Feature enhancement method of rolling bearing acoustic signal based on RLS-RSSD

Abstract

The bearing acoustic signal is interfered by reflected sounds and background noises, resulting in a low signal-to-noise ratio (SNR). To address this problem, this paper proposes a feature enhancement method that combines recursive least squares (RLS) with resonance-based sparse signal decomposition (RSSD) into the RLS-RSSD method. First, the RLS method is used as the inverse filter to remove the reverberation as well as reduce the interference of the late reflected sound on the direct signal, then RSSD and wavelet denoising are used to eliminate aperiodic component in the low and high frequency bands. The signals are synthesized based on the amplitudes of different frequency signals, and finally, the bearing fault is determined by envelope spectrum analysis. The results of the simulation data, experimental data, and field application data analysis indicate that the frequency of bearing defects can be accurately extracted by the proposed method.