# Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD
###### Abstract  
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the issue that rolling bearings are prone to noise in the early stage and difficult to extract feature information accurately. The method uses the CEEMDAN algorithm to reduce the noise of the rolling bearing vibration signal in the first step; then, the MCKD algorithm is used to deconvolve the signal to enhance the weak shock components in the signal and improve the SNR. Finally, the envelope spectrum analysis is performed to extract the feature frequencies. Simulation and experimental results show that the CEEMDAN-MCKD method can highlight the fault characteristic frequency and multiplier frequency better than other methods and realize the characteristic enhancement of incipient fault vibration signals of rolling bearings under constant and variable operating conditions. rolling bearings; feature enhancement; CEEMDAN; MCKD; vibration signal +
Footnote †: journal: Applied  
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poor decomposition integrity of EEMD, and this algorithm has been widely used in recent years. [PERSON] et al. [10] used CEEMDAN for noise reduction of the signal and combined it with a particle swarm optimization-support vector machine to achieve fault diagnosis of rolling bearings. [PERSON] et al. [11] used a combination of CEEMDAN and fuzzy entropy to achieve the extraction of fault features for the problem that the wind turbine bearing signal is weak and the fault information is not easily extracted. The kurtosis can detect anomalous information in the signal and also filter the signal to maximize its impact in the signal [12]. [PERSON] [13] proposed a crag-based minimum entropy deconvolution method, which uses the entropy minimization criterion to optimize the FIR filter parameters so that the entropy of the filtered signal is minimized and the fault shock characteristics in the signal are enhanced. Scholars have made use of the algorithm in fault diagnosis and have demonstrated that minimum entropy deconvolution (MED) has good results in noise reduction and highlighted the shock components in the signal. [PERSON] et al. [14] used wavelet packet and coordinate transform for fault feature enhancement of rolling bearings. [PERSON] et al. [15] used improved empirical wavelets and MED to process weak fault signals to achieve bearing fault diagnosis. [PERSON] et al. [16] combined time-synchronous averaging (TSA) with MED to remove noise for fault feature extraction of gearboxes. However, scholars found that the algorithm still has shortcomings [17], the shock component in the rolling bearing signal is periodic, and the MED algorithm can usually deconvolve only a single pulse feature and is prone to trigger modulation phenomena [18]. [PERSON] [19] et al. proposed the maximum correlation kurtosis deconvolution method to solve that problem. This method preprocesses the non-integer fault cycles in accordance with the a priori knowledge of the fault cycles and extracts the shock pulses that occur at each rotation of the rotating machinery fault signal. MCKD has been a popular tool for diagnosing problems with gears, bearings, and other rotating machinery parts in recent years. The reliability of the suggested method was experimentally confirmed by [PERSON] et al. [20], who employed MCKD to improve the reconstructed signal in order to tackle the problem of a weak rolling bearing signal. However, the filter length, shift number, and deconvolution period are easily able to alter the enhancing effect of MCKD. Thus, the literature [21] employed particle swarm optimization (PSO) to optimize the filter length and the fault period in MCKD in order to achieve the extraction of faint-bearing fault features against a noisy environment.