Dual-guidance based optimal resonant frequency band selection and multiple ridge path identification for bearing fault diagnosis under time-varying speeds

• Published in: IEEE access Vol. 7; p. 1
• Main Authors: Shi, Juanjuan, Huang, Weiguo, Shen, Changqing, Jiang, Xingxing, Zhu, Zhongkui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Band-pass filters; Bearing fault diagnosis; Fault diagnosis; Fault feature extraction; Feature extraction; Frequencies; Index hybrid; Indexes; Kurtosis; Mathematical analysis; Optimal resonant frequency; Resonant frequencies; Resonant frequency; Ridges; Search algorithms; Segmentation; Signal to noise ratio; Smoothness; Synchronism; Time frequency ridge extraction; Time-frequency analysis; Variable speed condition; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2945030

To perform bearing fault diagnosis under variable speeds, the optimal resonant frequency (ORF) band selection and diagnosis strategy are pivotal. Indexes, such as kurtosis, crest factor (CF) and smoothness index (SI), are extensively used for guiding ORF selection. Due to that each index has unique advantages, the hybrid of such indexes has been developed. However, applications of the current index hybrid method are impeded by problems of: 1) ineffectiveness for signal corrupted by impulsive noises and 2) equal segmentation of frequency band with human intervention. This paper, therefore, firstly proposes a dual-guidance based scheme with an embedded tunable Q-factor wavelet transform (TQWT) to address the problems. The so-called dual-guidance scheme contains two guidance procedures: (1) the SI guided pre-process for obtaining weight vectors, and (2) the index hybrid output guided scheme for ORF selection. The embedded TQWT is used for frequency band segmentation and sub-band signal acquisition without subjective interventions. With the proposed scheme, the ORF band can be determined for bearing fault feature extraction. Then, an algorithm for multiple instantaneous frequency (IF) ridge identification is exploited based on the peak search algorithm for diagnosis. To tackle the difficulty that, at each time instance, the amplitudes of IF ridges of interest do not always dominate the time frequency representation (TFR), a starting point search tactic with a synchronization step is explored. A diagnosis vector can subsequently be obtained by calculating the average ratios of the identified ridges and bearing fault diagnosis can then be done by matching the elements of the diagnosis vector with fault characteristic coefficient (FCC). Comparisons are performed to illustrate the superiority of the proposed method. The experimental analyses are also conducted to validate the proposed method for bearing fault diagnosis under variable speeds.