Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao, Feng; Zhao, Rongzhen; Jia, Leilei; Wang, Xianli

doi:10.1155/2021/9914724

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…In 2020, Lu et al proposed an online BSS method with an adaptive step size based on an isometric adaptive separation method to find a valid equilibrium between the convergence rate and the steady-state error of online BSS; this method demonstrated high estimation precision [18]. In 2021, because the vibration signal of the composite fault of a rotary mechanical machine acquired in the field had a complex noise source, Feng et al addressed this problem by proposing a wavelet package analysis and fast independent component analysis extraction method for the source fault signal [9]. As the research progress discussed above indicates, various methods for signal separation have been proposed, and later generations of researchers have improved the BSS algorithm on the basis of the previous studies.…”

Section: Related Workmentioning

confidence: 99%

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Pan¹,

Li²,

Wang³

et al. 2022

ITC

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Electrical Impedance Tomography (EIT) can perform non-invasive, low-cost, safe, fast, and simple system structure and functional imaging to map the distribution and changes of root zone. Multi frequency EIT solves the problem that single-frequency EIT can only carry more impedance information than a given single excitation frequency. It still remains challenges to simultaneously obtain multi-frequency electrical impedance tomography. To address the problem, a mixed signal superimposed by multiple frequencies is injected to the object. Essentially, separating the measured mixed voltage signals, which can be used to obtain electrical impedance information at different frequencies at the same time quickly. Since the measurement signal is a multi-frequency signal, the effect of decomposing the multi-frequency signal directly affects the accuracy of imaging. In order to obtain more accurate data, this article used the variational mode decomposition (VMD) method to decompose the measured multi-frequency signal. Accurate amplitude and phase information could be obtained simultaneously at the same time in multi-frequency excitation, and these data could be used to reconstruct electrical impedance distribution The results showed that the proposed method can achieve the expected imaging effect. It was concluded that using the variational modal decomposition method to process the data of multi-frequency signals is more accurate and the imaging effect is better, and it can be applied to multi-frequency electrical impedance imaging in practice.

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Section: Related Workmentioning

confidence: 99%

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Pan¹,

Li²,

Wang³

et al. 2022

ITC

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“…If an appropriate linear transformation is applied, the sources not sparse in the time domain can be sparse in the time-frequency (TF) domain. Some algorithms for achieving sparsity in the transform domain, namely short-time Fourier transform (STFT) (Linh-Trung et al, 2005;Lu et al, 2019;Su et al, 2017) and wavelet packet transform (Li et al, 2003;Miao et al, 2021;Sadhu et al, 2011), have been proposed thus far.…”

Section: Introductionmentioning

confidence: 99%

Underdetermined Blind Source Separation of Bioacoustic Signals

Hassan¹,

Ramli²

2023

JST

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Bioacoustic signals have been used as a modality in environmental monitoring and biodiversity research. These signals also carry species or individual information, thus allowing the recognition of species and individuals based on vocals. Nevertheless, vocal communication in a crowded social environment is a challenging problem for automated bioacoustic recogniser systems due to interference problems in concurrent signals from multiple individuals. The bioacoustics sources are separated from the mixtures of multiple individual signals using a technique known as Blind source separation (BSS) to address the abovementioned issue. In this work, we explored the BSS of an underdetermined mixture based on a two-stage sparse component analysis (SCA) approach that consisted of (1) mixing matrix estimation and (2) source estimation. The key point of our procedure was to investigate the algorithm’s robustness to noise and the effect of increasing the number of sources. Using the two-stage SCA technique, the performances of the estimated mixing matrix and the estimated source were evaluated and discussed at various signal-to-noise ratios (SNRs). The use of different sources is also validated. Given its robustness, the SCA algorithm presented a stable and reliable performance in a noisy environment with small error changes when the noise level was increased.

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“…Currently, there are many denoising methods used in the analysis of vibration signal of rotating machinery, including the Fourier filter, wavelet transform (WT), and fast independent component analysis (FastICA) [ 2 , 3 , 4 ]. While these methods have achieved some success, they often encounter problems.…”

Section: Introductionmentioning

confidence: 99%

Method for Denoising the Vibration Signal of Rotating Machinery through VMD and MODWPT

Zhou

Wang

et al. 2023

Sensors

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The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal’s intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

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Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Cited by 8 publications

References 46 publications

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Underdetermined Blind Source Separation of Bioacoustic Signals

Method for Denoising the Vibration Signal of Rotating Machinery through VMD and MODWPT

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