基于深度学习的油液磨粒智能检测与分割
Intelligent Detection and Segmentation of Wear Debris Based on Deep Learning
任松 1涂歆玥 1朱倩雯 1李眉慷1
作者信息
- 1. 重庆大学煤矿灾害动力学与控制国家重点实验室 重庆,400044
- 折叠
摘要
针对机械系统磨损状态监测与故障诊断中油液磨粒识别难度大、时间与人力成本高等问题,提出了基于深度学习的油液磨粒智能检测与分割方法.首先,基于滤膜谱片技术制备油液磨粒谱片并采集图像,构建了含6类不同金属磨粒的优质数据集;其次,根据数据集特点与算法优缺点,搭建单阶段实例分割模型YOLACT与两阶段实例分割模型Mask-RCNN对磨粒进行智能检测与分割.实验结果表明:Mask-RCNN模型平均检测精确率为93.8％,召回率为92.7％,适用于磨损颗粒智能分析的精准检测;YOLACT模型平均检测精确率为84.7％,召回率为83.3％,检测速度快,边缘分割精细,适用于磨损颗粒快速检测与智能分割;两种模型均有效提高了油液磨粒的检测效率.
Abstract
In view of the difficulties in high cost of time and labor in the identification of oil wear debris in me-chanical system wear state monitoring and fault diagnosis,an intelligent detection and segmentation method for oil wear debris based on deep learning is proposed.Based on the filter spectrum technology,this method pre-pares spectrum of oil debris to collect images,and constructe a high-quality dataset containing 6 different types of wear debris;According to the characteristics of the dataset and the advantages and disadvantages of the algo-rithm,the one-stage instance segmentation model YOLACT and the two-stage instance segmentation model Mask-RCNN are respectively constructed for Intelligent detection and segmentation of oil debris.The experi-mental results show that the average detection accuracy of the Mask-RCNN model is 93.8％,and the recall rate is 92.7％,which is suitable for the precise detection of wear debris intelligent analysis.The average detection ac-curacy of the YOLACT model is 84.7％,the recall rate is 83.3％,with advanced detection speed and detailed edge segmentation,which is suitable for rapid detection and intelligent segmentation of wear debris.Both mod-els effectively improve the detection efficiency of oil wear debris.
关键词
深度学习/卷积神经网络/分割模型/油液磨粒分析/金属磨粒检测Key words
deep learning/convolutional neural network/segmentation model/oil wear debris analysis/metal wear debris detection引用本文复制引用
出版年
2024
NETL
NSTL
万方数据