Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

Since inline monitoring method has the advantages of no sampling, being real-time, no human intervention, and low error, this paper innovatively proposes to study the inline monitoring of wear particles in an oil pipeline, from the perspective of the different motion characteristics of the particles. In this paper, an inline optical sensor was designed and developed by studying the velocity characteristics of different particles through theoretical calculations, numerical simulations, and experimental analysis. First, an equation for particle motion was statistically established, based on the forces acting on wear particles in an oil-filled vertical tube. Then a finite element model of particle motion in a full-flow oil pipeline was created, to simulate particle motion with various diameters, densities, locations, and shapes. Finally, the results of the theoretical study were effectively applied to design an inline optical monitoring sensor, and the experimental validation results demonstrated that the inline sensor has excellent suitability for monitoring wear particles. This study has significance for the safe operation of large rotating machinery.

The presence of water accumulation on aircraft surfaces constitutes a considerable hazard to both performance and safety, necessitating vigilant inspection and maintenance protocols. In this study, we introduce an innovative semantic segmentation model, grounded in deep learning principles, for the precise identification and delineation of water accumulation areas within infrared images of aircraft exteriors. Our proposed model harnesses the robust features of ResNet, serving as the foundational architecture for U-Net, thereby augmenting the model’s capacity for comprehensive feature characterization. The incorporation of channel attention mechanisms, spatial attention mechanisms, and depthwise separable convolution further refines the network structure, contributing to enhanced segmentation performance. Through rigorous experimentation, our model surpasses existing benchmarks, yielding a commendable 22.44% reduction in computational effort and a substantial 38.89% reduction in parameter count. The model’s outstanding performance is particularly noteworthy, registering a 92.67% mean intersection over union and a 97.97% mean pixel accuracy. The hallmark of our innovation lies in the model’s efficacy in the precise detection and segmentation of water accumulation areas on aircraft skin. Beyond this, our approach holds promise for addressing analogous challenges in aviation and related domains. The enumeration of specific quantitative outcomes underscores the superior efficacy of our model, rendering it a compelling solution for precise detection and segmentation tasks. The demonstrated reductions in computational effort and parameter count underscore the model’s efficiency, fortifying its relevance in broader contexts.