Characterization of Inductive Signals of Polymetallic Particles Under Variable Frequency Conditions

As the detection channel of an inductive particle sensor increases, it is inevitable that multiple particles will pass through the sensor simultaneously. However, the movement of multiple particles in fluid and magnetic fields, especially their aggregation behavior, can allow particle sensors to generate misleading wear signals. Therefore, to estimate the influence of the multi-particle aggregation effect on the accuracy of inductive sensors, this study constructed a magnetic coupling model of abrasive particles in strip structure and built the experimental platform required for the study, to investigate the difference in inductance change due to magnetic coupling effect among multiple metal particles at different frequencies. The experimental results reveal the phenomenon of inductance change of the three-particle combinations of the strip structure under two different conditions: in the “sequential entry” type of strip structure, the vortex effect between particles exhibits a mutual weakening, while in the “simultaneous entry” type, the vortex effect between particles exhibits a mutual enhancement. In the “simultaneous entry” type, the eddy current effects between the particles are characterized by mutual enhancement. These findings not only provide a new perspective for understanding the aggregation detection of polymetallic particles but also provide a theoretical basis for improving the accuracy of the sensor.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3557111
Citations
1
Primary Topic
Sensor Technology and Measurement Systems
Type
article
Field-Weighted Citation Impact
1.49

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article

Characterization of Inductive Signals of Polymetallic Particles Under Variable Frequency Conditions

Riwei Wang, Hongpeng Zhang, Chenyong Wang, Xiyuan Zhang et al.
1 citations
IEEE Transactions on Instrumentation and Measurement
Sensor Technology and Measurement Systems
1.49
article

Characterization of Inductive Signals of Polymetallic Particles Under Variable Frequency Conditions

Riwei Wang, Hongpeng Zhang, Chenyong Wang, Xiyuan Zhang, Chenzhao Bai, Xiangming Kan, Zhonglun Cai
article en
1 citations

Abstract

As the detection channel of an inductive particle sensor increases, it is inevitable that multiple particles will pass through the sensor simultaneously. However, the movement of multiple particles in fluid and magnetic fields, especially their aggregation behavior, can allow particle sensors to generate misleading wear signals. Therefore, to estimate the influence of the multi-particle aggregation effect on the accuracy of inductive sensors, this study constructed a magnetic coupling model of abrasive particles in strip structure and built the experimental platform required for the study, to investigate the difference in inductance change due to magnetic coupling effect among multiple metal particles at different frequencies. The experimental results reveal the phenomenon of inductance change of the three-particle combinations of the strip structure under two different conditions: in the “sequential entry” type of strip structure, the vortex effect between particles exhibits a mutual weakening, while in the “simultaneous entry” type, the vortex effect between particles exhibits a mutual enhancement. In the “simultaneous entry” type, the eddy current effects between the particles are characterized by mutual enhancement. These findings not only provide a new perspective for understanding the aggregation detection of polymetallic particles but also provide a theoretical basis for improving the accuracy of the sensor.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Dalian Maritime University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 18%
Sensor Technology and Measurement Systems
1.49
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