In situ collection and analysis of oil debris based on multi-physical field synthesis effect

Gao, Tian; Han, Zhibin; Wu, Diheng; Li, Yuli; Wang, Yishou

doi:10.1080/02533839.2019.1708803

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The lubrication state and wear condition can be evaluated by the quantity and size of the debris. The wear is more significant when the number and size of wear debris are large, as shown in Figure 11 [72][73][74]. Detection methods of debris in in-service lubricating oil can be divided into offline and online methods.…”

Section: Detection Of Wear Debris In Oilmentioning

confidence: 99%

Overview of Friction and Wear Performance of Sliding Bearings

et al. 2022

Coatings

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Sliding bearings are critical components of the internal combustion engine. Friction and wear occur in the contact area between the shaft and the bearing. Significant wear can occur in poor working conditions or after a long service time, leading to the failure of the sliding bearing and affecting the reliability of the machinery. It is essential to investigate the wear performance of sliding bearings, understand their wear mechanism, predict their service life, and select wear-resistant materials and surface treatments. This paper reviews the current status and prospects of sliding bearing wear research, focusing on the classification of sliding bearing wear tests, wear testing machines, wear test research, wear prediction models, and future research prospects.

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Section: Detection Of Wear Debris In Oilmentioning

confidence: 99%

Overview of Friction and Wear Performance of Sliding Bearings

et al. 2022

Coatings

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“…The sensor plug employs a differential method to make the sensor insensitive to parameters such as the variation in temperature and viscosity of oil, and the ferrous debris can also be collected by the sensor; however, the disadvantage of the sensor is that it cannot detect non-ferrous debris. Our team previously proposed a coaxial capacitive sensor and illustrated its characteristics through preliminary validation experiments [10,11,17]. Actually, in the previous work, ref.…”

Section: Introductionmentioning

confidence: 99%

A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil

Wang

Lin

et al. 2022

Sensors

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Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network.

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“…The size of the wear particles that can be monitored is limited; in addition, the monitoring efficiency is limited. The resistance type is easily disturbed by external factors such as temperature; friction generates energy to charge the wear particles, and the electrical charge monitors the oil wear particles by monitoring the charge of At present, the detection of lubricant wear particles mainly includes offline and online methods [2]. Offline methods mainly include ferrography [3] and spectroscopy [4], where lubricating oil samples are collected periodically and sent to the laboratory for testing.…”

Section: Introductionmentioning

confidence: 99%

“…The innovation of the proposed method mainly includes the following three points: (1) the multi-dimensional information measured by coaxial networked sensors is used to classify the wear particles. (2) The response signals of a large number of different shapes of abrasive particles through the sensor are simulated to construct the data set required for machine learning. (3) The support vector machine algorithm is improved to more accurately classify wear particle morphology.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Classification of Lubricating Oil Wear Particle Morphology Based on Coaxial Capacitive Sensing Network and SVM

Zhu

Xiangwen

et al. 2022

Sensors

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In addition to lubricating and cooling, aero-engine lubricating oil is also a transport medium for wear particles generated by mechanical wear. Online identification of the number and shape of wear particles is an important means to directly determine the wear state of rotating parts, but most of the existing research focuses on the identification and counting of wear particles. In this paper, a qualitative classification method of wear particle morphology based on support vector machine is proposed by using the wear particle capacitance signal obtained by the coaxial capacitive sensing network. Firstly, the coaxial capacitive sensing network simulation model is used to obtain the capacitance signals of different shapes of wear particles entering the detection space of different electrode plates. In addition, a variety of intelligent optimization algorithms are used to optimize the relevant parameters of the support vector machine (SVM) model in order to improve the classification accuracy. By using the processed data and optimized parameters, a SVM-based qualitative classification model for wear particles is established. Finally, the validity of the classification model is verified by real wear particles of different sizes. The simulation and experimental results show that the qualitative classification of different wear particle morphologies can be achieved by using the coaxial capacitive sensing network signal and the SVM model.

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In situ collection and analysis of oil debris based on multi-physical field synthesis effect

Cited by 6 publications

References 16 publications

Overview of Friction and Wear Performance of Sliding Bearings

Overview of Friction and Wear Performance of Sliding Bearings

A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil

Qualitative Classification of Lubricating Oil Wear Particle Morphology Based on Coaxial Capacitive Sensing Network and SVM

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