SSWT and VMD Linked Mode Identification and Time-of-Flight Extraction of Denoised SH Guided Waves

Huang, Songling; Sun, Hongyu; Wang, Shen; Qu, Kaifeng; Zhao, Wei; Peng, Lisha

doi:10.1109/jsen.2021.3051658

Cited by 27 publications

(13 citation statements)

References 33 publications

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“…Based on the matching pursuit and density peak clustering algorithm, the aliasing guided wave signal can be separated and the Method comparison. In order to verify the accuracy and advantages of the above method, it is compared with the modal extraction algorithm of VMD-SSWT proposed by Huang et al (2021).…”

Section: The Verification Of Methodsmentioning

confidence: 99%

“…The five modes are arranged according to the speed from large to small: S2, A0, S0, A1, and A2. At the same distance, Huang et al (2021) considered that the faster the modal speed of IMF, the shorter the peak time of IMF is. From Figures 10 and 13(c), (f), and (i), we can know that IMF2 is S2 mode, IMF3 is A0 mode, and IMF4 is S0 mode.…”

Section: The Verification Of Methodsmentioning

confidence: 99%

“…This method converts one-dimensional guided wave signal to two-dimensional image analysis, which increases the amount of calculation. Huang et al (2021) used vibrational mode decomposition (VMD) algorithm for modal identification and separation of SH guided waves. Although VMD can adaptively decompose signals in different frequency bands, its decomposition accuracy does not necessarily satisfy the requirements of identifying guided wave modes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of weld defects using ultrasonic-guided waves based on matching pursuit and density peak clustering

Mao

Ren

Tang

et al. 2023

Transactions of the Institute of Measurement and Control

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Due to the fish scale surface of the weld seam, the guided wave dispersion is intensified, resulting in serious mode aliasing problem of the detected signal. It is difficult to analyze defect echo signal and locate defect accurately. To solve this problem, a new method of ultrasonic guided wave detection is proposed for weld defects based on matching pursuit and density peak clustering. First, according to the characteristics of the guided wave echo signal, a matching pursuit algorithm based on Morlet wavelet dictionary is established. The time-frequency analysis and parameter analysis of the obtained wavelet atoms are carried out to realize the modal separation and identification of the guided wave signal. Then, the similarity weight is introduced into the density peak algorithm to cluster the atoms obtained by sparse decomposition. The obtained clustering center is used to locate the weld defect. The validity of the method is proved by simulation and experiment. Finally, the experimental results show that the positioning error is 0.261% when the proposed method is used to detect the weld defects of 3-mm wide steel plate.

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Section: The Verification Of Methodsmentioning

confidence: 99%

Section: The Verification Of Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of weld defects using ultrasonic-guided waves based on matching pursuit and density peak clustering

Mao

Ren

Tang

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…Variational Mode Decomposition (VMD) is an advanced signal processing method capable of decomposing a signal into several intrinsic mode functions (IMFs) [ 18 ]. Compared with empirical mode decomposition (EMD), VMD effectively suppresses mode aliasing and improves the quality of decomposition [ 19 ]. However, the mode number K and quadratic penalty term α, predefined in VMD, strongly influence the decomposition and are difficult to determine [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Single-Sensor Engine Multi-Type Fault Detection

Tang

Cheng

et al. 2023

Sensors

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Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements.

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“…VMD adaptively separates the frequency domain and modal components of the signal, under the condition that the sum of the estimated bandwidths of each component is minimized. Huang et al [12] proposed a method to denoise the original signal by combining VMD and simultaneous wavelet transform (SSWT) algorithms. Zhong et al [13] proposed a new method to distinguish foreign object debris (FOD) signals from clutter signals, based on optimal VMD and support vector data description (SVDD).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

et al. 2021

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With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.

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SSWT and VMD Linked Mode Identification and Time-of-Flight Extraction of Denoised SH Guided Waves

Cited by 27 publications

References 33 publications

Detection of weld defects using ultrasonic-guided waves based on matching pursuit and density peak clustering

Detection of weld defects using ultrasonic-guided waves based on matching pursuit and density peak clustering

Single-Sensor Engine Multi-Type Fault Detection

Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

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