The effect of TiB₂ particles content (10–40 wt.%) on the microstructure, mechanical properties and tribological properties of TiB₂-reinforced Inconel 718 alloy composite coatings by laser cladding was investigated. From the perspective of solidification thermodynamics and dynamics, when the TiB₂ particles content increases from 10 to 30 wt.%, the cooling rate increases for the increase in thermal conductivity and thermal diffusion coefficient, leading to the decrease in dendrite size, and the uniformity of TiB₂ particles becomes better for the decrease in the critical capture speed of the solid–liquid interface, causing the improvement of microhardness and tribological properties. However, when the TiB₂ particles content is too high (40 wt.%), the cooling rate decreases for the increase in heat released by solidification, so the dendrite size increases, and the Marangoni convection is too weak to drive the rearrangement of TiB₂ particles, leading to the heterogeneous microstructure, large fluctuation of microhardness and the deterioration of tribological properties. When the TiB₂ particles content is 30 wt.%, the composite coating has the finest and densest dendrites and evenly distributed TiB₂ particles, the bonding strength is as high as 1.714 GPa, the microhardness is up to 844.33 HV_0.2, which is 2.98 times that of Inconel 718 alloy coating, and the friction coefficient and the wear rate are 0.355 and 9.12 × 10⁻⁷ g/(N·m), which are 22.99% and 83.86% lower than those of the Inconel 718 alloy coating.

Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%.

Ultrasonic guided wave (UGW) nondestructive testing suffers from poor signal interpretation accuracy caused by the coherent noise that is related to dispersion, multi-mode and mode conversion. In order to split the L(0,2) UGW from the coherent noise, split-spectrum processing with raised cosine filters of constant frequency-to-bandwidth ratio (FBR-RC-SSP) is proposed. With the advantages of time domain resolution and frequency domain split, FBR-RC-SSP is studied based on time-frequency analysis using the chirplet transform, and the effects of filter parameters on signal-to-noise ratio gain (SNRG) and defect-to-coherent noise gain (DCRG) are explained. The excellent effects of eliminating the coherent noise and improving the signal resolution in FBR-RC-SSP are reported by the validation of synthesized, simulated and experimental UGW signals, of which the average SNRG and DCRG are 22.92% and 23.71% higher than those of traditional SSP using Gaussian filters, and it has the potential to locate and characterize defects in further UGW testing research.

Pipeline systems are prone to defects due to the harsh service conditions, which may induce catastrophic failure if found not in time. Ultrasonic guided wave (UGW) testing provides a convenient option for pipeline detection, showing high-efficiency, non-contact, long-distance and large-scale capabilities. To address the problem that UGW signals suffer from poor signal resolution that is mainly related to the coherent noise caused by the dispersion, multi-mode and mode conversion, an advanced signal processing method called VWISSP, based on variational mode decomposition (VMD), wavelet transform (WT), and improved split spectrum processing (ISSP) was proposed, of which SSP was improved by replacing the Gaussian filter bank with cosine filters of constant frequency-to-bandwidth and frequency-to-filter spacing ratios. Compared with ISSP, VWISSP shows better higher accuracy and resolution processing effects to noisy multi-defect UGW signals, which is manifested through the improvement of both the signal-to-noise ratio gain and the defect-to-noise gain. Only feature signals (defects and pipe end) are retained, whereas noise signals are eliminated completely.

Proper surface lubrication with optimal grease thickness is essential for extending steel wire rope service life. To achieve automated lubrication quality control and address challenges like variable lighting and motion blur that degrade recognition accuracy in practical settings, this paper proposes an improved lightweight GEMR-MobileViT. The model is designed to identify the grease thickness on steel wire rope surfaces while mitigating the high parameters and computational complexity of existing models. In this model, part of the standard convolution is replaced by GhostConv, a novel efficient multi-scale attention (EMA) module is introduced into the local expression part of the MobileViT block, and the structure of residual connections within the MobileViT block is designed. A transfer learning method is then employed. A custom dataset of steel wire rope lubrication images was constructed for model training. The experimental results demonstrated that GEMR-MobileViT achieved a recognition accuracy of 96.63% across five grease thickness categories, with 4.19 M params and 1.31 GFLOPs computational complexity. Compared to the pre-improvement version, recognition accuracy improved by 4.4%, while its parameters and computational complexity were reduced by 15.2% and 10.3%, respectively. When compared with current mainstream classification models such as ConvNeXtV2, EfficientNetV2, EdgeNeXt, NextViT, and MobileNetV4, our GEMR-MobileViT achieved superior recognition accuracy and demonstrated significant advantages in its model parameters, striking a good balance between recognition precision and model size. The proposed model facilitates deployment in steel wire rope lubrication working sites, enabling the real-time monitoring of surface grease thickness, thereby offering a novel approach for automating steel wire rope maintenance.