Optimal Resonant Band Demodulation Based on an Improved Correlated Kurtosis and Its Application in Bearing Fault Diagnosis

Chen, Xianglong; Zhang, Bingzhi; Feng, Fuzhou; Jiang, Pengcheng

doi:10.3390/s17020360

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…Kurtosis has been proven to be a very useful monitoring parameter measuring the waveform peak and sensitive to the impulse characteristics of signal in rotating machinery fault diagnosis. Generally, kurtosis represents the characteristic of signals around its mean value and characterizes the strength of transient impulses [28] and is defined as the ratio of the fourth central moment of the signal to the squared second central moment of the signal [29], which is expressed in the following equation:…”

Section: Squared Envelope Spectrummentioning

confidence: 99%

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

Cui

Chen

et al. 2018

Shock and Vibration

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Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.

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Section: Squared Envelope Spectrummentioning

confidence: 99%

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

Cui

Chen

et al. 2018

Shock and Vibration

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“…Wang et al [22] obtains the parameters of Morlet wavelet using simulated annealing algorithm that adopted the maximum sparsity as the fitness function. Chen et al [23] used a particle swarm optimization algorithm on the basis of the correlated kurtosis of squared envelope spectrum as fitness function to obtain the parameters of Morlet wavelet.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Asymmetric Real Laplace Wavelet Filtering and Its Application on Rolling Bearing Early Fault Diagnosis

Wan

Peng

2019

Shock and Vibration

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The early fault of rolling bearing is weak and may not be readily detected. To overcome this issue, the present paper comes up with a rolling bearing fault-diagnosing approach based on adaptive asymmetric real Laplace wavelet (ARLW) filtering, which is on the strength of water cycle optimization algorithm (WCA). Firstly, ARLW is introduced to filter the initial vibration signal since its waveform has the same asymmetric structure as the fault impact. Secondly, the optimum center frequency and bandwidth of ARLW is found out adaptively by applying the WCA through the proposed square envelope fault energy ratio (SEFER). Finally, envelope analysis is conducted to the narrowband signal obtained by the optimum ARLW filtering, and its envelope spectrum presents the rolling bearing fault characteristic frequency apparently. The proposed approach and two existing approaches are all tested in four signal analysis cases. The results are analyzed, and the conclusion is that the approach proposed by the present paper can detect the early fault of rolling bearing more accurately. The present research is valuable for diagnosing the early fault of rolling bearing.

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“…As a key component of rotating machinery, rolling bearings are responsible for ensuring the rotation accuracy of shafts and providing stable support for machines, so their running state is directly related to the performance of the entire system [ 1 , 2 , 3 ]. Severe running conditions cause bearings to be prone to various local damage faults, such as pitting, wear, and cracks, which can bring a series of adverse effects.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

Pang

Tang

Tian

et al. 2018

Sensors

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When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.

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Optimal Resonant Band Demodulation Based on an Improved Correlated Kurtosis and Its Application in Bearing Fault Diagnosis

Cited by 38 publications

References 25 publications

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

Adaptive Asymmetric Real Laplace Wavelet Filtering and Its Application on Rolling Bearing Early Fault Diagnosis

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

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