Rolling bearing fault diagnosis with compressed signals based on hybrid compressive sensing

Abstract

In recent years, many studies have been conducted on remote machine condition monitoring and on-board bearing fault diagnosis. To accurately identify the fault pattern, large amounts of vibration data must be sampled and saved. However, because the bandwidth of the wireless communication is limited, the volume of transmitted data cannot be too large. Therefore, raw data are usually compressed before transmission. All of the previous compression methods use a sample-then-compress framework; however, in this work, we introduce a compression method based on a Compressive Sensing that can simultaneously collect and compress raw data. Additionally, a hybrid measurement matrix is designed for Compressive Sensing and used to compress the data and make it easy to conduct fault diagnosis in the compression domain. This process can significantly reduce the computational complexity of on-board fault diagnosis. The experimental results demonstrate that the proposed compression method is easy to use and the reconstruction process can recover the original signal perfectly. Above all, the compressed signal can preserve the time series information of the original signal, and in contrast to fault diagnosis using the original signal, the accuracy of the fault diagnosis based on conventional methods in the compression domain is not significantly decreased.