Segmentation of Online Ferrograph Images with Strong Interference Based on Uniform Discrete Curvelet Transformation

Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically changing interference shado...

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Veröffentlicht in:Sensors (Basel, Switzerland) Switzerland), 2019-03, Vol.19 (7), p.1546
Hauptverfasser: Han, Leng, Feng, Song, Qiu, Guang, Luo, Jiufei, Xiao, Hong, Mao, Junhong
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Sprache:eng
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Zusammenfassung:Through real-time acquisition of the visual characteristics of wear debris in lube oil, an on-line visual ferrograph (OLVF) achieves online monitoring of equipment wear in practice. However, since a large number of bubbles can exist in lube oil and appear as a dynamically changing interference shadow in OLVF ferrograms, traditional algorithms may easily misidentify the interference shadow as wear debris, resulting in a large error in the extracted wear debris characteristic. Based on this possibility, a jam-proof uniform discrete curvelet transformation (UDCT)-based method for the binarization of wear debris images was proposed. Through multiscale analysis of the OLVF ferrograms using UDCT and nonlinear transformation of UDCT coefficients, low-frequency suppression and high-frequency denoising of wear debris images were conducted. Then, the Otsu algorithm was used to achieve binarization of wear debris images under strong interference influence.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19071546