As a wear particle detection technology for machinery, electrostatic monitoring has broad application prospects However, there is currently a lack of a comprehensive method for achieving precise wear particle detection through electrostatic signals. This study proposes a wear particle detection method based on coaxial array electrostatic probes and a VMD-SR-DTW algorithm model. Firstly, a joint signal processing model is constructed using Variational Mode Decomposition (VMD) and Sparse Representation (SR) to reduce noise interference and enhance the signals triggered by wear particles. Furthermore, an algorithm model based on sliding windows and Dynamic Time Warping (DTW) is developed by utilizing the multi-channel signals from the coaxial array electrostatic sensor. The overall algorithm flow is analyzed in detail. Simulations and experiments are conducted to validate the effectiveness of the proposed method. The results demonstrate that the method effectively detects the signal pulses triggered by wear particles and provides a reliable approach for the engineering application of online monitoring.

Wear debris is the direct byproduct of frictional interactions between mechanical components. The morphology, quantity, and material characteristics of the debris provide critical information on the wear condition of oil-lubricated systems. Timely detection and monitoring of the debris are critical to preventing further abrasive damage and ensuring reliable operation of the components. Various sensing technologies have been developed to address this challenge. Despite a lot of real-time monitoring research have been done over the past decade, a comprehensive review of online and inline monitoring methods remains lacking. To address this gap, this study presents a systematic bibliometric analysis of recent research on oil wear debris real-time monitoring. The results show that there is a growing interest and rapid development in this field. Key advances in these technologies are reviewed, including electrical sensors, magnetic sensors, acoustic sensors, energy-based sensors, and optical sensors. In addition to analyzing the current literature, this study also highlights the technical limitations and outlines the future research directions. The objective is to provide researchers with an in-depth understanding of the debris monitoring methods in this domain and offer guidance for future research endeavors.

Heavy equipment gear transmission system require monitoring of wear particles in lubricating oil to assess the degree of wear and predict failure. Compared to the traditional detection method, electrostatic sensor is an innovative online sensor for wear particle monitoring due to its high sensitivity and strong capability of early fault warning. This study presents a new spiral-shaped probe electrostatic sensor (S.P.E.S) and derives an analytical mathematical model of point charges on a spiral-shaped probe based on the physical model. To obtain a deeper understanding of the spiral-shaped probe, a finite element model was developed by using COMSOL software to explore the sensing characteristics and spatial static sensitivity of the spiral probe. Furthermore, experimental studies were conducted to investigate the dynamic sensitivity. The findings indicate that the sensitivity distribution of SPES is asymmetric due to the asymmetric structure of the probe. The simulation analysis and experimental provide a scientific basis for the design of spiral-probe sensor.

The aero-engine gas-path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, a method is proposed to acquire signal sample under a specific operating condition for on-line fault detection. The symbolic time-series analysis (STSA) method is adopted for the analysis of signal sample. Advantages of the proposed method include its efficiency in numerical computations and being less sensitive to measurement noise, which is suitable for in situ engine health monitoring application. A case study is carried out on a data set acquired during a turbojet engine reliability test program. It is found that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data (GPEMD) for different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults. Finally, the further research task and direction are discussed.