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.

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.

Purpose: This study aims to develop a lightweight and efficient classification model for wear particle analysis in lubricating oil, a crucial task in machinery condition monitoring and fault diagnosis. A key challenge is the severe class imbalance among particle categories, where conventional models often fail to recognize minority classes. Design/methodology/approach: A lightweight classification model, termed MobileNeSt, is proposed by integrating depthwise separable convolution, Split Attention and the H-Swish activation function within an efficient convolutional architecture. To alleviate class imbalance, a weighted loss function is incorporated to enhance the recognition capability for underrepresented wear particle categories. Findings: Experimental results demonstrate the superior performance of MobileNeSt compared with representative models such as ResNet, DenseNet and MobileNet, achieving notable gains for minority classes (Fatigue and Sliding). Ablation studies further confirm the effectiveness of its components and validate its efficiency. MobileNeSt achieved 93.54% accuracy with only 10.6 M parameters, 0.68 GFLOPs and an inference speed of 255.52 FPS, striking an excellent balance between accuracy and efficiency. Originality/value: This study proposes a lightweight wear particle classification framework that effectively mitigates class imbalance while satisfying practical requirements for lightweight design and fast inference.