A novel bearing health sensing technique using smartphone

Abstract

Bearing health sensing has drawn increasing attention as bearing plays a vulnerable yet essential role in modern industry. Nevertheless, conventional health sensing technologies involve dedicated sensor installation and high costs, which may not meet the monitoring and sensing demands. Given this, we present a health sensing technique to capture the fault information of bearing by leveraging an off-the-shelf smartphone. Specifically, we first exploit only a readily available smartphone to capture the audio signal of bearing. Given the captured audio signal, an adaptive time–frequency masking is proposed to separate the noise, and the fault impulse is then sharpened by the singular value decomposition (SVD). The extensive experimental results illustrate that our technique is able to enhance the bearing health sensing capability of smartphones under various real-world scenarios. Even in noisy mining field conditions, the framework still achieves a notable performance improvement rate of 169 %.