Most of the sensors currently used for wind turbine generator system oil detection can only perform measurements of a single physical quantity, and some sensors are capable of measuring multiple contaminants but require switching modes to achieve this, making it impossible to detect various oil contaminants simultaneously. These methods not only require the addition of numerous sensors and burden the wind turbine generator system piping, but also increase the time and cost of maintenance and inspection. In addition, it may be unable to respond quickly to specific emergencies. To address these limitations, this study proposes an LC-based passive dual solenoid coil multi-contaminant oil detection sensor. An analytical expression for the magnetic field strength of the dual solenoidal coil is derived, and a corresponding mathematical model is developed. The validity of the model is verified compared it with the finite element analysis results. An experimental verification was carried out. The experimental results show that the sensor can recognize multiple oil pollutants simultaneously according to different signal curves without switching modes. The sensor can detect iron particles with diameters larger than 38 μm, copper particles with diameters larger than 100 μm, liquid droplets with diameters larger than 100-110 μm, and air bubbles with diameters larger than 180-190 μm. By comparing with the existing sensors, the LC-based passive dual-coil multi-pollutant oil detection sensor proposed in this paper has a broad application prospect in real-time monitoring of wind turbine oil conditions.

Oil is crucial for the efficient operation of mechanical equipment as it transfers energy, lubricates, and cools the system. Real-time monitoring of the mechanical system’s condition requires analyzing abrasive particles in the oil through oil detection technology. However, during detection, these particles tend to adhere together, leading to signal overlap and misjudgments resulting in losses. To address this issue, this paper proposes a resistive oil detection sensor and a wireless signal transmission circuit. The sensor detects agglomerated metal abrasive particles in the oil, while the wireless circuit transmits resistance signals from the detection coil to the receiving coil, enabling the identification of metal particles in the oil. The study explores the superposition characteristics of mixed signals generated by metal particles, examining resistance signals of individual metal particles and mixed signals produced by particles of different sizes, magnetism, and adhesion. The results demonstrate that the sensor can detect 55 μm iron particles and 138 μm copper particles in a microfluidic channel measuring 1 mm. The amplitude of the superposition signal from two particles with the same magnetism exceeds that of a single particle of the same size, and the superposition resistance signal increases with particle size. In the case of two particles with different magnetisms, the superposition resistance signal exhibits a double-peak pattern. The sensor distinguishes various types of adhered magnetic metal particles by recognizing distinct resistance signal patterns, providing valuable insights into identifying the signal characteristics of overlapping particles.

Oil is vital to the stable operation of mechanical equipment, providing energy transfer, lubrication and cooling. Analyzing abrasive particles in oil is essential for effective maintenance of equipment. In order to improve the signal transmission distance and quality of the oil detection sensor. This paper presents a novel three-coil magnetically coupled resonant sensor structure for detecting wear particles in oil. This innovative design utilizes the interaction between the magnetic field generated by the excitation coil and the magnetization and eddy current effects produced by ferromagnetic and non-ferromagnetic metallic wear particles in the detection area to generate variable inductive signals. By analyzing the characteristics of these signals, the sensor can clearly distinguish the size and type of different metal particles. This structure achieves non-contact detection of particles in oil through electromagnetic coupling between the coils, while the addition of a relay coil greatly improves the signal transmission distance and quality of the sensor. Simulation results confirm that this sensor design can greatly improve the signal transmission distance and detection quality with an optimal transmission spacing of 0.5mm. The experimental results showed that the sensor was able to detect iron particles at 73μm and copper particles at 220μm. The signal amplitude for the detection of iron particles was dramatically increased by a factor of about 133, and that of copper particles by a factor of about 29. The novel sensor structure for oil wear particle detection proposed in this study provides a promising new approach for non-contact oil detection sensors.

Friction in marine engineering machinery produces abrasive particles containing valuable information. By employing oil detection technology, we can analyze these particles to monitor and diagnose mechanical system faults. This paper introduces an inductive oil detection sensor and wireless signal transmission circuit. The sensor utilizes two opposing solenoid coils of the same specifications, with the detection coil connected to a chip capacitor to form an LC resonant unit. The designed wireless transmission circuit wirelessly transmits a sensing signal from a detection coil to a receiving coil to detect metal particles in oil. This paper deduces the sensor’s inductance principle and simulates the magnetic field distribution using finite element simulation software. Through experiments, the optimal excitation frequency, coil spacing, and oil sample flow path location were determined. The sensor successfully detected 55 μm iron particles and 138 μm copper particles in a 1 mm microfluidic channel. With its simple structure, distinct signal characteristics, and high sensitivity, the sensor is suitable for detecting metal abrasive particles in hydraulic oil, providing a new approach for wireless transmission in oil detection sensors.

By detecting the wear debris information in the lubricating oil through lubricating oil detection technology, it is possible to achieve fault early warning and health assessment of key components of machinery equipment. The magnetic plug sensor, as a typical online detection sensor, has been widely used in the field of oil liquid detection. This paper proposes a magnetic plug-type ferromagnetic particle detection sensor based on LC resonance, which can be used to detect ferromagnetic wear debris in the oil liquid of engineering equipment. The circuit transmits the resistance signal of the detection coil to the PC end, thereby achieving the detection of ferromagnetic metal particles in the oil liquid. The sensor’s optimal frequency and magnetic field distribution were analyzed using COMSOL software. The sensitivity analysis experiment of the sensor for particles at different detection positions was carried out, and the dynamic experiment was set to detect the maximum flow rate of the oil under the normal working condition of the sensor, and the aliasing wear particles of different sizes were detected. The experimental results show that the sensor can detect ferromagnetic wear debris with a minimum diameter of about 50μm under the condition of a larger coil diameter. The sensor detection unit still has a good detection effect when the wear particles are adsorbed successively, and the experimental signal can be accurately reflected on the waveform diagram. The sensor proposed in this study has a probe diameter of 13 mm, a sensitivity of 28.119μH/mg, and a resolution of up to 3.9%, which is high in sensitivity and resolution relative to the same class of sensors, and can be better adapted to a variety of working conditions due to the large enough probe diameter. In addition, the structure of the sensor is simple, which provides a new idea for the research and development of oil detection equipment.

Metal abrasive particles are inevitably generated in the lubricating oil of mechanical equipment, which is an important cause of mechanical failure. Using the oil detection technology, the size and material of the abrasive particles can be analyzed to realize the monitoring and fault diagnosis of the mechanical system status. In this paper, a microfluidic abrasive grain detection sensor based on a three-coil structure is proposed. According to the relevant theory of electromagnetism, the magnetic induction strength of the induction zone and the induction voltage formula of the induction coil are deduced. Single iron and copper particles and iron and copper attached particles passing through this sensor are simulated using COMSOL simulation software. The simulation results showed that the signal formed by the superposition of individual iron and copper particles was the same as that of an iron and copper mixture. Subsequently, a prototype device was fabricated and a signal processing circuit was constructed for metal particle detection experiments. The experimental results showed that the sensor had a signal-to-noise ratio of 2.7 for iron particles of 50 μm and 3.9 for copper particles of 275 μm. The simple structure and the outstanding signal characteristics were able to differentiate between the types of superimposed particles and to estimate the size of the particles based on their volume.