Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

Jia, Feng; Lei, Yaguo; Shan, Hongkai; Lin, Jing

doi:10.3390/s151129363

Cited by 84 publications

(52 citation statements)

References 21 publications

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“…The MCKD algorithm is currently applied in many fault diagnosis studies on large-size rolling bearings. Jia et al [8] combined MCKD with improved spectrum kurtosis to diagnose vibration signals and extract the fault features of rolling bearings in wind turbines and hot-strip rolling mills. Zhao and Li [9] used MCKD and empirical mode decomposition (EMD) method to extract the early-stage fault features of rolling bearings in wind turbines from strong background noise.…”

Section: Introductionmentioning

confidence: 99%

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Wang

Liu

et al. 2018

Shock and Vibration

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Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Wang

Liu

et al. 2018

Shock and Vibration

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“…Here background noise is a non-ignorable factor that should be carefully considered. Maximum Correlated Kurtosis Deconvolution (MCKD) [20] is an effective approach to highlight the fault-related periodic impulse components [21]. However, whether its parameter settings are proper or not has a direct influence on analysis results [22,23].…”

Section: Introductionmentioning

confidence: 99%

The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Sun

Liao

Lin

2017

Sensors

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The properties of the time domain parameters of vibration signals have been extensively studied for the fault diagnosis of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock Pulse Index (SPI), is proposed in this paper. It integrates the mutual advantages of both the parameters mentioned above and can help effectively identify fault-related impulse components under conditions of interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the transient information of interest contained in the filtered signal can be highlighted through demodulation with the Teager Energy Operator (TEO). Fault-related impulse components can therefore be extracted accurately. Simulations show the SPI can correctly indicate the fault impulses under the influence of strong background noises, other harmonic components and aperiodic impulse and experiment analyses verify the effectiveness and correctness of the proposed method.

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“…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.…”

mentioning

confidence: 99%

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

Wang

Yan

2019

Applied Sciences

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The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

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Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

Cited by 84 publications

References 21 publications

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

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

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