OSESgram: Data-Aided Method for Selection of Informative Frequency Bands for Bearing Fault Diagnosis

Abstract

Since bearings are key components of rotatory machines and they are prone to have faults, bearing fault diagnosis has received much attention. Because vibration signals collected from the casing of a machine contain sufficient fault information, bandpass filtering-based envelope demodulation on vibration signals has been a standard approach for bearing fault diagnosis. Before envelope demodulation, selection of informative frequency bands (IFBs) for bandpass filtering to enhance fault signatures is of vital importance to achieve a good bearing fault diagnosis performance. Even though a fast kurtogram as a classic approach for IFB selection has been widely studied and many variants were proposed, they are still affected by interference caused by impulsive noise in the time domain and discrete frequencies in the frequency domain. To solve this tough problem, this paper proposes an OSESgram for IFB selection. The core of the proposed OSESgram is that healthy and faulty vibration signals are formulated as a convex optimization problem to aid IFB selection, i.e., a data-driven optimized square envelope spectrum (OSES) rather than a square envelope or a square envelope spectrum is quantified to select an IFB. Since the OSES is obtained in a data-driven way, the proposed OSESgram is promising to be more robust to interferential noise in vibration signals, especially under the existence of random impulsive noise. Benchmark and industrial bearing vibration datasets are used to validate the effectiveness and superiority of the proposed OSESgram. Experimental results demonstrated that the proposed OSESgram is more effective and robust in indicating an IFB than the fast kurtogram and an improved kurtogram.