Bearing Fault Identification Based on Blind Extraction of Cyclostationary Signals Using Order Tracking

Abstract

Mostly, a vibration signal from rotating machines comprises stationary and non-stationary components, whose description should be as accurate as possible to infer the internal and external forces that affect the system behavior. Moreover, either component can provide diverse but relevant information about the machine health. Thus, bearing faults foster the non-stationary component that is characterized by time-varying statistical moments, periodically changing through the time (or cyclostationary signals). Therefore, the problem to detect a bearing fault signal is usually addressed to separate the deterministic and the stochastic components of the vibration signal to make clear the damage characteristics. To this end, we present the novel order tracking (OT) method that decomposes the non-stationary vibration signal into narrow-band spectral components, aiming to enhance the cyclostationary characteristics. Moreover, a similarity measure is computed between the envelopes of the raw signal and each component, allowing to quantify the cyclic behavior of signal components. Since the proposed method acts as a narrow-band filter, a comparison with the spectral kurtosis (SK) is performed using a rolling element bearing dataset that includes an inner race, outer race, and rolling element defects. Specifically, the Case Western Reserve University data is carried out aiming to improve the diagnosis of bearing failures that are categorized in a recent work using the benchmark methods. As a result, the proposed blind extraction method allows capturing the cyclostationary behavior hidden in the signal and improves the identification of the bearing faults when the signal is noisy.