Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

Brusa, Eugenio; Delprete, Cristiana; Gargiuli, Simone; Giorio, Lorenzo

doi:10.3390/s23010008

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…The decomposed low-frequency part is still relatively smooth and can continue to be wavelet decomposed, while the high-frequency part is increasingly detailed. The decomposed high-frequency part contains the fast changing conditions of the signal, which can capture the instantaneous behavioral characteristics of the signal [13,14]. As the number of wavelet decomposition layers increases, the variance and amplitude of the noise decreases, while the variance and amplitude of the useful signal increases, thus making the signal characteristics more pronounced.…”

Section: Wavelet Transformsmentioning

confidence: 99%

Research on the quality evaluation method of copper and aluminum diffusion bonding

Li,

Hou,

Yun

et al. 2024

Preprint

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The application of copper-aluminum welded pole pieces is extensive in the new energy manufacturing sectors, such as automotive batteries. Diffusion bonding is one of the main ways of copper-aluminum connection, which is very important for the safe use of the components in terms of the quality of its connection. First, a water-immersion ultrasonic inspection platform was built to carry out inspection research on internal defects in copper-aluminum welding. In order to address the problem of overlapping signal features at the interface, the signal is decomposed and reconstructed by applying the wavelet decomposition method using the water-immersion focusing transmission method. The reconstructed signal is then decomposed in three layers, and the energy features of the last layer are extracted to form energy feature vectors for the qualitative analysis of different defect types. Secondly, different welding area samples were prepared and the peak power spectral density was extracted as an interface characterization parameter. Tensile tests were conducted on samples with varying weld areas, and the measured maximum pull-out force was correlated with the characteristic parameters of ultrasonic testing. The results demonstrate the effective capability of wavelet packet analysis in identifying un-welded, weak bond, and micro-porous defects at the diffusion welding interface. A higher peak value of power spectral density indicates superior welding quality. Establishing a reasonable threshold for peak power spectral density can serve as a reliable criterion for assessing the condition of copper and aluminum diffusion bonding.

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Section: Wavelet Transformsmentioning

confidence: 99%

Research on the quality evaluation method of copper and aluminum diffusion bonding

Li,

Hou,

Yun

et al. 2024

Preprint

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show abstract

“…The Hamming window with a wider main lobe and lower side lobe peak value is used to add window and framing to the numerical simulation internal leakage signal of a large-diameter pipeline ball valve. The expressions of Hamming window in the time domain and frequency domain are shown in Formulas ( 16) and ( 17), respectively [30,31]:…”

Section: Windowed Fft Frequency Division Processingmentioning

confidence: 99%

Multi-Leakage Source Localization of Safety Valve Based on Uniform Circular AE Array and Improved MUSIC Algorithm

Hou

Liu

2023

Sensors

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The safety valve is the core component of the pressure-relief protection device for pressure-bearing special equipment. When the safety valve leaks, the medium of the pressure vessel will be lost and wasted, which may cause safety accidents. With the aim to solve the problem of accurately locating the multiple leakage sources of safety valves, a localization method combining a uniform circular array acoustic emission detection and an improved multiple signal classification (MUSIC) algorithm is proposed. First, an improved wavelet threshold function denoising method is introduced to extract acoustic emission signals with high SNR, thereby reducing the rank of the covariance matrix, weakening the noise dispersion caused by eigenvalue reconstruction, avoiding signal and noise cross-confusion, and improving positioning accuracy. By introducing a windowed fast Fourier transform (FFT) frequency division processing link to obtain narrowband signal, the premise of using MUSIC positioning algorithm is established. In addition, a forward/backward spatial smoothing algorithm is introduced in the decoherence link to reduce co-channel interference, reduce the rank loss of the signal covariance matrix, and improve the positioning accuracy of the algorithm. The results show that when the working pressure is 0.70 MPa, 0.75 MPa, and 0.80 MPa, the deviation between the azimuth angle and elevation angle positioning results of each leakage source obtained by the improved MUSIC algorithm and the actual angle does not exceed 2°, and the relative error does not exceed 3.5%. Therefore, the improved MUSIC algorithm can accurately locate multiple leakage sources of the safety valve, and as the working pressure of the safety valve increases, the positioning accuracy of the improved MUSIC algorithm also increases accordingly.

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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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Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

Cited by 12 publications

References 31 publications

Research on the quality evaluation method of copper and aluminum diffusion bonding

Research on the quality evaluation method of copper and aluminum diffusion bonding

Multi-Leakage Source Localization of Safety Valve Based on Uniform Circular AE Array and Improved MUSIC Algorithm

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

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