Semantic segmentation of ferrography images for automatic wear particle analysis. (April 2021)

• Record Type: Journal Article
• Authors: Liu, Xinliang; Wang, Jingqiu; Sun, Kang; Cheng, Liang; Wu, Ming; Wang, Xiaolei
• Is Part Of: Engineering failure analysis. Volume 122(2021)
• Publication Date: 2021-04
• Subjects: Wear particle analysis -- Ferrography -- Convolutional neural network -- Semantic segmentation; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2021.105268
• Languages: English
• ISSNs: 1350-6307

Abstract

Research highlights: An improved DCNN model for semantic segmentation of wear particle images. An end-to-end process for the wear particle classification. An Encoder-ASPP-Decoder architecture for semantic segmentation DCNN. Abstract: Automatic wear particle detection and classification has remained a high priority research area for wear condition monitoring and failure analysis. In this study, a deep convolutional neural network (DCNN) with three modules, namely, an encoder, atrous spatial pyramid pooling (ASPP), and a decoder, is constructed. Instead of using handcrafted features, the DCNN can automatically learn features through a layer-wise representation and realize semantic segmentation, i.e., segmentation and identification concurrently, of five types of wear particles in ferrograph images using end-to-end processing. Experimental results show that the DCNN achieves 82.5% accuracy. This proposed method unifies the segmentation, classification, and edge location of the wear particles into a single model, avoids the accumulation and transmission of errors caused by numerous steps applied in a traditional linear process, and improves the efficiency and accuracy of the wear particle analysis.