with different configurations, different constraints were selected to obtain the maximized damping in the optimization design. The conclusions from this research provide an insight into the effects of thicknesses of VEM and constraining layers on the damping properties of CLD structures regardless of its size. The modeling and optimal methods using COMSOL in this paper are not limited to CLD structures and can be used by other structures also.

In-situ synthesis, microstructure, and mechanical properties of four TiB₂-Reinforced Fe-Cr-Mn-Al Steel Matrix Composites have been researched in this work. The microstructure and phases of the prepared specimens have been characterized by using scanning electron microscopy (SEM), X-ray diffraction technique, and transmission electron microscopy (TEM). The sintered specimens consisted of Fe₂AlCr, CrFeB-type boride, and TiB₂. The mechanical properties, such as hardness and compression strength at room temperature (RT) and at elevated temperatures (600 °C and 800 °C) have been evaluated. The compressive strength and Vickers hardness of the sintered specimens increase with the volume fraction of TiB₂ in the matrix, which are all much higher than those of the ex-situ TiB₂/Fe-15Cr-20Mn-8Al composites and the reported TiB₂/Fe-Cr composites with the same volume fraction of TiB₂. The highest Vickers hardness and compressive strength at room temperature are 1213 ± 35 HV and 3500 ± 20 MPa, respectively. As the testing temperature increases to 600 °C, or even 800 °C, these composites still show relatively high compressive strength. Precipitation strengthening of CrFeB and in-situ synthesis of TiB₂ as well as nanocrystalline microstructure produced by the combination of mechanical alloying (MA) and spark plasma sintering (SPS) can account for the high Vickers hardness and compressive strength.

The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.