Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator

Abstract

An intelligent monitoring lubricant is essential for the development of smart machines because unexpected and fatal failures of critical dynamic components in the machines happen every day, threatening the life and health of humans. Inspired by the triboelectric nanogenerators (TENGs) work on water, we present a feasible way to prepare a self-powered triboelectric sensor for real-time monitoring of lubricating oils via the contact electrification process of oil–solid contact (O–S TENG). Typical intruding contaminants in pure base oils can be successfully monitored. The O–S TENG has very good sensitivity, which even can respectively detect at least 1 mg mL –1 debris and 0.01 wt % water contaminants. Furthermore, the real-time monitoring of formulated engine lubricating oil in a real engine oil tank is achieved. Our results show that electron transfer is possible from an oil to solid surface during contact electrification. The electrical output characteristic depends on the screen effect from such as wear debris, deposited carbons, and age-induced organic molecules in oils. Previous work only qualitatively identified that the output ability of liquid can be improved by leaving less liquid adsorbed on the TENG surface, but the adsorption mass and adsorption speed of liquid and its consequences for the output performance were not studied. We quantitatively study the internal relationship between output ability and adsorbing behavior of lubricating oils by quartz crystal microbalance with dissipation (QCM-D) for liquid–solid contact interfaces. This study provides a real-time, online, self-powered strategy for intelligent diagnosis of lubricating oils.

Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion

“…Based on supervisory control and data acquisition (SCADA), the fault warning method of the wind turbine gearbox in the SCADA system is still concerned. A common method is that multiple parameters of SCADA are integrated with a machine learning method to establish a model of a certain state variable during normal operation and state assessment and fault warning are conducted by monitoring the dynamic residual change between the predicted value and actual value [2]. However, the method of integrating multiple monitoring parameters into a single predicted value of monitoring parameters is often di cult to characterize the operating state of the gearbox comprehensively and e ectively.…”

Self-Powered, Long-Durable, and Highly Selective Oil–Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

“…Thus, the enhanced electric output partly originates from the electron-withdrawing ability of the functional groups on the solid surface, which is due to the high content of F atoms on the surface providing a strong electron-withdrawing ability (Table S3). It is found that oils with low surface energy can be easily adsorbed on PTFE, glass and polyethylene surfaces, which will cause electric-field screening effect due to residual oils and resulting in a quick decrease of the output signal [ 38 ]. Fortunately, the as-designed FO-TENG has a superoleophobic surface, which ensures a much weaker electric screening effect and thus achieving a higher signal output.…”

“…In our recent work [ 38 ], we prepared an O-TENG based on the contact and separation between lubricating oils and solid surfaces to obtain triboelectric signals, which can be used for online monitoring lubricating oils conditions. It is found that oils are prone to strongly adsorbing on the contact solid surface, resulting in lower signal output.…”

Review of Tribological Failure Analysis and Lubrication Technology Research of Wind Power Bearings

“…Real-time monitoring of bearing lubrication is the most direct and effective method to determine the health of bearings, and although many explorations have been conducted in both academia and industry for surveillance techniques [ 214 , 215 ], further research is still required. Most of the existing research findings are still based on the signal analysis of bearing vibration so as to detect lubrication status in time and carry out maintenance when abnormal fluctuation of shock data occurs.…”