Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection

Sun, Yanjing; Jia, Lin; Zeng, Zhoumo

doi:10.3390/s19030515

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…The non-contact capacitive sensor described in this study is designed to preserve electrode integrity and achieve a high level of accuracy, with a tolerance of up to ±0.82%. Sun et al developed a microsensor that integrates a 12-plate capacitance array, employing adaptive cytogenetics (SA-CGA) and morphological algorithms, in 2019 [73]. They have also proposed a novel inversion method that utilizes hyper-heuristic partial differential equations to effectively identify multiscale metal abrasive particles.…”

Section: Capacitive 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: Capacitive 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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“…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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“…Acoustic sensors have high detection accuracy, but the installation location of the sensor, the selection of ultrasonic frequency, and the effects of vibration and noise will seriously affect the detection performance [12]. Capacitive sensor can detect water droplets and bubbles in oil by changing the dielectric constant, but they cannot discriminate the magnetic properties of metal wear debris in oil [13]. The inductive sensor is based on the magnetic anomaly change of the •3• inspection coil caused by the scattered magnetic field of the wear debris under the magnetic excitation condition, and uses the induction signals with different trends to characterize the metal wear debris of different magnetic properties, and is not affected by the clarity of the oil [14], based on the above advantages, this kind of detection method has been highly valued in the field of metal wear debris detection.…”

Section: Introductionmentioning

confidence: 99%

Online Detection System of Metal Wear Debris Based on Symmetrical Magnetic Excitation Balance Magnetic Field Model

Bai

et al. 2022

Preprint

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The metal wear debris generated by wear during the operation of the mechanical equipment can reflect the fault status of the equipment, and the online monitoring of the metal wear debris in the oil can timely understand the operation information of the equipment. Aiming at the effective response of metal wear debris under symmetrical magnetic excitation, an online monitoring system for metal wear debris with dual-coil balanced magnetic excitation was developed. Based on the spatial magnetic field distribution of the current-carrying element, an analytical model of the magnetic field intensity distribution of a single-turn current-carrying ring is designed, and it is optimized as a single set of coils made of a current-carrying ring, and the coupling of the magnetic excitation of the symmetrical double coil is deduced. The magnetic field distribution model is simulated and analyzed by numerical method. The flow channel of the system is 8 mm. After the semi-physical simulation test with the wear debris speed of 3.03-5.25 m/s, the sensor can realize the detection of 100 μm ferromagnetic metal and 1000 μm non-ferromagnetic metal. The experiment verifies the correctness of the simulation model of the coupled magnetic field distribution. This research provides technical support for the detection of hydraulic oil contaminants, which is of great significance for preventing hydraulic system failures and extending the working life of hydraulic equipment.

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Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection

Cited by 15 publications

References 24 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

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Online Detection System of Metal Wear Debris Based on Symmetrical Magnetic Excitation Balance Magnetic Field Model

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