A Full Field-of-View Online Visual Ferrograph Debris Detector Based on Reflected Light Microscopic Imaging

Li, Bo; Wu, Wei; Zhou, Mi; Xi, Yinhu; Wei, Hangxin; Mao, Junhong

doi:10.1109/jsen.2021.3079174

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…The ferrography images are obtained by using an OLVF, and the schematic diagram of this is shown in figure 3. Moreover, more detail about the test rig can be found in the literature [31]. All the ferrography images are photographed by selecting the transmission light source and the automatic mode detection.…”

Section: Datasetmentioning

confidence: 99%

Small data-driven semantic segmentation of wear debris in ferrography images

Xi,

Zhang,

2024

Meas. Sci. Technol.

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The segmentation of wear debris images is a prerequisite for ferrographic analysis, and uncertainties and errors in wear debris segmentation will inevitably affect the subsequent analysis. In this work, a small-data semantic segmentation model of wear debris images is constructed based on HRNetv2 for ferrography images acquired by using an online visual ferrography (OLVF). A major advantage of the current model is that fewer ferrography images are required for training, and fewer iterations are needed. The current work is performed for ferrography images with both clean oil and contaminated oil, and good segmentation results can be found. Specially, the experimental results show that the current model can achieve accurate segmentation of wear debris images with mean intersection over union (mIoU) values of 91.47% and mean pixel accuracy (mPA) values of 96.48%.

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Section: Datasetmentioning

confidence: 99%

Small data-driven semantic segmentation of wear debris in ferrography images

Xi,

Zhang,

2024

Meas. Sci. Technol.

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“…This approach exhibits versatility as it is capable of detecting wear particles of diverse nature, while simultaneously offering insights into their dimensions and morphology. Li et al proposed a novel full-field OLVF wear particle detection sensor that employs reflected light microscopy imaging to improve the precision of on-line wear particle monitoring in 2021 [82]. Through the utilization of Matlab R2023a numerical simulations and subsequent experimental validation, the research conducted by the authors showcased the efficacy and reliability of a newly developed sensor in capturing comprehensive reflection ferrographs of wear debris present in lubricating oil.…”

Section: Image Processing Sensormentioning

confidence: 99%

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Han,

Mu,

Liu

et al. 2023

JMSE

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In the field of marine engineering, the friction and wear experienced by rotating mechanisms are recognized as significant contributors to the failure of marine machinery. In order to enhance the safety and dependability of marine ship operations, the implementation of on-line oil wear debris particle detection sensors enables the on-line monitoring of oil and facilitates the rapid identification of abnormal wear locations. This paper provides a critical review of the recent research progress and development trends in the field of sensors for on-line detection of oil wear debris particles. According to the method of sensor detection, wear debris particle detection sensors can be classified into two distinct categories: electrical and non-electrical sensors. Electrical sensors encompass a range of types, including inductive, capacitive, and resistive sensors. Non-electrical sensors encompass a range of technologies, such as image processing sensors, optical sensors, and ultrasonic sensors. Finally, this review addresses the future research directions for wear debris particle detection sensors in light of the challenging problems currently faced by these sensors.

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“…1) Visual Sensors: In robot vision, image sensors such as the charge-coupled devices (CCDs) [63] and the complementary metal-oxide silicon (CMOS) [64], [65] that produce video frames have been applied to visual tracking, localization, and navigation [66]- [73] of robots. They have the advantages of low cost and high resolution of small pixels.…”

Section: A Sensors and Systemsmentioning

confidence: 99%

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Miao

Zhou

et al. 2022

IEEE Trans. Med. Robot. Bionics

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Humanoid robots are designed and expected to resemble humans in structure and behavior, showing increasing application potentials in various fields. Like their biological counterparts, their environmental perception ability is fundamental. In particular, the visual and tactile perception are the two main sensory modes that humanoids use to understand and interact with the environment. Vision-Tactile Fusion Perception (VTFP) has shown multiple possibilities for better sensing understanding in challenging conditions, causing new research interests and questions. The overlap between visual and tactile perception in humanoids is continually growing. This work has reviewed the current state of the art of VTFP. It starts with the physiological basis of biological vision and tactile systems as well as the VTFP mechanisms as inspirations for humanoid perception. Then, the bioinspired visual-tactile fusion systems for humanoids are reviewed as the emphasis. After the survey on the vision and tactile sensors of robots, seven currently publicly available VTFP datasets are introduced. They are the data sources for several studies on neural network-inspired fusion algorithms. Furthermore, the applications of VTFP on humanoids are summarized. Finally, the challenges and future work are discussed. This review aims to provide several references for further exploitation of VTFP and its applications on humanoids.

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A Full Field-of-View Online Visual Ferrograph Debris Detector Based on Reflected Light Microscopic Imaging

Cited by 19 publications

References 23 publications

Small data-driven semantic segmentation of wear debris in ferrography images

Small data-driven semantic segmentation of wear debris in ferrography images

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

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