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

Leng Han, Song Feng, Guang Qiu, Jiufei Luo, Hong Xiao, Junhong Mao
2019 Sensors  
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
more » ... ility, 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.
doi:10.3390/s19071546 fatcat:s64phk7w4jckrgssyd6p6ah4ue