Applications of robust statistics for cyclostationarity detection in non-Gaussian signals for local damage detection in bearings

TL;DR

This paper enhances cyclostationary analysis for non-Gaussian signals by developing robust spectral coherence methods, improving detection of periodic behavior in impulsive, non-Gaussian vibration signals for bearing damage diagnosis.

Contribution

It introduces robust variants of spectral coherence to better detect periodicity in impulsive, non-Gaussian signals, advancing condition monitoring techniques.

Findings

Robust spectral coherence outperforms classical methods in impulsive noise environments.

The approach effectively detects local damage in bearing vibration signals.

Validated on both simulated and real-world signals.

Abstract

Signals with periodic characteristics are ubiquitous in real-world applications. One of these areas is condition monitoring, where the vibration signals from rotating machines naturally display periodic behavior. Thus, the cyclostationary analysis has evolved into the investigation of such signals. For the traditional cyclostationary approaches, the autocovariance function (ACVF) and its bi-frequency representation, spectral coherence (SC), are regarded as the base. However, recent research has revealed that real vibration signals increasingly exhibit impulsive behavior in addition to periodicity. As a result, there was a need for new methods to identify periodic behavior that take into account the impulsiveness of the data. In this article, we provide a way to improve the SC method by using its robust variants in place of the classical ACVF estimator (sample ACVF). The suggested concept is intuitive and relatively simple. We create robust versions of the SC algorithm that more accurately detect periodic behavior in signals with significant disruptions in contrast to the classical techniques. The efficiency of the proposed approach is demonstrated for simulated signals with three different types of non-Gaussian noise distribution and different levels of periodic impulses imitating a local damage. The introduced approach is also validated on real vibration signal from the rolling element bearings operating in a crushing machine.