Abstract
The vibration signals acquired on machines usually have complex spectral structure. As the signal of interest (SOI) is weak (especially at an early stage of damage) and covers some frequency range (around structural resonance), it requires its extraction from a raw observation. Until now, most of the techniques assumed the presence of Gaussian noise. Unfortunately, there are cases when the non-informative part of the signal (considered as the noise) is non-Gaussian due to the random disturbances or nature of the process executed by the machine. Thus, the problem can be formulated as the extraction of the SOI from the non-Gaussian noise. Recently this problem has been recognized by several authors and some new ideas have been developed. In this paper, we would like to compare these techniques for benchmark signals (Gaussian noise, cyclic impulsive signals, non-cyclic impulsive signals with random amplitudes and locations of impulses and a mixture of all of them). Our analysis will cover spectral kurtosis, kurtogram, stability index (Alpha selector), conditional variance-based selector, spectral Gini index, spectral smoothness index and infogram. Finally, a discussion on the efficiency of each method is provided.
Highlights
The problem of bearings local damage detection is widely discussed in the literature [1,2,3,4].In the case of constant speed, the task is defined as the detection of the periodic impulsive signals.The vibration under time-varying speed requires special treatment, see [5,6], and will not be considered in this paper
(b) Kurtosis selector applied to signal s1
In the paper the most prominent techniques of the informative frequency band (IFB) selection developed in recent years have been tested for benchmark signals that imitate four different cases of the condition of the rotating component of the machine: 1
Summary
The problem of bearings local damage detection is widely discussed in the literature [1,2,3,4].In the case of constant speed, the task is defined as the detection of the periodic impulsive signals.The vibration under time-varying speed requires special treatment, see [5,6], and will not be considered in this paper. The problem of bearings local damage detection is widely discussed in the literature [1,2,3,4]. In the case of constant speed, the task is defined as the detection of the periodic impulsive signals. The most popular approach is the envelope analysis and detection of fault frequencies in the spectrum of the envelope for a pre-filtered signal. The filtration of a raw signal is used to select its informative part and avoid other spectral content not related to the local damage. An optimal solution has been proposed by using Wiener filtering [15]. The filter coefficients could be optimized by the genetic algorithm proposed in [16]. The most popular approach is based on the spectral kurtosis as an informative frequency band (IFB) selector (filter characteristic)
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