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

Wan, Shuting; Peng, Bo

doi:10.1155/2019/7475868

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Although Kurtogram and its improved methods can remove fault-independent noise and harmonic interference from vibration signals, there is still a problem with the accuracy of filter construction, which may lead to the loss of the signal information and affect the extraction of fault-related pulses. As opposed to the traditional method of dividing the frequency band layer by layer, the optimal wavelet filter methods are proposed [51][52][53][54][55]. Tse et al [51] used the Morlet wavelet as the filter, took maximizing sparsity of the filtered signal as the objective, and applied a genetic algorithm (GA) to locate the center frequency and bandwidth of the optimal Morlet wavelet for automatic filter construction.…”

Section: Filter-based Fault Detection Methodsmentioning

confidence: 99%

A Survey on Fault Diagnosis of Rolling Bearings

Peng

Xue

et al. 2022

Algorithms

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The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field.

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Section: Filter-based Fault Detection Methodsmentioning

confidence: 99%

A Survey on Fault Diagnosis of Rolling Bearings

Peng

Xue

et al. 2022

Algorithms

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“…Contact Angle (°) 9 7.94 39.04 0 Figure 8 displays the waveform of experimental signal as well as its spectra. There exists obvious impact phenomenon in the waveform, while the interval between adjacent impacts isn't the reciprocal of any fault feature frequency.…”

Section: Number Of Balls Diameter Of Balls (Mm) Pitch Diameter (Mm)mentioning

confidence: 99%

“…EMD has the unresolved problems of noise sensibility, endpoint divergence and aliasing effect, which may cause the obtained intrinsic mode function components losing specific meanings [8]. As for SK, the obtained parameters for filter design may be unreasonable due to random impact interference, and the pass-band of constructed filter may not cover the whole resonant region by reason of fixed tiling pattern [9].Considering the spreading effect of complicated transmission path of unknown time-variant system, the blind deconvolution operation is investigated to recover the impulsive features from the acquired signal. 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].…”

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confidence: 99%

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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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“…Although such improvements can improve the identification accuracy, the resonance frequency band is easily segmented by the adjacent subset because of the pyramid frequency segmentation method. Su et al [ 18 ], Chen et al [ 19 ], and Wan et al [ 20 ] utilized the genetic algorithm, particle swarm optimization algorithm, and water cycle algorithm to determine the function of the Morlet wavelet and asymmetric real Laplace wavelet, and constructed the bandpass filter with an arbitrary Q quality factor to realize adaptive matching of the resonance frequency band. However, such improvements require a lot of prior knowledge, as the optimization algorithms are relatively complex.…”

Section: Introductionmentioning

confidence: 99%

An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

Wan

Peng

2019

Entropy

Self Cite

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Early fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal. Firstly, the paper introduces frequency window, which can slip on the frequency axis and extract the frequency band. Secondly, the double time domain feature entropy is proposed to evaluate the strength of periodic components in signal. The location of the optimal frequency window covering the resonance band caused by bearing fault is determined adaptively by this entropy index and the shifting/expanding frequency window. Thirdly, the signal corresponding to the optimal frequency window is reconstructed, and it is further filtered by the sparse coding shrinkage algorithm to highlight the impact feature and reduce the residue noise. Fourthly, the de-noised signal is demodulated by envelope operation, and the corresponding envelope spectrum is calculated. Finally, the bearing failure type can be judged by comparing the frequency corresponding to the spectral lines with larger amplitude in the envelope spectrum and the fault characteristic frequency. Two bearing vibration signals are applied to validate the proposed method. The analysis results illustrate that this method can extract more failure information and highlight the early failure feature. The data files of Case Western Reserve University for different operation conditions are used, and the proposed approach achieves a diagnostic success rate of 83.3%, superior to that of the AFW method, SCS method, and Fast Kurtogram method. The method presented in this paper can be used as a supplement to the early fault diagnosis method of rolling bearings.

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

Cited by 6 publications

References 30 publications

A Survey on Fault Diagnosis of Rolling Bearings

A Survey on Fault Diagnosis of Rolling Bearings

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

An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

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