A capacitive–inductive magnetic plug sensor for online condition monitoring is proposed in this article, which can achieve particle counting, debris measurement, and wear rate estimation. The sensor can detect capacitance and inductance parameters, the coil is the outer electrode to detect the dielectric change in capacitance mode, and the coil directly induces magnetic field disturbance in inductance mode. Through introducing a reference unit, Wheatstone bridge circuits are constructed to enhance the response to debris and reduce the interference of oil temperature and viscosity. A postprocessing circuit with filtering and amplifying functions is adopted to improve accuracy. The sensor output characteristics and debris position effect are studied through the experiment. The comprehensive analysis of the detection results in two modes can improve sensor adaptability and overcome the deficiency of low reliability based on a single detection method. This sensor can provide more accurate debris information for the fault diagnosis of hydraulic equipment and is of great significance for intelligent maintenance.

At present, when performing material identification of metal wear debris, most sensors can only roughly distinguish between ferromagnetic and non- ferromagnetic metal wear debris, but fail to distinguish them deeply. To achieve fine detection of metal wear debris in the oil, this paper designs an asymmetric micro-three-coil sensor, which generates an asymmetric magnetic field by using two excitation coils of different sizes. Different metal wear debris are affected differently in the magnetic field and the shape of the output signal is also different, therefore, the wear debris material can be distinguished by the shape of the signal of the induction electric potential output from the induction coil alone, and the wear debris size can be determined by the amplitude of the signal. Copper particles, aluminum particles, iron particles and 304 stainless steel particles were selected as experimental objects, and the experimental results showed that the material differentiation of these four types of particles can be achieved by the asymmetric magnetic field of the sensor. The method provides a new research direction for the inductive method to distinguish non-ferrous metals, which is important for the fine detection of multi-parameter pollutants and the diagnosis and monitoring of mechanical system faults.

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.

Magnetic plug-type abrasive particle sensors have a wide range of applications in oil detection, but there is little literature on the effect of abrasive particle position on detection accuracy. In this paper, an alternating current (AC) bridge-type abrasive particle detection sensor is designed, in which the sensing module utilizes permanent magnets to attract iron particles, and the induction coil is specially designed to detect the magnetic field fluctuation caused by iron particles. A corresponding model was also designed to evaluate the sensor’s sensitivity at different locations. In this paper, the magnetic field distribution of the sensor was first analyzed using finite element analysis software to obtain the magnetic field strength at different positions. Then, the response sensitivity of the sensor to particles and the effect of different positions on the detection results are explored through experiments. The simulation and the experimental results show substantial signal difference signal at different sensor positions. The method outlined in this article can determine the optimal sensing range for subsequent magnetic plug-type abrasive particle detection sensors and subsequently improve their reliability.