Informative frequency band selection in the presence of non-Gaussian noise – a novel approach based on the conditional variance statistic with application to bearing fault diagnosis

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

“…In our case, the STFT is applied. The interpretation of the STFT-based map is intuitive-it describes the energy flow in time for some narrow frequency band, i.e., sub-signal [18,20,46,49,54]. The spectrogram has some limitations.…”

“…The conditional variance statistic is described in details in [49]. It originates from the statistical phenomenon commonly referred to as the 20/60/20 Rule [64,65].…”

“…Our analysis will cover spectral kurtosis [45], kurtogram [19], stability index (Alpha selector) [46], spectral Gini selector [47], spectral smoothness index [17], infogram [48] and conditional variance-based selector [49].…”

“…The first six methods were considered in the literature as the most effective and classical ones in the problem of local damage detection. In our recent research [49], we have utilized the statistic called the conditional variance statistic and proposed a new algorithm for local damage detection which seems to be applicable when the background noise is contaminated by the non-cyclic large impulses. In the paper [49], we have indicated the limitations of the proposed method and proved its effectiveness, especially when the ratio of the amplitudes of non-cyclic to cyclic impulses is large.…”

“…In our recent research [49], we have utilized the statistic called the conditional variance statistic and proposed a new algorithm for local damage detection which seems to be applicable when the background noise is contaminated by the non-cyclic large impulses. In the paper [49], we have indicated the limitations of the proposed method and proved its effectiveness, especially when the ratio of the amplitudes of non-cyclic to cyclic impulses is large. In the current paper, which can be considered as the extension of our previous research, we demonstrate that the results obtained for the conditional variance-based selector are superior with respect to the results based on the mentioned other algorithms.…”