【摘要】 为了研究尾轴承黏滑振动,首先,采用机器视觉技术采集水润滑橡胶尾轴承黏滑振动图像;其次,运用持续同调机器学习及单纯复形同调群分析图像,计算振动图像单纯复形的同调获得相应的条码图;然后,基于条码图获取振动图像的拓扑特征;最后,用改进型支持向量机机器学习法对拓扑特征进行研究,完成水润滑橡胶尾轴承黏滑振动鸣音的分类与识别。研究表明,最长贝蒂条码的长度与振动密切相关,可以有效预警鸣音,并建立了鸣音过程的智能化描述,为研究尾轴承黏滑振动提供一种新的思路。更多还原

【Abstract】 In order to study stick-slip vibration of water-lubricated rubber stern tube bearing,firstly,stick-slip vibration images are collected by machine vision technology. Secondly,images are analyzed by the methods of persistent homology based machine learning and simplicial complex,the corresponding barcodes are obtained by calculating the homology of the vibration images’ simple complex. Then,the topological characteristics of the vibration images are obtained based on the barcode images. Finally,the improved support vector machine learning is used to study the topological features,the classification and identification of the stick-slip vibration of water-lubricated rubber stern tube bearing are completed. The results have shown that the length of the longest Betti barcode is closely related to vibration,which can effectively warn the beep,establish an intelligent description of the beep process,and provide a new idea for stick-slip vibration of the stern bearing.更多还原