The Segmentation of Wear Particles Images Using<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>J</mml:mi></mml:mrow></mml:math>-Segmentation Algorithm

Hong, Liu; Wei, Haijun; Wei, Lidui; Li, Jingming; Yang, Zhiyuan

doi:10.1155/2016/4931502

Cited by 6 publications

(3 citation statements)

References 19 publications

(24 reference statements)

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“…For example, Wang et al [13] combined the watershed algorithm with an improved ant colony clustering algorithm to achieve accurate segmentation of wear debris images. Liu et al [14] employed the unsupervised segmentation of colortexture regions in images and a video algorithm to segment a wear debris image, and they suggested that the method outperforms standard threshold segmentation and edge detection.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Xi,

Zhang,

2024

Meas. Sci. Technol.

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“…For example, Yu et al [ 26 ] used background subtraction methods to segment wear particles, allowing them to quantify the wear of pivot bearings. Liu et al [ 27 ] used the JPEG segmentation algorithm (JSEG) [ 28 ] to segment wear particles in ferrographic images. More recently, Wang et al [ 29 ] used CNNs to classify, quantify, and register wear debris.…”

Section: Introductionmentioning

confidence: 99%

A U-Net Based Approach for Automating Tribological Experiments

Staar

Bayrak

Paulkowski

et al. 2020

Sensors

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Tribological experiments (i.e., characterizing the friction and wear behavior of materials) are crucial for determining their potential areas of application. Automating such tests could hence help speed up the development of novel materials and coatings. Here, we utilize convolutional neural networks (CNNs) to automate a common experimental setup whereby an endoscopic camera was used to measure the contact area between a rubber sample and a spherical counterpart. Instead of manually determining the contact area, our approach utilizes a U-Net-like CNN architecture to automate this task, creating a much more efficient and versatile experimental setup. Using a 5× random permutation cross validation as well as additional sanity checks, we show that we approached human-level performance. To ensure a flexible and mobile setup, we implemented the method on an NVIDIA Jetson AGX Xavier development kit where we achieved ~18 frames per second by employing mixed-precision training.

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“…The quantity of wear particles and their morphology consequently accelerate the process and the intensity of wear (Kučera et al, 2016;Sejkorová and Glos, 2017). Wear particle analysis helps to assess the condition of the machinery and reliability in mechanical systems (Liu et al, 2016;Peng et al, 2015). The wear process results in generation of particles of various size, shape, quality and morphology (Akchurin et al, 2016;Hönig and Orsák, 2016;Wäsche et al, 2015).…”

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confidence: 99%

Morphology Analysis of Friction Particles Generated in Tractor Transmission Oils

Kučera

Aleš

2017

Acta Technologica Agriculturae

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The aim of the study was the examination of the morphological and compositional attributes of wear particles of tested universal tractor transmission oil (UTTO) samples by means of automatic particle counter and classifier LaserNetFines TM -C, which is an ideal tool to establish the dynamic equilibrium concentration and hence to set the alarm limits for any type of machine. Such limits can be based on wear type and particle size ranges. The samples of two UTTO (mineral oil and biodegradable fully synthetic oil) were taken from the rear gearboxes of the wheel tractor during a long-term experiment. The measurement results showed that there was faster increase in the particle wear of mineral oil in comparison to biodegradable transmission oil. Considering the increase in the percentage of particles over 20 μm, the biodegradable oil seems to be more favourable for usage. Regardless of this, it can be concluded that the biodegradable oil is equivalent to petroleum-based oils.

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The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm

Cited by 6 publications

References 19 publications

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

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

A U-Net Based Approach for Automating Tribological Experiments

Morphology Analysis of Friction Particles Generated in Tractor Transmission Oils

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