首页|气泡强干扰小波调制的全视场在线可视铁谱磨屑图像分割算法

气泡强干扰小波调制的全视场在线可视铁谱磨屑图像分割算法

Wear Debris Image Segmentation Algorithm Based on Wavelet Modulation of Strong Bubble Interference for Full Field-of-view Online Visual Ferrograph

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为解决全视场在线可视铁谱(Online visual ferrograph,OLVF)工程应用存在的离焦气泡干扰状态下反射光谱片磨屑图像分割问题,构建了基于双曲正切函数的低频与高频非线性阈值函数,以此为基础,提出了气泡强干扰小波调制的磨屑图像分割算法,抑制了反射光谱片中的气泡阴影,消除了背景噪声,增强了磨屑图像锐度和对比度,实现了全视场OLVF反射光谱片磨屑图像的有效分割.对比不同算法处理的磨屑图像分割效果,结果显示:本算法通过调制气泡阴影和背景噪声能够更准确达到磨屑图像完整分割.最后,开展了齿轮磨损在线监测实验,提取了磨屑浓度指标反映齿轮全生命期的磨损变化,结果表明:本算法克服了全视场OLVF采样过程中气泡阴影对反射光谱片磨屑图像分割的影响,可准确的探测出齿面异常磨损失效故障,进一步验证了本算法应用于全视场OLVF油液磨屑监测之有效性.
To solve the problems of wear debris image segmentation of reflected ferrogram captured by full field-of-view online visual ferrograph(OLVF)under the condition of out-of-focus bubble interference,a low frequency nonlinear threshold function and a high frequency one are derivated respectively by using hyperbolic tangent function.On the basis of the novel threshold functions,a wear debris image segmentation algorithm based on wavelet modulation of strong bubble interference is proposed.In order to realize the effective segmentation of wear debris image of full field-of-view OLVF reflected ferrogram,the bubble impression and background noise in the reflected ferrogram need to be suppressed and the contrast and sharpness of its wear debris image are able to be enhanced by using this current one.Comparing with the segmentation effects of wear debris images processed by other algorithms,the results show that the complete segmentation of wear debris images in the reflected ferrogram can be achieved more accurately by modulating bubble impression and background noise with the proposed algorithm.Finally,an online monitoring experimental measurement of gear wear is carried out,and the index of debris concentration is extracted and obtained to characterize the fast-changing of gear wear in the full useful life.Results show that the effects of bubble impression on the vilidity of wear debris image segmentation of reflected ferrogram during the sampling periods of full field-of-view OLVF can be eliminated by this current algorithm,and the abnormal wear fault of gear failure is accurately detected.Accordingly,it has been further verified that this proposed algorithm for full field-of-view OLVF is applied to extracting the visual characteristics of wear debris in in-use lubricants.

full field-of-view OLVFbubble impressionwavelet modulationreflected ferrogramwear debris segmentation

李博、袁勋、吴伟、魏航信、万志国

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西安石油大学机械工程学院 西安 710065

全视场在线可视铁谱 气泡阴影 小波调制 反射光谱片 磨屑图像分割

西安市科技计划陕西省自然科学基础研究计划基金陕西省自然科学基础研究计划基金西安石油大学研究生教育综合改革研究与实践资助项目

22GXFW01032021JM-4052022JQ412YCS23214271

2024

10.3901/JME.2024.15.160

机械工程学报
中国机械工程学会

机械工程学报

CSTPCD北大核心
影响因子：1.362
ISSN：0577-6686
年,卷(期)：2024.60(15)
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