Abstract
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.
Highlights
Bearings are one of key components in rotating machinery; their fault detection and diagnosis have long been investigated to prevent severe equipment damage and unscheduled downtime [1], [2]
Even if a clear TFR can be obtained with extracted fault signatures, bearing fault diagnosis cannot be fulfilled yet as the fault type cannot be determined without knowing the relationship of the IF ridges on the TFR
Even though the proposed dual-guidance based ORF scheme can facilitate the identification of ridge paths, it cannot make sure that instantaneous fault characteristic frequency (IFCF), instantaneous shaft rotating frequency (ISRF) and their harmonics are accurately extracted as the associated signal components might be still faint for weak faults
Summary
Bearings are one of key components in rotating machinery; their fault detection and diagnosis have long been investigated to prevent severe equipment damage and unscheduled downtime [1], [2]. Most fundamental IF ridge extraction methods are performed via searching the frequency bins with maximum energy at each time instance [32], which is extensively used for IF estimation of non-stationary signals as it is easy to be implemented with computational efficiency This kind of methods are based on the assumption that at each time instance the IFs of interest have the maximum amplitude on TFR, which is not always true, for the multi-component signal with low SNR under variable speeds. To solve this issue, Wang et al developed a novel amplitude-sum based spectral peak search algorithm, where, for each frequency bin, the sum of the amplitudes of its several multiples is calculated to replace the original amplitude [33].
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