The Optimization of Parallel Resonance Circuit for Wear Debris Detection by Adjusting Capacitance

Abstract

Wear debris in lubrication oil provides important information for marine engine condition monitoring and faults diagnosis. Inductive sensors have been widely used to detect wear debris in lubrication oil. To improve the sensitivity, the inductive coil is always connected with a capacitor in parallel to form parallel LC resonance-sensing circuit. A previous study optimized the parallel resonance circuit by adjusting the excitation frequency. However, multiple parameters (namely, excitation signal, signal detection circuits, and signal-processing program, etc.) need to be adjusted accordingly for a series of the testing frequencies. To simplify the optimization, we propose a method based on adjusting the parallel capacitance in this work. The impedance (inductance and internal resistance) of the sensing coil and its variation induced by particles are first measured, which are the necessary parameters for establishing the function relationship between the parallel capacitance and the relative impedance variation. With the function relationship, the relative impedance variation is calculated directly, and the optimal capacitance is located at the highest absolute value of it. The experimental results for the ferrous and nonferrous particles match the calculation results well. Interestingly, the optimal capacitance for the nonferrous particle was larger than that of the ferrous particle. We speculate that the difference is generated due to the increased resistance induced by the eddy current effect.

Particle Counting Methods Based on Microfluidic Devices

“…Analogous to a balanced scale, where a slight weight change can tip the balance, even a minor impedance variation can lead to a substantial phase shift in the current passing through the resonator, resulting in heightened sensitivity. Liu et al [ 114 ] proposed a parallel resonance circuit optimization technique by adjusting capacitance, which was used to improve the sensitivity of detecting wear debris in lubrication oil, as shown in Figure 9 b. Based on the experimental data, a functional relationship between the parallel capacitance and the relative impedance variation is established.…”

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

“…The conducted experiments involved the Liu et al conducted a study on the optimization of parallel resonant circuits in capacitive abrasive sensors. The researchers employed a method that involved adjusting the parallel capacitance to enhance the performance of the sensors in 2022 [34]. The evaluation of the detection quality was conducted through the measurement of the relative impedance change of the LC resonant circuit.…”

“…By detecting wear in its incipient stages, these sensors help prevent costly and potentially catastrophic failures of critical ship equipment, ensuring the safety of the vessel and its crew [33]. These sensors provide data and insights into wear patterns, allowing for data-driven decision-making regarding maintenance schedules, component replacements, and system upgrades [34]. Timely detection and maintenance can prolong the lifespan of ship equipment, reducing the need for costly replacements and improving the overall efficiency of the vessel [35,36].…”

Numerical Approach and Verification Method for Improving the Sensitivity of Ferrous Particle Sensors with a Permanent Magnet

“…These particles usually contain ferrous, non-ferrous, and non-metal debris such as ceramics and polymers. To measure wear particles, wear particle sensors commonly utilize inductance- and capacitance-based methods [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], acoustic methods based on ultrasonic transducers [ 20 , 21 , 22 ], optical methods [ 23 , 24 , 25 , 26 ], magnetic methods [ 27 , 28 ], and a method based on a combination of a permanent magnet and inductance [ 29 ]. Among them, ferrous particle sensors are widely used to diagnose machine condition because machines are made of iron as their main component.…”