A direct reflection OLVF debris detector based on dark-field imaging

Li, Bo; Xi, Yinhu; Feng, Song; Mao, Junhong; Xie, Youbai

doi:10.1088/1361-6501/aab9fc

Cited by 25 publications

(7 citation statements)

References 20 publications

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“…Since the amplification amount is always not even, the extra pixel is added to the right side, as described in Eqs. ( 9) an (10). Similarly, the amplification strategy can be migrated to the width direction to ensure that width H is visible by P. The expanded pixels of the augmented pooling are defined in Eqs.…”

Section: P L Pmentioning

confidence: 99%

“…The online ferrograph sensor is composed of the excitation device, the micro-flow channel, the imaging system, and the light sources [8]. Based on the controllable electromagnetic field, the sensor has been systematically studied to improve the particle imaging quality [9,10]. For example, the magnetic pole has been designed for depositing particles in the sensor view field via the law of magnetic field distribution [11], and the imaging system is established with the optical imaging principle to enhance the resolution and clarity of wear particles [12].…”

Section: Introductionmentioning

confidence: 99%

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Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Wang,

Wan,

et al. 2023

Friction

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Ferrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

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Section: P L Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Wang,

Wan,

et al. 2023

Friction

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show abstract

“…Therefore, the preprocessing of the contaminated oil samples is required. To the best of our knowledge, the segmentation of wear debris images with contaminated oil without any pre-processing is rarely reported [20].…”

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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“…The online visual ferrograph (OLVF) is an exemplary technology derived from the conventional ferrograph [ 18 , 19 ]. In practical applications, OLVF is positioned externally to the lubrication system and effectively captures minuscule wear particles in the lube oil through the utilization of a high-gradient-magnetic-field force.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet

You,

Fan,

et al. 2024

Sensors

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The real-time monitoring and fault diagnosis of modern machinery and equipment impose higher demands on equipment maintenance, with the extraction of morphological characteristics of wear debris in lubricating oil emerging as a critical approach for real-time monitoring of wear, holding significant importance in the field. The online visual ferrograph (OLVF) technique serves as a representative method in this study. Various semantic segmentation approaches, such as DeepLabV3+, PSPNet, Segformer, Unet, and other models, are employed to process the oil wear particle image for conducting comparative experiments. In order to accurately segment the minute wear debris in oil abrasive images and mitigate the influence of reflection and bubbles, we propose a multi-level feature reused Unet (MFR Unet) that enhances the residual link strategy of Unet for improved identification of tiny wear debris in ferrograms, leading to superior segmentation results.

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A direct reflection OLVF debris detector based on dark-field imaging

Cited by 25 publications

References 20 publications

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

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

Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet

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