Rolling bearing fault diagnosis under fluctuant conditions based on compressed sensing

Citations Over TimeTop 11% of 2016 papers

Abstract

Bearings are widely used in industries and construction for shaft supporting or seismic isolation. In recent years, their fault diagnosis, especially under variable, or fluctuant conditions, has received increasing attention. Sufficient monitoring data are usually required for bearing diagnosis. However, sufficient data cannot be guaranteed in some engineering cases with limitations of the transmission channel bandwidth or onboard/onsite computational capabilities. Fortunately, the emerging compressed sensing technique, which provides an effective solution to data compression and processing, has the ability to transform traditional monitoring data to the compressed information domain for a highly effective diagnosis under fluctuant conditions. This study proposes a bearing fault diagnosis method under fluctuant conditions based on compressed sensing theory. First, a random matrix is constructed as the measurement matrix and is employed to compress the original signal into the compressed information domain. Then, reconstruction-evaluation based fault diagnosis method is conducted with compressed signals. Moreover, the compressed signals used for fault diagnosis are reconstructed on the remote side. The experimental results provide evidence that the proposed method can effectively reduce the data volume required for bearing diagnosis and maintain an accuracy similar to current approaches, and the reconstructed signals can be used for other fault diagnosis methods. Copyright © 2016 John Wiley & Sons, Ltd.

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