Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis

Zheng, Jinde; Pan, Haiyang; Su, Yongsheng; Cheng, Junsheng

doi:10.1016/j.sigpro.2016.07.023

Cited by 147 publications

(66 citation statements)

References 26 publications

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“…In order to test the effectiveness of the proposed method, experiments were conducted on the dataset provided by the laboratory of contact and structure mechanics at INSA Lyon, France [25]. Experiments of the proposed method and detailed comparison with another widely used methods HHT-SVD, LMD-SVD and WPT together with PCA is also conducted as follows.…”

Section: Resultsmentioning

confidence: 99%

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Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Rahmoune¹,

Merainani²,

Benazzouz³

et al. 2018

J. vibroeng.

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Abstract. Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction.

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Section: Resultsmentioning

confidence: 99%

“…In recent years, Gilles, [23] developed the empirical wavelet transform (EWT) [24,25]. The uniqueness of this method is in building an adaptive wavelet filter bank capable of extracting amplitude modulated-frequency modulated (AM-FM) components of a signal.…”

Section: Introductionmentioning

confidence: 99%

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Rahmoune¹,

Merainani²,

Benazzouz³

et al. 2018

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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“…To solve the above problems, scholars have done considerable research on noise filtering and improvement of the signal-to-noise ratio (SNR) of useful signals and have proposed various methods. Traditional signal processing methods include wavelet analysis [5,6], empirical mode decomposition [7,8], Hilbert transform [9,10], and singular value decomposition [11,12]. Because noise plays a negative role in most cases, traditional methods mostly focus on filtering out noise.…”

Section: Introductionmentioning

confidence: 99%

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Han

Wang

et al. 2019

Shock and Vibration

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Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

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Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis

Cited by 147 publications

References 26 publications

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

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