Metallic contaminants (solid) are generated by friction pair, causing wear of equipment by enters the lubricating system. This poses a great potential threat to the normal operation of such machines. The timely analysis and detection of debris can lead to the avoidance of mechanical failures. Abnormal wear in machinery may produce debris exceeding 10 μm. The traditional inductance detection method has low sensitivity and cannot meet the actual detection requirements. To boost the sensitivity of the inductance sensor, the mutual inductance of coils and the strong magnetic conductivity of permalloy was utilized to design a high sensitivity inductance sensor for the detection of debris in lubricating oil. This design was able to detect 10–15 μm iron particles and 65–70 μm copper particles in the oil. The experimental results illustrate that low-frequency excitation is the best for detecting ferromagnetic particles, while high-frequency excitation has the best effect for detecting non-ferromagnetic particles. This paper demonstrates the significant advantages of coil mutual inductance, and strong magnetic conductivity of permalloy in improving the detection sensitivity of oil debris sensors. This will provide technical support for wear detection in mechanical equipment and fault diagnosis.

An inductive oil pollutant detection sensor based on a high-gradient magnetic field structure is designed in this paper, which is mainly used for online detection and fault analysis of pollutants in hydraulic and lubricating oil systems. The innovation of the sensor is based on the inductance detection method. Permalloy is embedded in the sensing region of the sensor, so that the detection area generates a high gradient magnetic field to enhance the detection accuracy of the sensor. Compared with traditional inductive sensors, the sensor has a significant improvement in detection accuracy, and the addition of permalloy greatly improves the stability of the sensor’s detection unit structure. The article theoretically analyzes the working principle of the sensor, optimizes the design parameters and structure of the sensor through simulation, determines the best permalloy parameters, and establishes an experimental system for verification. Experimental results show that when a piece of permalloy is added to the sensing unit, the signal-to-noise ratio (SNR) of iron particles is increased by more than 20%, and the signal-to-noise ratio of copper particles is increased by more than 70%. When two pieces of permalloy are added, the signal-to-noise ratio for iron particles is increased by more than 70%, and the SNR for copper particles is increased several times. This method raises the lower limit of detection for ferromagnetic metal particles to 20 μm, and the lower limit for detection of non-ferromagnetic metal particles to 80 μm, which is the higher detection accuracy of the planar coil sensors. This paper provides a new and faster online method for pollutant detection in oil, which is of great significance for diagnosing and monitoring the health of oil in mechanical systems.

The uneven radial magnetic field distribution in the planar coil will lead to debris detection errors, especially for inductive debris sensors with high throughput and sensitivity, so the effect of debris position on the detection sensitivity should be addressed. In this study, based on spatial magnetic field analysis of planar coil, the characteristics of impedance signals induced by debris at different positions in planar coil were investigated under different frequencies, debris sizes and velocities. The experimental results demonstrated that the double peaks generated in the induced signals can be eliminated without sacrificing the sensitivity by placing the sensor channel at the coil inner wall, and the sensitivity can be improved by observing resistance signals, increasing the frequencies and reducing debris velocities.

Inductive debris sensors can distinguish, estimate, and count debris in oil in a nonembedded manner, becoming one of the most promising approaches for health monitoring of mechanical equipment in the marine and ocean industry. In this study, a method for estimating the composition and size of the debris based on the joint observation of inductance and resistance signals was proposed, and a model for calculating the inductance and resistance increment induced by a single debris was procured accordingly. The relationships between impedance increments and excitation frequencies and between impedance increments and debris sizes were investigated, and the theoretical results were in good agreement with the experimental results. The sensitivity of coil parameters on the induced impedance increment by the debris was also analyzed and discussed in the end.

Technologies for particle detection in lubricating or hydraulic oil are essential in mechanical industries to avoid constant machinery failure. The existing technologies utilize offline or online methods. Some of the online methods either have weak sensitivity or can only detect one type of particle. This article presents a highly sensitive, multi-parameter sensor for detecting various particles in hydraulic oil. This design takes advantage of high magnetic permeability and shielding efficiency of permalloy, placed behind dual opposite planar coils with a straight microchannel structure to improve the sensitivity of the sensor. The design includes both an inductive mode sensor, which can detect and distinguish ferromagnetic and nonferromagnetic metallic particles, and a capacitive mode sensor, which can detect and distinguish air bubbles and water droplets. Comparative COMSOL simulation and experiment were used to investigate the effect of permalloy and the results show that the sensor with permalloy has higher sensitivity compared to the sensor without permalloy. The sensor with permalloy successfully detected 15- $\mu \text{m}$ iron particle, 55- $\mu \text{m}$ copper particle, 75- $\mu \text{m}$ water droplet, and 95- $\mu \text{m}$ air bubble. This research shows the application of permalloy in the sensitivity improvement of microfluidic sensors.

A method for analyzing the influence of velocity changes on metal signals of different materials in oil detection technology is proposed. The flow rate of metal contaminants in the oil will have a certain impact on the sensitivity of the output particle signal in terms of electromagnetic fields and circuits. The detection velocity is not only related to the sensitivity of the output particle signal, but also to the adaptability of high-speed and high-throughput in oil online monitoring. In this paper, based on a high-sensitivity inductive bridge, the eddy current effect of velocity in a time-harmonic magnetic field is theoretically analyzed and experimentally verified, the phenomenon of particle signal variation with velocity for different materials is analyzed and discussed, and finally the effect of velocity on the output signal of the processing circuit is also elaborated and experimentally verified. Experiments show that under the influence of the time-harmonic magnetic field, the increase of the velocity enhances the detection sensitivity of non-ferromagnetic metal particles and weakens the detection sensitivity of non-ferromagnetic particles. Under the influence of the processing circuit, different velocities will produce different signal gains, which will affect the stability of the signal at different velocities.