Online monitoring and failure detection of capacitive displacement sensor in a Capball device using fractal analysis. (March 2018)

• Record Type: Journal Article
• Authors: Rimpault, X.; Bitar-Nehme, E.; Balazinski, M.; Mayer, J.R.R.
• Is Part Of: Measurement. Volume 118(2018)
• Page Start: 23
• Page End: 28
• Publication Date: 2018-03
• Subjects: Sensor failure detection -- Capacitive displacement sensor -- Online monitoring -- Fractal analysis -- Machine error; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.01.006
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Sensor failure may be difficult to detect depending on the nature of the defect. In this study, fractal analysis was used to analyse the raw signal of capacitive displacement sensors in order to detect abnormalities. The fractal analysis of the signals was performed for stationary and dynamic measurements of the target–sensor distance mounted in a Capball device. The fractal results such as fractal dimension and topothesy are found to be effective estimators of the signal characteristics. A sensor failure index, based on a combination of fractal parameters, is proposed herein and is found efficient to detect sensor defects.