Noise reduction and feature enhancement of hob vibration signal based on parameter adaptive VMD and autocorrelation analysis

Yuan, Yanfei; Yan, Ping; Zhou, Han; Huang, Qin; Wu, Dayuan; Zhu, Junhan; Zhongming, N

doi:10.1088/1361-6501/ac8e23

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…It also makes GWL data sensitive to noise by dividing it into various IMFs and residues. The flexibility of the VMD model proves to be beneficial in modelling studies with limited data, such as GWL, as evidenced by previous research (Singh & Kaur, 2022; Taran & Bajaj, 2018; Yuan et al, 2022).…”

Section: Discussionmentioning

confidence: 83%

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Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Gezici,

Katipoğlu,

Şengül

2024

Hydrological Processes

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Water scarcity is a pressing issue, intensified by factors such as population growth and industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater resources, which are essential sources of clean and usable water. This study examines changes in groundwater levels (GWLs) in northeastern Turkey's mountainous and snow‐covered area. The primary objective is to assess the effectiveness of integrated machine learning models, specifically, the extreme learning machine (ELM) technique combined with signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and empirical wavelet transform (EWT) for monthly GWL prediction models. Seventy percent of the accessible data is allocated for training, while 30% is designated for testing. A correlation matrix involving precipitation, temperature, relative humidity and GWL parameters is generated with inputs that possess significant correlations being selected, such as GWLt−1, GWLt−2, RHt, RHt−1 and RHt−2. To evaluate model results, various metrics, including mean squared error, mean absolute error, mean absolute percentage error, mean bias error, bias factor, determination coefficient, Nash‐Sutcliffe efficiency, as well as tools such as box plots, Taylor diagrams and radar charts, are utilized to compare outcomes during the interpretation phase. The results of the analyses show that applying data decomposition methods such as EEMD, VMD and EWT significantly improves the performance of the ELM algorithm in predicting GWLs. VMD‐ELM is the most accurate for GWL forecasting among the approaches examined. The R2 values of the most successful models established in the two wells are 0.993 and 0.905. The outcomes of this research hold significance for decision‐makers and policymakers as it offers informative insights into aquifer surveillance, irrigation strategizing and efficient administration of water resources.

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Section: Discussionmentioning

confidence: 83%

“…The assessment of GWL prediction accuracy for the models listed in (Singh & Kaur, 2022;Taran & Bajaj, 2018;Yuan et al, 2022).…”

Section: Effectiveness Of Vmd-elm Hybrid Approachmentioning

confidence: 99%

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Gezici,

Katipoğlu,

Şengül

2024

Hydrological Processes

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“…Common denoising algorithms include empirical mode decomposition (EMD) [17][18][19], variational mode decomposition (VMD) [20][21][22], wavelet transform (WT) [23][24][25], and additional techniques. While these techniques demonstrate positive results in denoising, numerous challenges remain that necessitate resolution.…”

Section: Introductionmentioning

confidence: 99%

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Deng,

Wang

2024

Meas. Sci. Technol.

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To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

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“…Fault features can be extracted from vibration signals and used to evaluate the health condition of the machines. However, due to the complex and harsh operating environments of the rotating machinery, the collected vibration signals often contain a lot of noise, which interferes with the feature extraction results and the accuracy of fault diagnosis [6,7].…”

Section: Introductionmentioning

confidence: 99%

A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index

Zhou¹,

Zhang²,

Lu³

et al. 2023

Meas. Sci. Technol.

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Feature extraction from vibration signals plays a vital role in rotating machinery fault diagnosis. The noise contained in the signals will interfere with the fault feature extraction result. Wavelet denoising is a commonly used method to reduce the noise, but its parameters are generally selected based on subjective experience. With this problem in mind, an adaptive wavelet denoising method is proposed in this paper. Using permutation entropy to evaluate the signal noise level and taking its minimum value as the fitness function, the intelligent optimization algorithm is applied to optimize the wavelet denoising parameters. Based on this adaptive wavelet denoising method and a synthetic detection index, a new feature extraction approach is proposed. Results from simulation experiments and engineering applications prove that the signal denoising performance of the adaptive wavelet denoising method and the results of the fault feature extraction approach are satisfactory.

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Noise reduction and feature enhancement of hob vibration signal based on parameter adaptive VMD and autocorrelation analysis

Cited by 10 publications

References 40 publications

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index

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