Research on Electrostatic Monitoring of Tribo-Contacts with Dynamic Adaptive Fusion Method

Abstract

Electrostatic monitoring is a unique and rapid developing technique applied in the prognostics and health management of the tribological system based on electrostatic charging and sensing phenomenon. It has considerable advantages in condition monitoring of tribo-contacts with high sensitivity and resolution. Unfortunately, the monitoring result can be affected due to the switch of operating conditions that reduces its accuracy. This paper presents a dynamic adaptive fusion approach, moving window local outlier factor based on electrostatic features to overcome the influence. Life cycle experiments of rolling bearings and railcar gearbox were carried out on an electrostatic monitoring platform. The MWLOF method was used to extract and analyze the experimental data, combined with the Pauta criterion to judge wear faults quantitatively, and compare with other feature extraction results. It is verified that the proposed method can overcome the influence of changes in working conditions on the monitoring results, improve the monitoring sensitivity, and provide an accurate reference for friction and wear faults.

Compressive sensing-based de-aliasing method for array electrostatic signal

“…In recent years, thanks to breakthroughs in EMT basic research such as electrostatic signal conditioning circuit design (Addabbo et al , 2015), electrostatic sensing characteristics research (Yan et al , 2021) and electrostatic signal process methods (Wang et al , 2013); some researchers have designed a variety of different electrostatic sensors for condition monitoring of aero-engine lubricating oil line, carrying out researches including fault diagnosis of accessory gearbox, rolling bearing fault diagnosis and wear monitoring (Li et al , 2020; Zhang and Wang, 2020), wear site monitoring (Liu et al , 2022a; Liu et al , 2022b) and online oil debris monitoring (Sun et al , 2021). In addition, another group of researchers have carried out research on the fault diagnosis and health monitoring of aero-engine gas path components, including the monitoring of foreign object inhalation in the aero-engine intake (Guo et al , 2022), aero-engine gas path fault diagnosis and exhaust emissions assessment (Jin et al , 2020; Guo et al , 2021a).…”