Model of a Light Extinction Sensor for Assessing Wear Particle Distribution in a Lubricated Oil System

Krogsøe, Kevin; Henneberg, Morten; Eriksen, René Lynge

doi:10.3390/s18124091

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…High sensitivity, fast detection speed, and particle morphology analysis High cost, easily affected by oil discoloration and particle aggregation and unable to distinguish particle properties [92][93][94][95][96]…”

Section: Optical Sensor Nomentioning

confidence: 99%

“…The researchers examined the impact of various factors, including the optical system design, particle concentration, and measurement noise, on the performance of the sensor. These investigations are illustrated in Figure 8 [92]. Simulations conducted on the sensor demonstrated that, in optimal circumstances, the correlation between the sensor aperture and the particle could be utilized to estimate the probability of effectively capturing the particle sample.…”

Section: Optical Sensormentioning

confidence: 99%

See 1 more Smart Citation

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: Optical Sensor Nomentioning

confidence: 99%

Section: Optical Sensormentioning

confidence: 99%

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

Han,

Mu,

Liu

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to monitor the condition of the oil. There are many methods for monitoring and analyzing lubricating oil pollutants, which include; acoustic wave technology [6][7][8], optical technology [9,10], capacitance technology [11], and inductive technology [12][13][14]. In the development process of the above technical methods, the inductance method has high flexibility, and strong robustness compared to other methods and has been widely studied in recent years.…”

Section: Jinst 17 P06017mentioning

confidence: 99%

Design and experimental research of abrasive particle detection sensor based on coil magnetic field

Gu¹,

Bai²,

Cheng³

et al. 2022

J. Inst.

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This paper develops a dual-coil sensor integrated with high-permeability materials based on inductive sensor technology to detect wear particles in oil. This method is mainly used for online detection and fault analysis of pollutants in hydraulic and lubricating oil systems. The sensor innovatively embeds permalloy into the sensing unit of the sensor to generate high gradient magnetic field in the sensing area, consequently improving the detection sensitivity of the sensor. The detection unit consists of two pieces of permalloy and two plane coils. The permalloys have a rectangular groove at the center, which is placed in line with the inner hole of the coil, thereby forming the detection area. Particles in the microchannel can be detected as they flow through the detection area. The article theoretically analyzes the working principle of the sensor and establishes a verification experiment system. The experimental results show that after adding permalloy to the sensing unit, the signal-to-noise ratio (SNR) of iron particles is increased by more than 40%, and the SNR of copper particles is increased by more than 30%. As the particle size increases, the SNR decreases. Using this design, the range of the lower limit detection for ferromagnetic metal particles increased to 10–15 μm, while that of non-ferromagnetic metal particles increased to 60 μm. Compared with traditional inductive sensors, the addition of permalloy greatly improves the sensor's performance, which significantly boosts the sensitivity of the dual-coil type sensor.

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“…Recently, online oil debris monitoring [ 5 , 6 , 7 ] has become more and more common. It involves installing magnetic [ 8 , 9 , 10 ], capacitive [ 11 , 12 ], optical [ 13 , 14 , 15 ], or acoustic sensors [ 16 ] in the engine. These sensors can count particles or measure their diameters, but most of them are unable to identify material or detect fine wear debris.…”

Section: Introductionmentioning

confidence: 99%

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Deliś

Kłysz

Przysowa

2023

Sensors

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Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

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Model of a Light Extinction Sensor for Assessing Wear Particle Distribution in a Lubricated Oil System

Cited by 15 publications

References 14 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

Design and experimental research of abrasive particle detection sensor based on coil magnetic field

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

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