Diesel engines in marine power systems often work in extreme environments. Oil monitoring technology can guarantee the operational safety of diesel engines. In this paper, a magnetic plug sensor for oil debris monitoring is proposed to improve sensitivity and accuracy. Through finite element analysis, absolute deviation is reduced by optimizing the sensor structure. A bridge nonlinear correction circuit is designed to make sensitivity consistent over the entire scale range, which can facilitate calibration and data processing. In order to reduce noise and amplify the signal effectively, a signal post-processing circuit is adopted as well, which consists of a first stage filter circuit, a second stage filter, an active filter module, and an instrumentation amplifier. Therefore, this magnetic plug sensor exhibits better sensitivity and accuracy. Furthermore, a void test and a dynamic test are carried out to investigate its performance. There is a linear relationship between the voltage and the particle mass for the sensor with a bridge nonlinear correction circuit. The results illustrate a minimum of 0.033 mg iron debris with a 1.647 signal-to-noise ratio. Additionally, it can capture and detect 47 μm particles with a debris capture rate of over 90%, which allows it to excel in early fault diagnosis as well.

Oil is rich in information about the operating status of machinery and equipment, and oil condition monitoring is essential for maintaining the safety and reliability of machinery and equipment. Most sensors not only have the problem of small detection throughput and low detection efficiency, but also large-sized contaminants can easily block or even scratch the detection channel. A sensor with high sensitivity and high throughput for detecting multiple oil contamination is designed in this article. The sensor comprises four glass capillary detection channels and four detection units, including a planar coil, solenoid coil, cylindrical parallel plate capacitor, and rectangular parallel plate capacitor. Detection flux of the sensor is increased by 12 times its initial value, which significantly improves the detection efficiency. Through experimental research, in the inductance mode, the best excitation frequency is 2 MHz, and the voltage is 2 V. In the capacitance mode, the best excitation frequency is 1.8 MHz, and the voltage is 2 V. In addition, the sensor has a higher detection capability for Fe than Cu in the inductance mode, and a higher detection capability for water droplets than air bubbles in the capacitance mode. By analyzing the signal characteristics of each sensor unit, the sensor can distinguish between Fe of 38– $900 ~\mu \text{m}$ and Cu of 88– $900 ~\mu \text{m}$ , as well as water droplets greater than $130 ~\mu \text{m}$ and air bubbles greater than $100 ~\mu \text{m}$ . This research provides a new idea for online oil monitoring.

In this paper, a three-coil microfluidic lubricating oil detection sensor is designed by combining microfluidic principle with three-coil inductive detection method. The sensor adopts micro-channel design to reduce position error, improve detection accuracy of a single particle, and reduce the occurrence of detection errors caused by multiple particles passing through at the same time. The inductive electric potential of the induction coil was obtained respectively by simulation analysis and experimental verification methods. It is found that the influence of particles on the internal magnetic field of the micro coil is relatively enhanced when the micro coil is used as excitation and induction coil. This results in two peaks and valleys shown from the generated inductive electric potential, increasing of the signal’s characteristic point position, and improvement in signal recognition. Therefore, it will provide more information for the subsequent material identification and multi-particle mixing signal differentiation. Meanwhile, a signal processing circuit with the function of low-pass filtering and amplification is designed to suppress the noise value of the output signal to around 1.25 mV, which could improve the stability of the system. A series of lubricating oil abrasive particle detection tests are conducted, and the results show that the new detection sensor is able to detect iron particles below 64 μm and copper particles below 125 μm. The abrasive particle information collected by this sensor can provide more precise wear status information for the health assessment and fault diagnosis of mechanical systems, which is important for the health monitoring of mechanical systems.

This paper presents a novel approach to microbubble technology for the treatment of aquatic invasive organisms in ship ballast water. The microbubbles are produced by hydrodynamic cavitation with a sudden and dramatic water pressure drop. The air and ozone microbubbles, respectively, verified the bioavailability of ship ballast water treatment using marine microalgae as an indicator. Besides the effects of an ozone injection dose, the morphological changes of cells and the effluent toxicity were investigated. Compared with the ozone microbubble treatment, the inactivation of marine microalgae by air microbubbles required a long treatment time. In the storage experiment, it was found that air microbubbles did not inhibit the growth of microalgae cells, and that the injection of active matter such as ozone was still necessary to ensure the validity of biological invasion. However, even with very low doses of ozone, the inactivation effect of ozone microbubbles was still very evident. Overall, it helps to minimize the use of active matter to reduce the toxicity of treated water, and this has the capability to develop into an environmentally acceptable and practical ballast water treatment technology.

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.