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.

Lubricant is a critical component of a ship’s power plant, and analyzing the information on the change of lubricant viscosity and the wear particle it carries is of great significance to realize the condition monitoring of the power plant. In this paper, we propose and design an inductive sensor capable of simultaneously detecting the viscosity, particle diameter, and velocity of lubricants. COMSOL was used to investigate the magnetic field, particle flow, and particle velocity characteristics. The inductance signal amplitude and velocity values of spherical iron particles of eight diameters passing through four levels of viscosity lubricants were collected. Based on this data, we plotted the particle diameter-inductance signal amplitude curves, viscosity-velocity (for different diameters), and diameter-velocity (for different viscosities) curves. According to the experiment, when the two unknown iron particles passed through the standard oil sample of 50 mm ² /s (20°C), the corresponding particle diameter can be obtained from the particle diameter-inductance amplitude curves as 300 μm and 278 μm, respectively. For the calibrated particle with a diameter of 300 μm, the viscosity-velocity (for different diameters) curve yielded the viscosity value of 53.382mm ² /s with an error of 6.76%. The estimated viscosity value of 54.73 mm ² /s was obtained by diameter-velocity (for different viscosity) curve for the non-calibrated particle of 278 μm, with an error of 9.46%. This detection technology provides a new research idea for marine lubricant testing.

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.

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.

The operating environment of large mechanical equipment on ships is extremely harsh. Under such harsh conditions, it is necessary to effectively monitor and assess the health status of machinery and equipment and to take appropriate maintenance measures to ensure the normal operation of the ship and the safety of the lives and property of the crew. However, currently used methods are less effective in detecting non-ferromagnetic abrasive particles and non-metallic contaminants and may not be able to respond to certain emergencies promptly. Therefore, in this paper, a quad-solenoid coil multi-contaminant oil detection sensor is proposed to detect metallic abrasive particles and non-metallic contaminants using the voltage–capacitance dual mode. We provide an analytical expression for the magnetic field strength of the present sensor and develop a corresponding mathematical model. In order to verify its accuracy, we compared the model results with finite element analysis and verified them experimentally. Analysis of the experimental results shows that by switching the detection mode of the sensor, ferromagnetic metal particles, non-ferromagnetic metal particles, and non-metallic contaminants in the oil can be identified according to the different experimental signal curves. The sensor recognizes ferromagnetic particles over 70 μm in diameter, non-ferromagnetic particles over 220 μm in diameter, water droplets over 100–110 μm in diameter, and air bubbles over 180–190 μm in diameter. By comparing the sensor with existing sensors, the sensor can provide accurate information about various pollutants, help maintenance personnel to develop a reasonable maintenance program, and reduce the maintenance cost of ship machinery.