In the field of marine engineering, the friction and wear experienced by rotating mechanisms are recognized as significant contributors to the failure of marine machinery. In order to enhance the safety and dependability of marine ship operations, the implementation of on-line oil wear debris particle detection sensors enables the on-line monitoring of oil and facilitates the rapid identification of abnormal wear locations. This paper provides a critical review of the recent research progress and development trends in the field of sensors for on-line detection of oil wear debris particles. According to the method of sensor detection, wear debris particle detection sensors can be classified into two distinct categories: electrical and non-electrical sensors. Electrical sensors encompass a range of types, including inductive, capacitive, and resistive sensors. Non-electrical sensors encompass a range of technologies, such as image processing sensors, optical sensors, and ultrasonic sensors. Finally, this review addresses the future research directions for wear debris particle detection sensors in light of the challenging problems currently faced by these sensors.

Micromixers, as crucial components of microfluidic devices, find widespread applications in the field of biochemistry. Due to the laminar flow in microchannels, mixing is challenging, and it significantly impacts the efficiency of rapid reactions. In this study, numerical simulations of four baffle micromixer structures were carried out at different Reynolds numbers (Re = 0.1, Re = 1, Re = 10, and Re = 100) in order to investigate the flow characteristics and mixing mechanism under different structures and optimize the micromixer by varying the vertical displacement of the baffle, the rotation angle, the horizontal spacing, and the number of baffle, and by taking into account the mixing intensity and pressure drop. The results indicated that the optimal mixing efficiency was achieved when the baffle’s vertical displacement was 90 μm, the baffle angle was 60°, the horizontal spacing was 130 μm, and there were 20 sets of baffles. At Re = 0.1, the mixing efficiency reached 99.4%, and, as Re increased, the mixing efficiency showed a trend of, first, decreasing and then increasing. At Re = 100, the mixing efficiency was 97.2%. Through simulation analysis of the mixing process, the structure of the baffle-type micromixer was effectively improved, contributing to enhanced fluid mixing efficiency and reaction speed.

Oil wear particle can offer wear condition for marine machinery. The shape, size and composition of wear particle can supply important information for diagnosing the type and severity of marine machinery faults. Through oil wear particle detection, potential marine machinery fault diagnosis can be found early, thereby reducing the risk of marine machinery failure diagnosis and improving the reliability and service life of marine machinery. This review summarizes the detection methods, technical characteristics and research progress of oil wear particle detection sensors applied in marine machinery condition monitoring. Oil wear particle detection sensors are divided into impedance-based particle detection method and non-impedance-based particle detection method. Impedance-based method includes inductance, capacitance and resistance principles. Non-impedance-based method includes image processing, optical and acoustic principles. Finally, it points out the future development trends and novel ideas for the oil wear particle detection sensors employed in marine machinery.

The presence of moisture and air in hydraulic oil will seriously affect the reliability of machines. This paper proposes a new cross-capacitive oil pollution detection sensor, which is based on the Thompson and Lampard theorem. The sensing unit consists of four identical copper electrodes with infinitesimally small gaps. The sensor can effectively distinguish water droplets and air bubble pollutants mixed in the oil through the pulse direction of the signal. Compared with traditional capacitive sensors, the sensor has a significant improvement in detection accuracy and detection throughput. In this paper, the relationship between the cross-capacitance value with the dielectric constant and the frequency in an alternating electric field was deduced, and the best excitation frequency was chosen as 1.9 MHz. Experiments show that the sensor can effectively detect water droplets of 140–160 µm and bubbles of 170–190 µm and has good linearity for detecting water droplets and air bubbles of different sizes. The sensor provides a new method for machine condition monitoring of hydraulic systems.

Intelligent operation and maintenance technology for vessels can ensure the safety of the entire system, especially for the development of intelligent and unmanned marine technology. The material properties of metal abrasive particles in oil could demonstrate the wear areas of the marine mechanical system because different components consist of different materials. However, most sensors can only roughly separate metallic contaminants into ferromagnetic and non-ferromagnetic particles but cannot differentiate them in greater detail. A micro-three-coil sensor is designed in this paper; the device applies different excitation signals to two excitation coils to differentiate materials, based on the different effects of different material particles in the asymmetric magnetic field. Therefore, a particle’s material can be judged by the shape of the induction electromotive force output signal from the induction coil, while the particle size can be judged by the amplitude of the signal. Experimental results show that the material differentiation of four different types of particles can be achieved, namely, of aluminum, iron, 304 stainless steel, and carbon steel. This newly designed sensor provides a new research prospect for the realization of an inductive detection method to distinguish non-ferrous metals and a reference for the subsequent detection of metal contaminants in oil and other liquids.

This study proposes a vertical microfluidic channel oil viscosity measurement method based on the falling ball method and inductance detection, which aims to simultaneously detect oil particle contaminants and viscosity. Through microfluidic inductance detection technology, this measurement method can measure the sedimentation velocity of metal particles in the oil, and then derive the oil viscosity. We analyzed the particle sedimentation process through numerical simulation and experiments. In the numerical study, particles of aluminum, iron, and copper were considered; the experimental validation was conducted using iron particles. The experimental and numerical results show that the sedimentation velocity of the 100 μm aluminum particle (the smallest density and size) is 0.98 mm s ⁻¹ , while that of the 400 μm copper particle (the largest density and size) is 7.27 mm s ⁻¹ in the simulation. In the experiments with iron particles, when the oil viscosity increases from 25.12 mm ² s ⁻¹ to 105.1 mm ² s ⁻¹ , the sedimentation velocity shows a decreasing trend. This study verifies the feasibility of deducing oil viscosity from the sedimentation velocity of particles.