Robust correlation measures for informative frequency band selection in heavy-tailed signals

Abstract

Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological process (falling pieces of rock) generates random impulses that complicate detection. To address this, signal pre-filtering (extracting the informative frequency band from noise-affected signals) is necessary. This paper proposes an algorithm for processing vibration signals from a bearing used in an ore crusher. Selecting informative frequency bands (IFBs) in the presence of impulsive noise is notably challenging. The approach employs correlation maps to detect cyclic behavior within specific frequency bands in the time–frequency domain (spectrogram), enabling the identification of IFBs. Robust correlation measures and median filtering are applied to enhance the correlation maps and improve the final IFB selection. Signal segmentation and the use of averaged results for IFB selection are also highlighted. The proposed trimmed and quadrant correlations are compared with the Pearson and Kendall correlations using simulated signal, real vibration signal from crusher in mining industry and acoustic signal measured on the test rig. Furthermore, the results of real vibration analyses are compared with established IFB selectors, including the spectral kurtosis, the alpha selector and the conditional variance-based selector.