Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Abstract

In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

“…Signal decomposition-based methods are similar to pattern recognition that relied on feature engineering, in which the different components are expected to be separated. Scholars and researchers have proposed lots of successful methods for compound fault diagnosis, such as Wavelet Transform (WT) [20][21][22][23][24][25][26][27][28], Variational Mode Decomposition (VMD) [29][30][31][32][33][34], Local Mean Decomposition (LMD) [35], Singular Spectrum Decomposition (SSD) [36,37], Symplectic Geometry Mode Decomposition (SGMD) [38,39], and other methods [40][41][42][43][44][45][46][47][48]. first, the compound fault signals are separated into different empirical models by empirical WT; second, a duffing oscillator which incorporates all single fault frequency is used to establish the fault isolator; finally, all the single faults can be recognized one by one by observing the chaotic motion from the Poincar mapping of the fault isolator outputs [20].…”

“…Different from the empirical WT which uses the fixed basis functions, He et al combined an adaptive redundant multiwavelet packet that can automatically select the sensitive frequency bands and Hilbert transform demodulation analysis to decouple the compound fault of two gearboxes [24]. An improved tunable Qfactor wavelet transform (TQWT) was proposed by Hu et al to decompose the vibration signal and the compound fault can be recognized by comparing the fault characteristic frequencies between the experimental results and theoretical values [27]. Although WT-based methods have many good properties ensuring the effectiveness in compound fault diagnosis, their decomposition performance of compound fault signals depended on the selected wavelet basis function.…”

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

“…Tis includes tunable Q-wavelet transform (TQWT) [28], stationary wavelet transform (SWT) [29], empirical wavelet transform (EWT) [30], and dual-tree complex wavelet transform (DTCWT) [31]. Te advantage of the TQWT is that it does not require the adjustment of the wavelet base function and can easily be adjusted according to the signal [32]. SWT shows the local time-frequency characteristics of a signal and has multiresolution analysis capability [33].…”

Research on signal decomposition algorithm of mechanical system based on multiwavelet

“…Wavelet transform 1,2 , empirical mode 3,4 , variational mode 5,6 , spectral kurtosis 7,8 are commonly used in mechanical system signal analysis. Among them, the wavelets are known as a "mathematical microscope" and have the characteristics of multi-resolution analysis.…”