Abstract
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.
Highlights
The wear debris in lube oil are closely related to the wear state of the machines
Bubbles appear as a low Multiscale analysisshadow of OLVFinferrograms was performed using uniform discrete curvelet transformation (UDCT)
A wear debris image with a clearsegmentation edge can be the method is used in online gear wear monitoring
Summary
The wear debris in lube oil are closely related to the wear state of the machines. The size and form of the debris can reflect the wear level and reveal the content and temperature rise of the particular material. Oil debris analysis has become an essential condition monitoring technique that is utilized to diagnose wear and serve as an early warning system. The electromagnetic wear detection sensor and image wear detection sensor are commonly used in online debris monitoring. When metal particles pass through the detection coil, they change the inductance of the coil or the magnetic flux through the coil such that the electromagnetic debris sensor can detect the inductive voltage of the coil, which can be measured in real time to allow for abrasive particle monitoring [2,3,4,5]. For the image wear detection sensor, detection is mainly based on transmission imaging [6] and reflection imaging [7] with a high detection accuracy of up to 5 microns
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