Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Li, Lingfeng; Guo, Aibin; Chen, Huayue

doi:10.1109/access.2021.3065307

Cited by 17 publications

(6 citation statements)

References 54 publications

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“…In traditional signal processing, methods such as EMD, FFT, and local mean decomposition (LMD) have found widespread application in feature extraction. In [20], a method based on maximum correlated Kurtosis deconvolution (MCKD) is proposed for extracting subtle bearing faults. Meng et al [21] combines EMD with kurtosis for bearing fault diagnosis, and in [22], generalized S-transform and sparse decomposition are utilized for fault diagnosis under variable operating conditions.…”

Section: Signal Reconstruction Methods Based On Tkeo and Ssa-vmdmentioning

confidence: 99%

“…Based on references, the relevant parameters of the SSA algorithm are initially set, such as the population size N = 50, the number of iterations T = 100, the optimization range of two crucial parameters [α, K], and other basic parameters. Specifically, the penalty factor α is optimized within the range [100, 20 000], and the number of modes K is optimized within the range [1,20]. The optimization process iteration curves for the two sets of simulated signals are shown in figure 11.…”

Section: Simulation Analysismentioning

confidence: 99%

See 1 more Smart Citation

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

Li,

Lv,

Zhou

et al. 2023

Meas. Sci. Technol.

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In addressing the problem of low prediction accuracy in the Remaining Useful Life (RUL) prediction of rolling bearings, caused by noise interference and insufficient extraction of sensitive features by deep learning models, this paper presents a life prediction method based on signal reconstruction and dual-channel network fusion. First, addressing the issue of extracting weak features from rolling bearing vibration signals, an optimized combination of Variational Mode Decomposition (VMD) and Teager-Kaiser Energy Operator (TKEO) for signal reconstruction is proposed. TKEO is used to track the energy of high-frequency impulses in the original signal. The Sparrow Search Algorithm (SSA) is employed for optimizing VMD to perform high-frequency filtering, resulting in an optimized impulse energy signal. This signal is then multiplied with the original signal to enhance weak impulse features under noisy background. Next, a dual-channel network model for Remaining Useful Life (RUL) prediction is constructed based on Temporal Convolutional Network (TCN) and Convolutional Neural Networks (CNN). The one-dimensional time-series signal obtained after reconstruction and denoising serves as the input for the TCN network channel. Simultaneously, the signal obtained after reconstruction and denoising undergoes Synchrosqueezed Wavelet Transforms (SWT) to generate a two-dimensional time-frequency representation as input for the CNN network channel. This setup allows TCN and CNN to respectively extract temporal features from the vibration signal and time-frequency features from the spectrogram. By incorporating self-attention mechanisms, internal correlations between different features are fully explored, thereby enhancing prediction accuracy.

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Section: Signal Reconstruction Methods Based On Tkeo and Ssa-vmdmentioning

confidence: 99%

Section: Simulation Analysismentioning

confidence: 99%

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

Li,

Lv,

Zhou

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Therefore, it is difficult to find the optimal threshold of the objective function, resulting in the non-ideal noise reduction effect. Continuous differentiable threshold function (CDTF) and nonlinear soft-like thresholding function (NSTF) with continuous derivatives can be described as (11) and (12) in literature [16] and [17]respectively. , ,,…”

Section: B Novel Adaptive Exponential Wavelet Threshold Functionmentioning

confidence: 99%

“…A new noise-controlled second-order enhanced stochastic resonance (SR) method based on the Morlet wavelet transform was proposed to enhance the weak fault identification, and it could extract fault feature of the looseness fault for wind turbine shaft coupling [10]. In conclusion, the above research shows that wavelet analysis is a useful and effective time-frequency method for wind turbine incipient fault detection, and it is mainly applied to feature separation and noise elimination [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Kong

Tongle

et al. 2021

IEEE Access

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Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied to the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. And the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the preexisting algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.INDEX TERMS Adaptive wavelet threshold, dynamic weight particle swarm optimization, fault diagnosis, sigmoid-based acceleration coefficients, wind turbine bearing.

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“…As a result, research into MCKD parameter selection is required. In recent year, some optimization strategies have been suggested to determine its parameters (Li et al, 2021; Zhou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A three-stage method for efficiently extracting the higher-order harmonics of bearing fault characteristic frequencies

Jiao

Yan

Pan

et al. 2022

Journal of Vibration and Control

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Weak bearing fault feature extraction (FFE) research has previously focused on bearing fault signal of transient impulse extraction whereas higher harmonic feature extraction of fault feature frequency aspects are relatively less. While traditional envelope demodulation method has certain limitations to capture the rolling bearing fault characteristic frequency of higher harmonic. To this end, combining adaptive chirp mode decomposition (ACMD), improved maximum correlation kurtosis deconvolution (IMCKD), and 1.5-dimensional Teager energy cyclo-stationary spectrum (1.5-DTECS), in the current work we propose a three-stage defect detection system. The performance of the proposed method is evaluated by analyzing the three-stage FFE (STFFE) of multiple kinds of rolling bearing fault data. The findings reveal that the three-stage fault detection approach successfully suppresses noise, highlights the fault impact, and extracts the higher order harmonics of the bearing defect characteristic frequency more effectively. The research contributes to the field of bearing fault high harmonic component extraction and provides guidance on techniques related to the extraction of bearing impulse characteristic.

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Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Cited by 17 publications

References 54 publications

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

A three-stage method for efficiently extracting the higher-order harmonics of bearing fault characteristic frequencies

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