Fault Diagnosis of Rolling Element Bearings with a Spectrum Searching Method

TL;DR

This paper introduces a novel spectrum searching method for fault diagnosis in rolling element bearings, effectively identifying impulse harmonics in noisy vibration signals to improve fault detection accuracy.

Contribution

A new spectrum searching algorithm that constructs the spectrum information of impulses for clearer fault feature extraction in noisy environments.

Findings

Successfully detects bearing faults in simulated signals

Effective in noisy conditions with clear harmonic identification

Validated with benchmark bearing data

Abstract

Rolling element bearing faults in rotating systems are observed as impulses in the vibration signals, which are usually buried in noises. In order to effectively detect the fault of bearings, a novel spectrum searching method is proposed. The structural information of spectrum (SIOS) on a predefined basis is constructed through a searching algorithm, such that the harmonics of impulses generated by faults can be clearly identified and analyzed. Local peaks of the spectrum are located on a certain bin of the basis, and then the SIOS can interpret the spectrum via the number and energy of harmonics related to frequency bins of the basis. Finally bearings can be diagnosed based on the SIOS by identifying its dominant components. Mathematical formulation is developed to guarantee the correct construction of the SISO through searching. The effectiveness of the proposed method is verified with a simulation signal and a benchmark study of bearings.