Application of energy distribution-based adaptive superlet transform in bearing fault diagnosis

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (15) : 224-234.

FAULT DIAGNOSIS ANALYSIS

Application of energy distribution-based adaptive superlet transform in bearing fault diagnosis

HOU Junqi, XIAO Songhua, SUN Beibei*

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Abstract

The Superlet Transform (SLT), which is a novel high-resolution time-frequency analysis method, is obviously superior to classical time-frequency analysis methods such as the Short-time Fourier Transform and the Continuous Wavelet Transform in terms of energy aggregation and noise robustness. However, when applying SLT to bearing vibration signals with widely distributed and sparse frequency components, meeting diagnosis requirements in practical becomes challenging. To enhance computational efficiency, Energy Distribution based Adaptive SLT (ED-ASLT) is proposed. Firstly, the frequency energy distribution of signal is obtained based on Welch method. Then, the superlets center frequencies adaptive sampling is carried out based on the energy distribution. Then, the window lengths of wavelets in each superlet are determined according to the center frequency value and the energy at the center frequency. Finally, the superlets are constructed to complete the time-frequency analysis. The ED-ASLT method can identify the key frequency bands that may reflect fault information in the signal according to the energy distribution of the specific fault signal, determine the SLT parameters adaptively, and perform high energy aggregation, high noise robustness and efficient time-frequency analysis of the signal, which is helpful for more accurate time-frequency positioning of the abnormal components in the signal and resist the interference of noise. Improve the accuracy and efficiency of fault diagnosis. The ED-ASLT method were compared with other time-frequency analysis methods in terms of energy aggregation, noise robustness and time cost by using bearing vibration signals from laboratories of Paderborn University and Southeast University. It is verified that the proposed method can greatly reduce the time cost while maintaining the advantages of high energy aggregation and high noise robustness of SLT, which is helpful to provide higher-quality criteria for fault diagnosis.

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adaptive superlet transform / time-frequency analysis / fault diagnosis / energy distribution

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HOU Junqi, XIAO Songhua, SUN Beibei. Application of energy distribution-based adaptive superlet transform in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2025, 44(15): 224-234

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