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| 1 | 航空发动机滑油磨粒在线监测显示文摘航空发动机轴承部件磨损是导致发动机失效,引起飞机重大事故的主要因素之一。分析了航空发动机突发性剧烈磨损的疲劳磨损失效机理,陈述了磨粒尺寸和数量表征磨损程度的关系,系统介绍了各种航空发动机滑油磨粒在线监测技术,讨论了各技术方法的原理、技术特点、典型参数、典型应用、最新研究成果及技术适应性。滑油磨粒在线监测技术,能有效发现航空发动机突发性剧烈磨损,及时预警失效,避免出现重大事故,具有重大工程应用价值。 | 孙衍山 杨昊 佟海滨 张雯 曾周末 | 2017 | 仪器仪表学报2017，38，7： | 29 |
| 2 | 一种融合在线铁谱图像特征信息的磨损状态诊断方法显示文摘采用在线铁谱图像表征机器磨损状态是铁谱诊断技术的核心和瓶颈.针对在线油液监测获取的磨粒图像信息,设计了一种融合在线铁谱图像特征信息的磨损状态诊断方法,首先通过对原始图像进行灰度化、直方图阈值二值化、高斯与椒盐增强、模板锐化、多次膨胀与腐蚀、边界跟踪、自适应阈值分割处理,获得较为准确的图像磨粒量化统计质量分数信息来量度相对磨损浓度;再融合能量、熵、惯性矩、局部平稳性等图像纹理特征对磨损状态进行分析、诊断、评价;最后采用RBF神经网络技术对铁谱磁性磨粒进行自动识别.实验验证了该方法的创造性和可行性. | 陶辉 冯伟 贺石中 陈闽杰 | 2012 | 哈尔滨理工大学学报2012，17，4： | 12 |
| 3 | 滑油磨粒在线传感技术研究进展显示文摘航空发动机的轴承和齿轮等区域的磨损,是造成发动机故障的重要原因。磨损产生的磨粒蕴含着有关旋转部件磨损状况的重要信息,对滑油中的磨粒进行在线监测是诊断旋转部件潜在故障的一种有效方法。首先分析了滑油磨粒的产生机理与表征研究进展,陈述了磨损特征与磨粒特征的关系及磨粒的典型特征,然后重点介绍了光学法、电磁法、声学法和能量法等发动机滑油磨粒在线监测技术,论述了各种技术方法的监测机理、技术特点、研究进展以及局限性,最后系统讨论了滑油磨粒在线监测的发展趋势和面临的挑战。 | 王奕首 吴迪恒 朱凌 刘渊 卿新林 | 2021 | 电子测量与仪器学报2021，35，3： | 9 |
| 4 | 润滑油污染在线监测技术研究进展显示文摘根据监测油液污染物的类型和检测手段,从基于磨粒分析、其他污染物监测以及综合监测、多元融合监测几个方面综述了润滑油污染在线监测技术,并针对目前在油液监测手段单一、监测机理片面以及监测效果有待加强等几个亟待解决的问题,提出了油液在线监测技术新的概念和深入进行系统全面的研究实用的油液在线监测技术的发展思路。 | 陈彬 刘阁 张贤明 黄朗 | 2012 | 应用化工2012，41，7： | 9 |
| 5 | Intelligent Identification of Wear Mechanism via On-line Ferrograph Images显示文摘Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring. | WU Tonghai PENG Yeping SHENG Chenxing WU Jiaoyi | 2014 | Chinese Journal of Mechanical Engineering2014，27，2： | 4 |
| 6 | 在线铁谱图像分析中基于蚁群算法改进Otsu的设计与应用显示文摘在线铁谱图像获取机器磨损状态信息是铁谱诊断技术的核心和瓶颈。针对在线铁谱磨粒图像的Kirsch边缘检测特征不明显和Otsu(最大类间方差法)获取最佳阈值的局限性及耗时等问题,设计了一种基于蚁群算法改进Otsu方法完成图像分割,并结合Kirsch边缘检测来提取磨粒图像信息的新方法。首先通过Kirsch算子检测出图像边缘,然后运用基于蚁群算法改进Otsu方法求取最佳阈值并进行二值化处理,最后采用灰度堆栈空间实现磨粒自动定位。通过现场对三峡电厂5号水轮发电机组2012年油液进行试验和数据分析、及近一年的机组开机老化运行,得出所设计的算法能够有效提取磨粒图像信息,同时节省运算时间,对水轮机组故障预测、诊断起到了良好的实际作用。 | 陶辉 陈闽杰 贺石中 冯伟 | 2014 | 电子设计工程2014，22，10： | 2 |
| 7 | Dimensional Description of On-line Wear Debris Images for Wear Characterization显示文摘As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle's chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area(IPCA), show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity. | WU Tonghai PENG Yeping DU Ying WANG Junqun | 2014 | Chinese Journal of Mechanical Engineering2014，27，6： | 1 |
| 8 | 保护电梯门的动态图像识别方法显示文摘针对用于电梯门保护的图像处理技术存在信息量大、动态特性强等问题,提出一种适用于电梯门保护系统的动态图像识别方法。改进了中值滤波方法,有效过滤了图像噪声并可保持边界特征;采用类间方差法对图像进行全局阈值分割,建立以背景差分法为主、相邻帧差法为辅的动态目标检测模型,采用区域过度生长算法实现动态目标跟踪和矢量方向判断。建立电梯门保护系统试验平台并进行试验验证。结果表明,该方法可在3帧图像内完成目标识别,对目标慢速、中速、快速移动判别的正确率分别达到83.3%、93.3%和100%。 | 吕新知 谷明非 宋世杰 宫大为 | 2021 | 软件导刊2021，20，5： | 1 |
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