Local Spectral Amplitude Modulation and its Application as Samgram for Fault Diagnosis of Rolling Bearings

Abstract

Envelope analysis is one of the most commonly used methods in rolling bearing fault diagnosis. However, when a signal contains heavy noise, even if an appropriate frequency band is selected, the fault information can still be overwhelmed. In view of this problem, a local spectral amplitude modulation (LSAM) method is proposed in this paper, where a wider range of weights MO are given to a single frequency band for amplitude modulation to highlight frequency components related to bearing faults and obtain a series of modified filtered signals. Meanwhile, an indicator called normalized harmonic kurtosis (NHK) is proposed to select an optimal modified filtered signal automatically by quantifying repetitive impulsive information in a signal. To extend LSAM to practical applications, two spectrum segmentation approaches based on scanning spectrum and trend component respectively are provided to realize LSAM, named the scanning SAMgram and adaptive SAMgram, which aim at determining the optimal modified filtered signal for envelope demodulation by searching for the combinations of frequency bands and MOs. A simulated signal of bearing compound faults and experimental signals of bearing outer ring and inner ring faults indicate that the proposed method can not only select a frequency band related to bearing defect to eliminate interference of invalid components, but also highlight fault characteristics in the selected frequency band and weaken the disturbance of noise, which is superior over traditional envelope analysis and more applicable for fault diagnosis of rolling bearings under complex environments.

Keywords: spectral amplitude modulation, local spectral amplitude modulation, normalized harmonic kurtosis, SAMgram, fault diagnosis