Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis

This paper presents a new signal processing algorithm, termed multi-scale enveloping spectrogram (MuSEnS), for vibration signal analysis in the condition monitoring and health diagnosis of rolling bearings. Compared to the conventional enveloping spectral analysis technique in which the bandwidth of the signal components of interest needs to be known a priori to obtain consistent results under varying machine operating conditions, the new technique enables simultaneous multi-scale decomposition to extract and separate envelopes of the repetitively excited mechanical vibrations with different frequency coverage, thus improving the robustness in signal analysis. Complex wavelet was investigated as the base wavelet on its ability in combining band-pass filtering and enveloping into a single-step operation. The new technique was experimentally evaluated using vibration signals measured on rolling element bearings that contain localized structural defects, and good results were obtained that verified the validity and effectiveness of the new signal processing technique.