The fault-induced impulse responses of localized bearing fault are usually interfered by the background noise and other harmonic components. They are strongly coupled together and are hard to be separated. It is crucial to develop a fast and reliable method to extract the impulse-based feature for online bearing fault diagnosis in the industry application. In this article, we propose a new sparse elitist group lasso denoising (SEGLD) algorithm in frequency domain to detect the incipient impulse-based fault feature, which is free of utilizing the prior knowledge. We first reveal the sparse characteristics of the bearing fault signals in frequency domain. Then, a tailored denoising model is proposed. To obtain a satisfactory analytical stationary solution, the Douglas-Rachford splitting solver is employed for the denoising model. Moreover, we explore the relationship between the best regularization parameters, the periodic information and the normalization estimated noise of the rolling bearing fault signal. A rule of adaptively selecting the best regularization parameters is demonstrated. Finally, the robustness and effectiveness of the proposed SEGLD algorithm are profoundly verified by the numerical simulation and two evaluation experiments under the conditions of early fault stage and low speed scenario. Also, it is demonstrated that the proposed approach outperforms the state-of-the-art method for extracting the weak fault feature.

A novel fiber Bragg grating (FBG) sensing-based acceleration sensor has been proposed to simultaneously decouple and measure temperature and acceleration in real-time. This design applied a diaphragm structure and utilized the axial property of a tightly suspended optical fiber, enabling improvement in its sensitivity and resonant frequency and achieve a low cross-sensitivity. The theoretical vibrational model of the sensor has been built, and its design parameters and sensing properties have been analyzed through the numerical analysis. A decoupling method has been presented with consideration of the thermal expansion of the sensor structure to realize temperature compensation. Experimental results show that the temperature sensitivity is 8.66 pm/°C within the range of 30–90 °C. The acceleration sensitivity is 20.189 pm/g with a linearity of 0.764% within the range of 5~65 m/s². The corresponding working bandwidth is 10~200 Hz and its resonant frequency is 600 Hz. This sensor possesses an excellent impact resistance for the cross direction, and the cross-axis sensitivity is below 3.31%. This implementation can avoid the FBG-pasting procedure and overcome its associated shortcomings. The performance of the proposed acceleration sensor can be easily adjusted by modifying their corresponding physical parameters to satisfy requirements from different vibration measurements.

Herein, a series of Ag coatings with different micro-dimples were fabricated on copper surfaces by laser surface texturing (LST) and magnetron sputtering. Multilayer graphene lubricating grease (MGLG) was prepared using multilayer graphene as an additive. The textured Ag coatings and MGLG were characterized. Moreover, the tribological and electrical performances of the textured Ag coatings under MGLG lubrication were investigated in detail. Results demonstrated that the textured Ag coating with an appropriate dimple diameter could exhibit improved tribological and electrical properties when compared to the non-textured Ag coating under MGLG lubrication. The characterization and analysis of the worn surfaces suggest that the synergetic effect of LST and MGLG contributes to these excellent tribological and electrical properties.