A Mixed-Mode Decomposition Denoising Algorithm Based on Variance Estimation

Cai, Rong; Wang, Rongkun; Sun, Sigun; Yang, Bo; Dong, Xiaofeng; Xie, Feng; Lian, Jianyang

doi:10.1007/s00034-022-02161-w

Cited by 4 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ICEEMDAN signal processing method is an improvement over CEEMDAN [20][21][22][23][24]. Unlike the traditional EMD method, ICEEMDAN incorporates adaptive noise and a complete ensemble strategy to enhance the stability and accuracy of the decomposition process.…”

Section: Iceemdan Algorithmmentioning

confidence: 99%

“…It can suppress mode mixing and endpoint effects and improve denoising effectiveness through an adaptive noise model [5,7,9]. However, it still faces issues such as residual noise and pseudo-modes [20][21][22]. Marcelo A. Colominas [23] proposed an improved denoising method called improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which is based on CEEMDAN and addresses some of its limitations [20][21][22]24].…”

Section: Introductionmentioning

confidence: 99%

“…However, it still faces issues such as residual noise and pseudo-modes [20][21][22]. Marcelo A. Colominas [23] proposed an improved denoising method called improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which is based on CEEMDAN and addresses some of its limitations [20][21][22]24]. Unlike CEEMDAN, ICEEMDAN incorporates white noise as part of the complete noise ensemble instead of directly adding Gaussian white noise.…”

Section: Introductionmentioning

confidence: 99%

“…It can effectively detect subtle information variations in signals under complex backgrounds and is widely used in denoising and feature extraction [28][29][30][31][32]. In recent years, nonstationary vibration signal noise reduction via CEEMDAN-PE, ICEEMDAN-PE, and CEEMDAN-SVD has been used for the vibration feature extraction of hydropower units [7], underwater acoustic monitoring denoising [9], ball mill barrel vibration signal denoising [18], heart and brain electrical signal noise filtering [19], weld signal denoising [21], coal machine and gas signal denoising [33,34], building structure vibration signal denoising [26,35], parameter prediction [22,24,36,37] and gear-bearing fault diagnosis [31,[38][39][40][41][42][43]. The initial applications of these methods have made good progress, but there is still a need to improve the vibration signal denoising of hydropower units.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Zhang

Guo

Yuan

et al. 2023

Sensors

View full text Add to dashboard Cite

Vibration monitoring and analysis play a crucial role in the fault diagnosis of hydroelectric units. However, accurate extraction and identification of fault features from vibration signals are challenging because of noise interference. To address this issue, this study proposes a novel denoising method for vibration signals based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy (PE), and singular value decomposition (SVD). The proposed method is applied for the analysis of hydroelectric unit sway monitoring. Firstly, the ICEEMDAN method is employed to process the signal and obtain several intrinsic mode functions (IMFs), and then the PE values of each IMF are calculated. Subsequently, based on a predefined threshold of PE, appropriate IMFs are selected for reconstruction, achieving the first denoising effect. Then, the SVD is applied to the signal after the first denoising effect, resulting in the SVD spectrum. Finally, according to the principle of the SVD spectrum and the variation in the singular value and its energy value, the signal is reconstructed by choosing the appropriate reconstruction order to achieve the secondary noise reduction effect. In the simulation and case analysis, the method is better than the commonly used wavelet threshold, SVD, CEEMDAN–PE, and ICEEMDAN–PE, with a signal-to-noise ratio (SNR) improvement of 6.9870 dB, 4.6789 dB, 8.9871 dB, and 4.3762 dB, respectively, and where the root-mean-square error (RMSE) is reduced by 0.1426, 0.0824, 0.2093 and 0.0756, respectively, meaning that our method has a better denoising effect and provides a new way for denoising the vibration signal of hydropower units.

show abstract

Section: Iceemdan Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Zhang

Guo

Yuan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Similar with WTD, the key of the EMD denoising algorithm is to select and integrate qualified IMF components. For this reason, the wavelet threshold EMD denoising method, 55 randomized-order EMD, 56 probability density functions based EMD, 57 and other improved algorithms 58 – 62 are devoted to optimizing the threshold determination criterion and signal reconstruction methods to enhance their noise reduction capabilities.…”

Section: Optimal Background Clutter Suppression Scheme Designmentioning

confidence: 99%

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Wang

Weng

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.Impulse radio ultrawide band (IR-UWB) radar possesses several advantages, such as heat flow insensitivity, high resolution, great penetration, good mobility, and lightweight, making it a promising technology for detecting and rescuing individuals trapped in fire scenarios. However, reliable detection and extraction of vital signs of trapped people in the presence of complex fire environment, such as water droplets, smoke, wall occlusions, and reflections, require an optimal background clutter suppression scheme designed specifically for fire scenarios. This study comprehensively evaluates 20 background noise suppression schemes using an exhaustive approach that involves constructing radar echo simulation signals and designing an evaluation method for algorithms. The practical applicability of the improved design solutions was evaluated by conducting small-scale fire experiments in an acrylic chamber. The results indicate that the designed scheme is effective in overcoming the background interference from the combustion environment and is capable of supporting subsequent trajectory identification and vital sign extraction in the fire scene environment.

show abstract

Adaptive Gaussian Filter Based on ICEEMDAN Applying in Non-Gaussian Non-stationary Noise

Zhang,

Xu,

Yang

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

A Mixed-Mode Decomposition Denoising Algorithm Based on Variance Estimation

Cited by 4 publications

References 17 publications

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Research on Denoising Method for Hydroelectric Unit Vibration Signal Based on ICEEMDAN–PE–SVD

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Adaptive Gaussian Filter Based on ICEEMDAN Applying in Non-Gaussian Non-stationary Noise

Contact Info

Product

Resources

About