Iterative Morlet wavelet with SOSO boosting strategy for impulsive feature extraction

Yang, Lei; Duan, Rongkai; Kang, Tao; Li, Jiaqi; Liao, Yuhe

doi:10.1016/j.measurement.2022.110965

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Yet, the fixed shape of the wavelet filter restricts the elimination of in-band noise. In light of the strengthen-operatedenosing-subtract-strengthen boosting strategy which has been proven to be effective in denoising [28], the latter is designed to further removes in-band noise and enhances the in-band features.…”

Section: Frequency Band Selectionmentioning

confidence: 99%

“…Commonly used criteria such as kurtosis, Shannon entropy [25], smoothness index [26], and peak energy [27] are used to assess filtered signals and systematically identify the most effective frequency band. Yang et al [28] adopted the lifted correlation kurtosis as the criterion for determining the most suitable filter parameters for the Morlet filter and adaptively output the optimal result. Afterward, some intelligent optimization algorithms have been introduced to seek fault information bands with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Shi,

Miao,

Xia

et al. 2024

Meas. Sci. Technol.

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Locating the informative frequency band of rolling bearing fault signals is of great significance for feature extraction and fault diagnosis. Benefiting from the adjustable center frequency and bandwidth as well as the similarity to impulse-like characteristics induced by bearing failures, Morlet wavelets are commonly used in resonance demodulation. However, the fault impulses are completely masked by the noise at a very low signal-to-noise ratio (SNR), which imposes many limitations on the existing wavelet parameter selection strategies and frequency band optimization methods. In this paper, an adaptive Morlet wavelet-based iterative filtering (AMIF) method is proposed for frequency band optimization under low SNR. The resonance frequency band is pinpointed based on adaptive Morlet wavelet filter banks, with off-band noise being canceled and fault features being refined during the filtering process. Additional iterative operations are leveraged to enhance fault features of in-band signals to facilitate the optimization of the filtering parameters. The effectiveness of the proposed AMIF method and its superiority over the WPT-based Kurtogram are verified through simulation and experimental analysis. According to the results, AMIF can effectively isolate the impulsive fault features even from overwhelming background noise.

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Section: Frequency Band Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Shi,

Miao,

Xia

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…As gear and bearing fault vibration signals present impactresponse characteristics, different wavelet basis functions have been applied for gear and bearing fault signal analysis and diagnosis, such as Laplace wavelets [23], Morlet wavelets [24], Mexihat wavelets [25], complex Morlet wavelets [26], etc. However, the performance of these different wavelet functions in deep network feature extraction needs to be evaluated and discussed.…”

Section: Feature Visualization Analysismentioning

confidence: 99%

Rotating machinery fault diagnosis based on impact feature extraction deep neural network

Aijun¹,

Sun²,

Xiang³

et al. 2022

Meas. Sci. Technol.

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Gears and bearings are important components in rotating machinery and are crucial for the safety and operation of the whole mechanical system. Intelligent fault diagnosis methods based on deep-learning algorithms have undergone rapid development in recent years. Despite this, integrating fault features in a deep network construction remains a challenge for intelligent fault diagnosis of rotating machinery. In this paper, a novel impact feature extraction deep neural network (IFE-DN) is proposed for intelligent gear and bearing fault diagnosis. An improved three-layer Laplace wavelet kernel convolutional neural network (LW-CNN), where the Laplace wavelet kernel is designed in the first convolutional layer, is constructed to extract and enhance the impact features in the vibration signal. Using a visualized heat map, the physical meaning of the LW-CNN’s extracted features is explained and the interpretability of the network model is enhanced. The wavelet function selection in the deep neural network is also discussed. The extracted features are transferred to a primary capsule layer and a digital capsule layer. With a feature vector converting process and dynamic routing algorithm, more detailed features are optimized and the fault types are classified. Four experimental data sets from different laboratories are used to verify the performance of the proposed model, and t-distributed stochastic neighbour embedding is carried out to visually analyze the extracted features in different layers. The results of the analysis of gear and bearing faults of different types and defect sizes show that the IFE-DN presents significant accuracy and satisfactory generalization ability.

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CF-HSACNN: A joint anti-noise learning framework for centrifugal fan state recognition

Fan

Wang

et al. 2022

Measurement

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Iterative Morlet wavelet with SOSO boosting strategy for impulsive feature extraction

Cited by 5 publications

References 43 publications

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Rotating machinery fault diagnosis based on impact feature extraction deep neural network

CF-HSACNN: A joint anti-noise learning framework for centrifugal fan state recognition

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