An Ensemble Empirical Mode Decomposition-Based Lossy Signal Compression Method for a Remote and Wireless Bearing Condition Monitoring System

Abstract

Rolling bearings are one of the most widely used and most likely to fail components in the vast majority of rotating machines. A remote and wireless bearing condition system allows the bearings to be inspected in remote or hazardous environments and increases the machine reliability. To minimize the transmission loads of enormous vibration data for accurate bearing fault diagnosis, a lossy compression method based on ensemble empirical mode decomposition (EEMD) method was proposed for bearing vibration signals in this paper. The EEMD method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different frequency bands of signal components called intrinsic mode functions (IMFs). After applying the EEMD method to the vibration signal, the impulsive signal component related to the faulty bearing is extracted. The noise and irrelevant signal components that are often embedded in the collected vibration signals were removed. In the bearing signal, the distribution for most of the extremes is around zero. Almost all meaningful extremes related to the defect are concentrated in a small fraction of the samples. Hence, this signal compression provides high compression ratio for the bearing vibration signal. To verify the effectiveness of this method, raw vibration signals were collected from an experimental motor and a real traction motor. The proposed lossy signal compression method was applied to these vibration signals to extract the bearing signals and compress them. A comparison of this compression method with the popular wavelet compression method was also conducted. Wirelessly transmitting these compressed data demonstrates that the proposed signal compression method provides high compression performance for bearing vibration signals. Furthermore, the fault diagnosis using the reconstructed signal indicates that most of the impulses relating to the bearing fault are retained, including their periodicity and amplitudes, which are vital for accurate bearing fault diagnosis. Therefore, the compression of the bearing vibration signal contributes not only on the decreases of the file size and the transmission time, but also on the extraction of faulty bearing features to improve the accuracy in signal analysis. With the help of this method, wireless data communication for the remote and wireless bearing condition monitoring system becomes highly efficient, even in a limited bandwidth environment and maintains accurate bearing fault detection without loss of features and the need of transmitting a large amount of vibration data.