4.7 Article

Multi-Channel Dual-Mode Oil Multi-Pollutant Detection Sensor

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MDPI
DOI: 10.3390/jmse11101938

关键词

sensor; multi-pollutant; inductive-capacitive mode; online monitoring; fault diagnosis

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This study proposes and fabricates a multi-channel, dual-mode oil multi-pollution detection sensor for the lubricant fluid condition monitoring of ships and offshore engineering equipment. The sensor has three detection channels connected via tee tubes and uses inductive and capacitive modes for detection. Compared to traditional sensors, this sensor can distinguish and identify a wide range of pollutants and has significantly improved detection efficiency. Experimental results demonstrate that the sensor can simultaneously detect metallic and non-metallic contaminants in lubricating oil fluids.
In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, inductive and capacitive, respectively. In comparison to the traditional sensor, this sensor not only has the ability to distinguish and identify a diverse range of pollutants, but it also experiences an 11-fold increase in its volume of flow, resulting in a significant enhancement in detection efficiency. The mechanism of the inductive and capacitive modes for the differentiated detection of multiple pollutants is elucidated through theoretical analysis. The performance of the sensor is investigated using the constructed experiment platform. The experimental results show that the sensor can realize the simultaneous detection of metallic and non-metallic contaminants in lubricating oil fluids. It can detect the smallest iron particle size of 54 mu m, the smallest copper particle size of 90 mu m, the smallest water droplet size of 116 mu m, and the smallest air bubble size of 130 mu m. A novel approach for achieving ship and marine engineering equipment health monitoring and fault diagnosis is presented in this study.

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