An improved YOLOv8-CGA-ASF-DBB method for multi-class wear debris recognition of online visual ferrograph image

Abstract

The analysis of wear based on on-line visual ferrograph provides crucial insights for the analysis of wear faults in mechanical equipment.However, online ferrograph analysis has been greatly limited by the low imaging quality and recognition accuracy of particle chains and high hubbles when analyzing lubricant oils in practical applications. To address this issue,this paper proposes an enhanced OLVF wear image detection model based on YOLOv8 and applies it to the multi-class intelligent recognition of ferrograph images .The cascade group attention module is introduced to enhance the diversity of features and improve computational efficiency. The attentional scale sequence fusion module is introduced to achieve precise and rapid recognition of small targets. This diverse branch block module is introduced efficiently to extracts features without compromising reasoning speed during training. For verification, a test of the bridge transmission box was conducted based on OLVF and 992 ferrograph images were collected. Experimental results reveal that the improved algorithm achieves an accuracy of 94.53% on the dataset of bridge transmission box ferrograph wear debris images collected through OLVF. This represents a 5.2% increase in recognition accuracy compared to the original algorithm while maintaining a processing time of only 0.69 ms per image. These findings provide compelling evidence for significant enhancements in both recognition accuracy and processing speed achieved by the improved algorithm, thereby establishing its considerable value for engineering applications.