Characterization of Multiabrasive Mixed Signals From LC Resonance-Based Dual-Coil Oil Detection Sensors

Le résumé fourni par la source

Oil is crucial for the efficient operation of mechanical equipment as it transfers energy, lubricates, and cools the system. Real-time monitoring of the mechanical system’s condition requires analyzing abrasive particles in the oil through oil detection technology. However, during detection, these particles tend to adhere together, leading to signal overlap and misjudgments resulting in losses. To address this issue, this paper proposes a resistive oil detection sensor and a wireless signal transmission circuit. The sensor detects agglomerated metal abrasive particles in the oil, while the wireless circuit transmits resistance signals from the detection coil to the receiving coil, enabling the identification of metal particles in the oil. The study explores the superposition characteristics of mixed signals generated by metal particles, examining resistance signals of individual metal particles and mixed signals produced by particles of different sizes, magnetism, and adhesion. The results demonstrate that the sensor can detect 55 μm iron particles and 138 μm copper particles in a microfluidic channel measuring 1 mm. The amplitude of the superposition signal from two particles with the same magnetism exceeds that of a single particle of the same size, and the superposition resistance signal increases with particle size. In the case of two particles with different magnetisms, the superposition resistance signal exhibits a double-peak pattern. The sensor distinguishes various types of adhered magnetic metal particles by recognizing distinct resistance signal patterns, providing valuable insights into identifying the signal characteristics of overlapping particles.

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