With the high G value of vibration, outstanding sieving behaviors of a flip-flow screen are more likely to appear, but structural damages are aggravated under the high amplitude vibration. Considering rubber springs role on the vibration energy dissipation, viscoelasticity of rubber springs (i.e., shear springs and vibration isolation springs), is noticed to deal with the above contradiction. However, when rubber springs experience large amplitude vibration, viscoelastic responses influence more on the amount of damping than in the linear (i.e., small amplitude) vibration regime, as the frequency dependence of rubber springs is noteworthy. For this purpose, the Generalized Maxwell model is used to depict the frequency dependence of rubber springs and the practical damping is converted to a series damping parameters of the system, numerical models of the flip-flow screen under startup and shutdown modes and steady working conditions are proposed and verified firstly. Secondly, in order to explore kinetics responses of the elastic screen panel under different rubber springs damping coefficients, nonlinear finite element model of which is established. Finally, effects of rubber springs nonlinear damping on screen frames vibrations and screen panels nonlinear responses are studied. Results indicate that hysteretic damping of rubber springs can effectively solve the conflict between vibration strength and structural reliability of the flip-flow screen, since the low sensitivities of maximum stresses and the high sensitivities of accelerations to rubber springs damping are found.

A newly proposed method, feature mode decomposition (FMD), can effectively enhance signal features while decomposing the signal. This feature is beneficial for analyzing weak vibration signals. However, the input parameters (the segment number K, the filter length L, and the mode number n,) significantly influence the decomposition performance and efficiency. Based on the analysis of filter properties and decomposition performance of the FMD method, a step-by-step parameter-adaptive FMD method is proposed. First, parameters K and L are optimized; second, the parameter n is determined. In addition, a comprehensive evaluation indicator, the ratio of sample entropy and ensemble kurtosis, is constructed considering both the periodic impact characteristics of fault signals and the noise intensity to create objective functions for each step. Compared with the methods of variational mode decomposition spectral kurtosis method and the wavelet packet decomposition, the proposed method exhibits better decomposition performance: the amplitude has increased by nearly ten times for the simulation data and six times for the actual engineering data, and three evaluation factors (the crest factor, the impulse factor, and the kurtosis) have higher value. Therefore, it can be concluded that the proposed method has better superiority in identifying weak periodic fault features.

Flexibility piezoelectric sensors play an extraordinary significant role in force detection in many fields, such as biomedical, mechanical engineering, environmental monitor & measurement, and others. However, the limitations of the performance of sensing materials and and sensor functionality restrict their applications. Herein, a high-sensitivity arrayed stretchable piezoelectric force sensor based on PVDF-TrFE/nano-Fe₃O₄ was proposed. The flexible sensor consists of a double-layer PDMS substrate with different ratios of main agent and curing agent, serpentine stretchable silver electrodes, and a nanofiber-sensitive layer. The composite nanofiber was fabricated by doping Fe₃O₄ nanoparticles (NPs) into polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) and utilizing high-voltage electrospinning technology. X-ray diffraction (XRD) and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy results indicate that the addition of Fe₃O₄ NPs promotes the formation of the β-phase in the PVDF-TrFE polymer. Comparative experiments demonstrate that when the Fe₃O₄ NPs content is 5 wt%, the sensor exhibits better frequency (higher than 16 V at 3 Hz) and faster response time (45 ms). More importantly, the arrayed stretchable piezoelectric force sensor demonstrates exceptional sensitivity in detecting pressure, stretching, and bending deformations, with values of 740 mV/kPa, 380 mV/N, and 21.16 mV per 10% deformation, respectively. It also exhibits high reliability, as its output maintains durability and stability after undergoing 10000 stability cycles at 7 Hz. Application experiments with the arrayed sensor reveal its capability to detect distributed forces applied by weights and fingers, presenting novel applications in identifying object weight and hardness. Looking ahead, this research offers an unprecedented multimodal detection strategy for next-generation stretchable sensors, demonstrating exceptional practicality.

Fault diagnosis transfer learning models commonly employ deep neural networks (DNNs) to analyze time–frequency features. However, excessively DNNs can result in diminished generalization capabilities, leading to subpar performance of the model across various working conditions. Furthermore, inappropriate domain adaptation (DA) strategies significantly constrain the accuracy of the model. To address this issue, a robustly optimized residual-network and vision transformer (ViT) domain adaptation model is proposed in this article, combining wavelet packet transform (WPT), residual networks, and self-attention mechanisms. Firstly, the WPT is utilized to construct multi-band wavelet coefficient matrix (MWCM) and corresponding multi-band wavelet coefficient time–frequency feature matrix (MWSM) with small size and feature aggregation. Subsequently, a shallow robustly optimized residual network is designed to effectively extract features from MWCM, considering the spatial distance dependencies of features. Additionally, ViT is employed for time–frequency global feature extraction from MWSM. Furthermore, domain adversarial neural network and multi-kernel maximum mean discrepancy are employed to extract domain-invariant features from signals of different operating conditions and fault types. At last, three fault diagnosis experiments are conducted in multi-condition scenarios of bearings. The experimental results illustrate the superiority and effectiveness of the proposed model.