An impedance debris sensor capable of simultaneously detecting inductance and resistance parameters is presented, which mainly consists of two planar coils in parallel, two excitation silicon steel strips inserted in the inner hole of coils and a built-in silicon steel strip located in the center of channel. The excitation silicon steel strips and the built-in silicon steel strip establish a detection region with a high-gradient magnetic field to improve the sensitivity of the sensor. And the square channel makes full use of the sensitive detection region, thereby further increasing the detection throughput. Experiments have shown that the amplitudes of the inductance and resistance pulses generated by the same particle many times through the detection region are different. The uncertainty of the sensor can be reduced for an accurate size information by combining the detection results of the inductance and resistance parameters. By comparing and analyzing the inductance parameter detection result and the resistance parameter detection result, the impedance debris sensor can distinguish and measure 25μm iron particle and 85μm copper particle. With its high sensitivity, high throughput and capability of multi-parameter detection, this sensor can potentially be used for health monitoring of hydraulic equipment.

Inductive particle sensors are widely used in online wear condition monitoring. Understanding the behavior of a particle magnetized in an alternating current (ac) magnetic field and the associated sensing mechanism is fundamental to identifying the particle size, improving the detection precision and enhancing the sensor performance. Here, we first establish a particle magnetization model related to particle conductivity, permeability, diameter, and field frequency. Next, an expression for particle detection using inductance is derived that consists of a product of the particle volume, complex particle susceptibility, and particle sensor sensitivity. The derived formulas and experiments reveal that the output of the inductive sensor is a complex number. Therefore, a vector analysis method is proposed for inductive particle detection. The sensitivities of various sensors are discussed. Experiments are conducted using milliparticles and microparticles in various positions relative to sensors. A comparison of the calculation results with the experimental results exhibits satisfactory agreement. Consequently, an optimized rectangular cross-section of coils is proposed. This study provides a theoretical basis for the design and optimization of particle sensors based on magnetic principles.

A high sensitivity micro impedance sensor is presented for oil condition monitoring. The sensor consists of a microfluidic chip and a sensing unit. The sensing unit contains two solenoid coils and eight silicon steel tips, which can generate a highly focusing magnetic field. The micro impedance sensor can not only distinguish between ferromagnetic and non-ferromagnetic metal particles, but also water droplets and air bubbles in oil. The simulation results show that the magnetic induction is significantly improved due to the silicon steel tips, and the electric field intensity is also slightly enhanced. In the inductance experiments, the sensitivity is extremely improved based on magnetic focusing by silicon steel tips, iron particles with diameters down to 18μ m and copper particles with diameters down to 75 μm have been successfully detected by using the simple high sensitivity micro impedance sensor. In the capacitance experiments, the signal to noise ratio (SNR) of the micro impedance sensor with silicon steel tips is slightly improved; water droplets with diameters down to 100 μm and air bubbles with diameters down to 200 μm have been detected. The high sensitivity micro impedance sensor has been demonstrated to achieve the precise measurement of particle sizes and the accurate counting of microparticles.

A novel impedance sensor based on a microfluidic chip is presented. The sensor consists of two single-layer coils and a straight micro-channel, and can detect, not only ferromagnetic and non-ferromagnetic particles in oil as an inductive sensor, but also, water droplets and air bubbles in oil as a capacitive sensor. The experiments are carried out at different excitation frequencies, number of coil turns and particle sizes. For the inductance detection, the inductance signals are found to increase with the excitation frequency and the noise is constant; both the inductance signals and the noise increase with the number of coil turns, but because the noise increases at a faster rate than the signal, the signal-to-noise ratio decreases with the number of coil turns. We demonstrate the successful detection of 40 μm iron particles and 110 μm copper particles using the coil with 20 turns at the excitation frequency of 2 MHz. For the capacitance detection, capacitance signals decrease with the excitation frequency and the noise is constant; the capacitance signals decrease with the number of coil turns, while the noise increases, thus, the signal-to-noise ratio decreases with the number of coil turns. We can detect 100 μm water droplets and 180 μm bubbles successfully using the coil with 20 turns at the excitation frequency of 0.3 MHz.

The wear debris in hydraulic oil or lubricating oil has a wealth of equipment operating information, which is an important basis for large mechanical equipment detection and fault diagnosis. Based on traditional inductive oil detection technology, magnetic nanoparticles are exploited in this paper. A new inductive oil detection sensor is designed based on the characteristics of magnetic nanoparticles. The sensor improves detection sensitivity based on distinguishing between ferromagnetic and non-ferromagnetic wear debris. Magnetic nanoparticles increase the internal magnetic field strength of the solenoid coil and the stability of the internal magnetic field of the solenoid coil. During the experiment, the optimal position of the sensor microchannel was first determined, then the effect of the magnetic nanoparticles on the sensor’s detection was confirmed, and finally the concentration ratio of the mixture was determined. The experimental results show that the inductive oil detection sensor made of magnetic nanoparticle material had a higher detection effect, and the signal-to-noise ratio (SNR) of 20–70 μm ferromagnetic particles was increased by 20%–25%. The detection signal-to-noise ratio (SNR) of 80–130 μm non-ferromagnetic particles was increased by 16%–20%. The application of magnetic nanoparticles is a new method in the field of oil detection, which is of great significance for fault diagnosis and the life prediction of hydraulic systems.