For mechanical equipment, the wear particle in the lubrication system during equipment operation can reflect the lubrication condition, wear mechanism, and severity of wear between equipment friction pairs. To solve the problems of false detection and missed detection of small, dense, and overlapping wear particles in the current ferrography wear particle detection model in a complex oil background environment, a new ferrography wear particle detection network, EYBNet, is proposed. Firstly, the MSRCR algorithm is used to enhance the contrast of wear particle images and reduce the interference of complex lubricant backgrounds. Secondly, under the framework of YOLOv5s, the accuracy of network detection is improved by introducing DWConv and the accuracy of the entire network is improved by optimizing the loss function of the detection network. Then, by adding an ECAM to the backbone network of YOLOv5s, the saliency of wear particles in the images is enhanced, and the feature expression ability of wear particles in the detection network is enhanced. Finally, the path aggregation network structure in YOLOv5s is replaced with a weighted BiFPN structure to achieve efficient bidirectional cross-scale connections and weighted feature fusion. The experimental results show that the average accuracy is increased by 4.46%, up to 91.3%, compared with YOLOv5s, and the detection speed is 50.5FPS.

For the purpose of extracting the frictional vibration characteristics of the friction pair during friction and wear in different friction states, the friction and wear tests of friction pair in different friction states were conducted on a testing machine. Higher-dimensional fractal and multifractal characteristics hidden in time series can be examined by multifractal detrended fluctuation analysis (MFDFA) method. The frictional vibration time-domain signals, the friction coefficient signals and the frictional vibration frequency-domain signals were analyzed and multifractal spectra were acquired by using the MFDFA algorithm. According to the spectra, the multifractal spectrum parameters of these signals were calculated to realize the quantitative characterization of frictional vibration characteristics in different friction states. The analysis shows that it is symmetric in the variation trends of the multifractal spectrum parameters of the frictional vibration signals and the friction coefficient data. Based on the multifractal spectrum parameters of frictional vibration, the principal component analysis (PCA) algorithm was applied to establish the friction state recognition method. The results show that the multifractal spectra and their parameters can characterize the frictional vibrations, and the friction state recognition can be realized based on the multifractal spectrum parameters of frictional vibrations.

The intelligent recognition technology for ferrography images is one of the important methods for diagnosis fault and state detection of machines. In allusion to these questions for the influences of wear particle images’ blurring, background intricacy, wear particle overlapping and lack of light, and others which lead to be the reason for the difficulty of achieving accurate identification, missed detection, and false detection, an intelligent recognition algorithm for ferrography wear particle based on convolutional block attention module (CBAM) and YOLOv5 is proposed. Firstly, it needs enhancement to improve contrast for ferrography wear particle images and lower background interference by adaptive histogram homogenization algorithm. Then, under the framework of YOLOv5 algorithm, the depthwise separable convolution is added to improve the detection speed of the network, and the detection accuracy of the entire network is improved by optimizing the loss function. Moreover, increase weight ratio on wear particle in images by adding a convolution block CBAM model and increase feature representative capability in detection network with YOLOv5 algorithm detection network, which can improve detection accuracy for wear particle. Finally, compare the algorithm with the three classical homologous series object detection algorithm. The experimental results show that the detection accuracy of the model can reach 96.7%, and the detection speed is 32 FPS for the images with a resolution of . It can be developed and applied to the fault diagnosis and condition monitoring of mechanical equipment.

Since the implementation of the sulfur cap legislation in 2020, marine very-low-sulfur fuel oil, often known as VLSFO, has become a crucial source of fuel for the contemporary shipping industry. However, both the production and utilization processes of VLSFO are plagued by the poor miscibility of the cutter fraction and the residual fraction, which can result in the precipitation of asphaltene. In this study, biodiesel was chosen as a cutter fraction to improve the stability and compatibility of asphaltene in VLSFO because of its environmental benefit and strong solubility. The average chemical structure of asphaltene derived from the marine low-sulfur fuel oil sample was analyzed using element analysis, FTIR, ¹HNMR, and time-flight spectroscopy. The composition of biodiesel was analyzed using GC-MS. It was found that the asphaltene had a feature of a short side chain, low H/C ratio, high aromaticity, and a high proportion of heteroatoms. Both laboratory experiments and molecular dynamic simulations were applied to investigate the dispersion effect and mechanism compared with other dispersants. The dispersion effect of biodiesel was studied using measurements of the initial precipitation point (IPP), dispersion improvement rate, and morphology of asphaltene in the model oil. Experimental results revealed that biodiesel was fully compatible with heavy fuel oil and that it can postpone the IPP from 46% to 54% and increase the dispersion improvement rate to 35%. Molecular dynamics (MDs) simulation results show that biodiesel can form strong interactions with the fused aromatics structures and heteroatoms in the asphaltene; such interactions can increase the solubility of asphaltene and acts as a “connection bridge” to promote the dispersion effect of asphaltene molecules.

The wear particle classification algorithm proposed is based on an integrated ResNet50 and Vision Transformer, aiming to address the problems of a complex background, overlapping and similar characteristics of wear particles, low classification accuracy, and the difficult identification of small target wear particles in the region. Firstly, an ESRGAN algorithm is used to improve image resolution, and then the Separable Vision Transformer (SepViT) is introduced to replace ViT. The ResNet50-SepViT model (SV-ERnet) is integrated by combining the ResNet50 network with SepViT through weighted soft voting, enabling the intelligent identification of wear particles through transfer learning. Finally, in order to reveal the action mechanism of SepViT, the different abrasive characteristics extracted by the SepViT model are visually explained using the Grad-CAM visualization method. The experimental results show that the proposed integrated SV-ERnet has a high recognition rate and robustness, with an accuracy of 94.1% on the test set. This accuracy is 1.8%, 6.5%, 4.7%, 4.4%, and 6.8% higher than that of ResNet101, VGG16, MobileNetV2, AlexNet, and EfficientV1, respectively; furthermore, it was found that the optimal weighting factors are 0.5 and 0.5.