Ferrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

基于铁谱仪的磨损碎片分析 (WDA) 为机械设备的磨损故障分析提供了重要信息。经过几十年的离线应用，这种传统技术正在由在线铁谱传感器驱动，用于实时磨损状态监测。然而，在实际应用中分析降解润滑油时，在线铁谱技术由于成像质量低和颗粒链分割精度低而受到很大限制。为了解决这个问题，开发了一种集成优化方法，重点关注两个方面：在线铁谱传感器的结构重新设计和粒子链的智能分割。为了提高磨损颗粒的成像质量，对在线铁谱传感器的磁极进行了优化，使成像系统能够直接观察磨损颗粒，而无需穿透油污。此外，基于光强分布理论建立了光源仿真模型，并确定了在线铁谱传感器中颗粒照明均匀性的LED安装参数。在此基础上，针对不规则颗粒形态，专门建立感兴趣区域（ROI）生成层和ROI对齐层，构建了基于Mask-RCNN的颗粒链分割模型。通过这些措施，设计了一种新型在线铁谱传感器，以增强磨损颗粒的图像采集和信息提取。为了进行验证，对所开发的传感器进行了测试，以收集不同降解油的颗粒图像，并使用基于 Mask-RCNN 的模型进一步处理图像以进行颗粒特征提取。实验结果表明，优化后的在线铁谱技术即使在高度降解的润滑油中也能捕获清晰的颗粒图像，成像场照明均匀度达到90%。最重要的是，磨损颗粒的统计准确率从67.2%提高到94.1%。