scholarly journals Research on the Noise Reduction Method of the Vibration Signal of the Hydrogenerator Unit Based on ITD-PE-SVD

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Ren ◽  
Pan Liu ◽  
Leiming Hu ◽  
Ruoyu Qiao ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Electromagnetic Interference ◽  
Reduction Method ◽  
Recognition Rate ◽  
Difference Spectrum ◽  
Singular Value ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Signal Features ◽  
Reduction Effect

Aiming at the problem that the vibration signals of the hydrogenerator unit are nonlinear and nonstationary and it is difficult to extract the signal features due to strong background noise and complex electromagnetic interference, this paper proposes a dual noise reduction method based on intrinsic time-scale decomposition (ITD) and permutation entropy (PE) combined with singular value decomposition (SVD). Firstly, the vibration signals are decomposed by ITD to obtain a series of PRC components, and the permutation entropy of each component is calculated. Secondly, according to the set permutation entropy threshold, the PRC components are selected for reconstruction to achieve a noise reduction effect. On this basis, SVD is carried out, and the appropriate reconstruction order is selected according to the position of the singular value difference spectrum mutation point for reconstruction, so as to achieve the secondary noise reduction effect. The proposed method is compared with the LMD-PE-SVD and EMD-PE-SVD dual noise reduction method by simulation, taking the correlation coefficient and signal-to-noise ratio to evaluate the noise reduction performance and finding that the ITD-PE-SVD noise reduction has good noise reduction and pulse effect. Furthermore, this method is applied to the analysis of the upper guide swing data in the X-direction and Y-direction of a unit in a hydropower station in China, and it is found that this method can effectively reduce noise and accurately extract signal features, thus determining the vibration cause, which is helpful to improve the turbine fault recognition rate.

scholarly journals Research on Noise Reduction Method of Pressure Pulsation Signal of Draft Tube of Hydropower Unit Based on ALIF-SVD

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Ren ◽  
Pan Liu ◽  
Leiming Hu ◽  
Jin Huang ◽  
Ruoyu Qiao ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Pressure Fluctuation ◽  
Reduction Method ◽  
Draft Tube ◽  
Sample Entropy ◽  
Singular Value ◽  
Hydropower Station ◽  
Stable Operation ◽  
Reconstructed Signal ◽  
Reduction Effect

Aiming at the problem that it is difficult to extract the characteristics of the draft tube pressure fluctuation signal under the background of strong noise, this study proposes a dual noise reduction method based on adaptive local iterative filtering (ALIF) and singular value decomposition (SVD). First, perform ALIF decomposition of the signal to be decomposed to obtain a series of IMF components, calculate the sample entropy of each component, select some IMF components to reconstruct according to the set sample entropy threshold, and then perform SVD decomposition on the reconstructed signal, and according to the location of the singular value difference spectrum mutation point, the appropriate number of reconstructions is selected for reconstruction, so as to achieve the double noise reduction effect. The ALIF-SVD dual noise reduction method proposed in this study is compared with the single ALIF, EMD, and EMD-SVD dual noise reduction method through simulation, and the correlation coefficient, signal-to-noise ratio, and mean square error are used to evaluate the noise reduction. It is found that the ALIF-SVD dual noise reduction method avoids the phenomenon of modal aliasing in the decomposition process, effectively removes the noise, and can retain the useful information of the original signal, and the noise reduction effect is better. A unit of a hydropower station in China is further selected as the research object, and its draft tube pressure fluctuation data were analyzed for noise reduction. It was found that this method can accurately extract the signal characteristics under strong noise background, so as to determine the type of pressure fluctuation of the unit, which is helpful to improve the fault recognition rate of hydraulic turbines. And it provides some technical support for the safe and stable operation of hydropower units and the promotion of condition-based maintenance strategy and improves the intelligent level of hydropower station operation management.

scholarly journals A Novel Noise Reduction Method of UAV Magnetic Survey Data Based on CEEMDAN, Permutation Entropy, Correlation Coefficient and Wavelet Threshold Denoising

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1309
Author(s):  
Yaoxin Zheng ◽  
Shiyan Li ◽  
Kang Xing ◽  
Xiaojuan Zhang
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Field Experiments ◽  
Reduction Method ◽  
Magnetic Data ◽  
Permutation Entropy ◽  
Magnetic Survey ◽  
Intrinsic Mode Functions ◽  
Real Signal ◽  
The Real

Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction method of UAV magnetic data based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), correlation coefficient and wavelet threshold denoising. The original signal is first decomposed into several intrinsic mode functions (IMFs) by CEEMDAN, and the PE of each IMF is calculated. Second, IMFs are divided into four categories according to the quartiles of PE, namely, noise IMFs, noise-dominant IMFs, signal-dominant IMFs, and signal IMFs. Then the noise IMFs are removed, and correlation coefficients are used to identify the real signal-dominant IMFs. Finally, the wavelet threshold denoising is applied to the real signal-dominant IMFs, the denoised signal can be obtained by combining the signal IMFs and the denoised IMFs. Both synthetic and field experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can eliminate the interference to a great extent, which lays a foundation for the further interpretation of UAV magnetic data.

scholarly journals Intelligent alarm data noise reduction for power systems based on singular value decomposition

2021 ◽  
Vol 2108 (1) ◽  
pp. 012074
Author(s):  
Chen Chen ◽  
Hongren Man ◽  
Xiu Liu
Keyword(s):  
Data Structure ◽  
Singular Value Decomposition ◽  
Power System ◽  
Noise Reduction ◽  
Reduction Method ◽  
Singular Value ◽  
Data Noise ◽  
Noise Data ◽  
Value Decomposition ◽  
Noise Statistics

Abstract The noise types of power system intelligent alarm data are complex. When reducing the intelligent alarm data, the profile noise statistics of the noise data are large, resulting in the actual noise reduction value is too small. To solve this problem, a power system intelligent alarm data noise reduction method based on singular value decomposition is designed. The selected normalized decomposition matrix iteratively processes the original matrix, the singular value decomposes the power system alarm data, sets an estimation quantity within the paradigm of the alarm data, controls the noise profile noise statistics, characterizes the noise alarm data structure, uses the SC algorithm to process the cluster basis vectors in the noise data structure, and constructs a repeated iterative convergence process to realize intelligent data noise reduction processing. The original alarm data within a known power system is used as test data, the power system alarm window is set, and the power system alarm data singular values are circled. The data mining-based alarm data noise reduction method, the regularized filter-based alarm data noise reduction method and the designed data noise reduction method are applied to the noise reduction process, and the results show that the designed data noise reduction method has the largest noise value and the best noise reduction effect.

scholarly journals A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 624 ◽  
Author(s):  
Zhe Chen ◽  
Yaan Li ◽  
Renjie Cao ◽  
Wasiq Ali ◽  
Jing Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Noise Reduction ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Normalized Mutual Information ◽  
Radiated Noise ◽  
New Feature

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

scholarly journals Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

2020 ◽  
Vol 10 (4) ◽  
pp. 1409
Author(s):  
Gang Zhang ◽  
Benben Xu ◽  
Kaoshe Zhang ◽  
Jinwang Hou ◽  
Tuo Xie ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Noise Pollution ◽  
Singular Value ◽  
Reduction Model ◽  
Mode Decomposition ◽  
Starting Point ◽  
Reduction Effect ◽  
Value Decomposition

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 918 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Pearson Correlation ◽  
Low Frequency ◽  
Acoustic Signals ◽  
Permutation Entropy ◽  
Underwater Acoustic ◽  
Mode Decomposition

Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 367 ◽  
Author(s):  
Guiji Tang ◽  
Tian Tian
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Excellent Performance ◽  
Spectral Correlation ◽  
Vibration Signals ◽  
Order Energy ◽  
Fault Features ◽  
Resonance Band ◽  
Reduction Effect

Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.

scholarly journals Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

2019 ◽  
Vol 9 (10) ◽  
pp. 2102 ◽  
Author(s):  
Lingjun Xiao ◽  
Yong Lv ◽  
Guozi Fu
Keyword(s):  
Noise Reduction ◽  
Projection Method ◽  
Experimental Studies ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Subspace Projection ◽  
Vibration Signals ◽  
Mechanical Signals ◽  
Rotary Machinery

Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.

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