Design of a Three-Coil Microfluidic Lubricating Oil Detection Sensor With Multiple Signal Characteristics

Abstract

In this article, a three-coil microfluidic lubricating oil detection sensor is designed by combining microfluidic principle with three-coil inductive detection method. The sensor adopts a microchannel design to reduce the position error, improve the detection accuracy of a single particle, and reduce the occurrence of detection errors caused by multiple particles passing through at the same time. The inductive electric potential of the induction coil was obtained by a simulation analysis and experimental verification methods. It is found that the influence of particles on the internal magnetic field of the micro coil is relatively enhanced when the micro coil is used as excitation and induction coil. This results in two peaks and valleys shown from the generated inductive electric potential, increasing of the signal’s characteristic point position, and improvement in signal recognition. Therefore, it will provide more information for the subsequent material identification and multiparticle mixing signal differentiation. Meanwhile, a signal processing circuit with the function of low-pass filtering and amplification is designed to suppress the noise value of the output signal to around 1.25 mV, which could improve the stability of the system. A series of lubricating oil abrasive particle detection tests are conducted, and the results show that the new detection sensor is able to detect iron particles below 64 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and copper particles below 125 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> . The abrasive particle information collected by this sensor can provide more precise wear status information for the health assessment and fault diagnosis of mechanical systems, which is important for the health monitoring of mechanical systems.