A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography

Feng, Song; Qiu, Guang; Luo, Jiufei; Han, Leng; Mao, Junhong; Zhang, Yi

doi:10.3390/s19030723

Cited by 12 publications

(6 citation statements)

References 30 publications

(28 reference statements)

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“…This study showcases the integration of image analysis with engine condition assessment. Feng et al proposed an algorithm for wear particle segmentation that effectively tackles the difficulties posed by reflective and light-absorbing regions in 2019 [90]. The methodology employed by the researchers entails the utilization of bilateral filtering to enhance ferrograph images of wear particles, followed by the segmentation of the enhanced images using an adaptive algorithm in order to accurately identify wear particles.…”

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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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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“…Subsequently, micrographs are captured to acquire images of the wear particle deposition spectrum, and subsequently visual characteristics are extracted [ 20 ]. The image processing technology is utilized for particle differentiation and size identification [ 21 ], facilitating the diagnosis and assessment of machine operating wear conditions [ 22 ], as well as enabling effective monitoring of machine wear conditions. The index of particle coverage area (IPCA) is employed in this process to characterize particle concentration and analyze the characteristics of large debris within the spectrum, enabling the identification of abnormal wear.…”

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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“…Offline ferrography [16][17][18][19] and online microfluidics [20,21] are common optical techniques for analyzing particles. Ferrography uses a high gradient magnetic field to separate metallic wear particles from lubricants and contaminants, to analyze the wear particle's state, size, composition, and formation mechanism.…”

Section: Introductionmentioning

confidence: 99%

An Oil Wear Particles Inline Optical Sensor Based on Motion Characteristics for Rotating Machines Condition Monitoring

Liu

Zuo

et al. 2022

Machines

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Since inline monitoring method has the advantages of no sampling, being real-time, no human intervention, and low error, this paper innovatively proposes to study the inline monitoring of wear particles in an oil pipeline, from the perspective of the different motion characteristics of the particles. In this paper, an inline optical sensor was designed and developed by studying the velocity characteristics of different particles through theoretical calculations, numerical simulations, and experimental analysis. First, an equation for particle motion was statistically established, based on the forces acting on wear particles in an oil-filled vertical tube. Then a finite element model of particle motion in a full-flow oil pipeline was created, to simulate particle motion with various diameters, densities, locations, and shapes. Finally, the results of the theoretical study were effectively applied to design an inline optical monitoring sensor, and the experimental validation results demonstrated that the inline sensor has excellent suitability for monitoring wear particles. This study has significance for the safe operation of large rotating machinery.

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A Wear Debris Segmentation Method for Direct Reflection Online Visual Ferrography

Cited by 12 publications

References 30 publications

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

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

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

An Oil Wear Particles Inline Optical Sensor Based on Motion Characteristics for Rotating Machines Condition Monitoring

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