Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Abstract

Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

Fault Diagnosis of Rotating Machine

“…In the first paper, Duan et al [39] improved the fault detection rate of a general rolling bearing by combining the local mean decomposition (LMD) and the ratio correction methods. Then, Cui et al [40] discussed the diagnosis of multiple defects in a rolling bearing via vibration analysis; Shi et al [41] reported a frequency matching linear transform technique for bearing fault detection under variable rotating speeds; moreover, Yin et al [42] proposed a Huffman coding technique to identify bearing defect severity. By what follows, artificially intelligent techniques, including ensemble learning [43] and deep learning [44,45] were developed to detect bearing faults.…”

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

“…[(1 + A cos(2π f r t))δ(t − iT)] ⊗ [e −ct cos(2π f n t)] + n(t) (14) where ⊗ denotes the convolution operation, δ(t) represents the Dirac delta function and c = 700 rad/s is the structural attenuation factor of bearing system. M = 60, f r = 30 Hz and f n = 4000 Hz respectively represent the number of impact, the rotating frequency and the resonant frequency excited by defect point strike.…”

“…As the premier deconvolution technology, minimum entropy deconvolution (MED) has been successfully utilized for bearing defect identification [10], but its performance is weakened due to it preferably recovers a large random impact rather than the periodic impacts [11]. Subsequently, maximum correlated kurtosis deconvolution (MCKD) [12] technology is further developed to overcome the drawback of MED, and satisfactory diagnosis results have been achieved in some cases [13,14]. However, some inherent disadvantages also limit its engineering application.…”

Adaptive sparse denoising and periodicity weighted spectrum separation for compound bearing fault diagnosis

“…To save time of designing atoms, Zhang et al [17] used tunable Q wavelet transform (TQWT) as the dictionary to separate compound faults and diagnose the corresponding faults by energy operator demodulation. Cui et al [18] also adopted dual-Q-factor TQWT as the dictionary and separated compound faults through improved maximum correlated kurtosis deconvolution (MCKD). Even though these dictionary-related SR methods all successfully diagnose and separate the compound faults of rotary machines, they all need precise construction of the dictionary, which is hardly guaranteed in real applications.…”