Adaptive Kurtogram and its applications in rolling bearing fault diagnosis

• Published in: Mechanical systems and signal processing Vol. 130; pp. 87 - 107
• Main Authors: Xu, Yonggang, Zhang, Kun, Ma, Chaoyong, Cui, Lingli, Tian, Weikang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-09-2019
• Subjects: Adaptive kurtogram; Empirical wavelet transform; Non-stationary signals; Order statistics filter; Rolling bearing; Adaptive kurtogram; Empirical wavelet transform; Rolling bearing; Order statistics filter; Non-stationary signals
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.05.003

•Adaptive Kurtogram is proposed to diagnose bearing faults.•The envelope of spectrum is calculated by order statistics filter.•The minimum values of this envelope are used to divide the boundaries.•Meyer wavelets are used to construct filters and reconstruct signals.
As one of the most important components in the rotating machinery, the rolling bearing will affect the operation precision of the equipment, the running state of the gear, the degree of the axis and even cause the damage of the equipment. Therefore, it is very necessary to improve the processing methods of non-stationary signals. In this paper, an adaptive Kurtogram (AK) method is proposed. The greatest advantage of this method is the use of the order statistics filter (OSF) to estimate and divide the effective modal components from the spectrum to replace the fast Kurtogram (FK). The minimum envelope value of the signal in frequency domain is obtained and taken as the boundaries. Change the sliding window width to divide different boundaries and form an array. The empirical wavelet transform (EWT) is used to reconstruct the signal components according to the boundary array. Then their kurtosis values are calculated. The frequency band with the largest kurtosis value contains the impact information, and the corresponding time domain component presents periodicity impact. AK improves the shortcomings of the center frequency and the bandwidth of the fast Kurtogram (FK) that cannot be explained theoretically. The method of dividing the boundaries in the frequency domain is optimized. After verification by the simulated signals and the actual signals, this method is faster and more efficient.