Abstract Typical wear particles can be considered as distinctive indicators of on-going wear faults in machines. However, the small number of samples has limited the identification accuracy for similar fault particles. Besides, the three-dimensional (3D) characterization of wear particles may face huge challenges due to excessive surface parameters. Focusing on the problems of high similarity particles and few samples, a CNN-based particle classification method is developed with an example set of fatigue and severe sliding particles, which are the product of severe wear of machines. For data reduction, imaged 3D particle surfaces are firstly converted into 2D depth maps without losing surface information. Virtual fault particle images are then synthesized using a Conditional Generative Adversarial Networks (CGAN), according to the particle generation mechanism and distinctive particle features. Furthermore, a non-parametric particle identification model is established with the optimization of the CNN structures and training method, and the network is further optimized with the particle image standardization and the network visualization. Validation experiments reveal that the proposed method can accurately identify all tested fatigue and severe sliding particles with their typical characteristics.

中文翻译：

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用于识别少量样本中相似 3D 磨损颗粒的优化 CNN 模型

摘要 典型的磨损颗粒可以被认为是机器持续磨损故障的独特指标。然而，样本数量少限制了对相似断层粒子的识别精度。此外，由于过度的表面参数，磨损颗粒的三维（3D）表征可能面临巨大挑战。针对粒子相似度高、样本少的问题，以机器严重磨损产生的疲劳和严重滑动粒子为例，开发了一种基于CNN的粒子分类方法。为了减少数据，首先将成像的 3D 粒子表面转换为 2D 深度图，而不会丢失表面信息。然后使用条件生成对抗网络 (CGAN) 合成虚拟断层粒子图像，根据粒子的产生机制和独特的粒子特征。此外，通过优化CNN结构和训练方法建立非参数粒子识别模型，并通过粒子图像标准化和网络可视化进一步优化网络。验证实验表明，所提出的方法可以准确识别所有具有典型特征的测试疲劳和严重滑动颗粒。