An online de-noising method for oil ultrasonic wear debris signal: fuzzy morphology component analysis
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 4 July 2018
Issue publication date: 17 August 2018
Abstract
Purpose
In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.
Design/methodology/approach
For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.
Findings
According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.
Research limitations/implications
The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.
Practical implications
This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.
Originality value
This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.
Keywords
Acknowledgements
The work described in this paper was supported in part by the National Natural Science Foundation of China (No. 51205405).
Citation
Li, Y. and Zhang, P. (2018), "An online de-noising method for oil ultrasonic wear debris signal: fuzzy morphology component analysis", Industrial Lubrication and Tribology, Vol. 70 No. 6, pp. 1012-1019. https://doi.org/10.1108/ILT-12-2016-0302
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited