Analysis of hydropower unit vibration signals based on variational mode decomposition

2015 ◽  
Vol 23 (12) ◽  
pp. 1938-1953 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan ◽  
Fei Zhang
Keyword(s):  
Hydraulic Turbine ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Accurate Analysis ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Face ◽  
Band Limited ◽  
Nonlinear Signal

The vibration signals of hydropower units are nonstationary when serious vortex occurs in the draft tube of the hydraulic turbine. The traditional signal analysis method based on Fourier transform is not suitable for the nonstationary signals. In the face of the nonstationarity of such signals and the limitation of the empirical mode decomposition method, a new nonstationary and nonlinear signal analyzing method based on variational mode decomposition (VMD) is introduced into hydropower unit vibration signals analysis. Firstly, VMD is used to decompose the signal into an ensemble of band-limited intrinsic mode functions components. Then, frequency spectrum analysis of these components is conducted to obtain the characteristic frequencies of the signal caused by the serious vortex of hydraulic turbine. Analysis of real test data shows that this proposed method can effectively suppress mode mixing. It can realize accurate analysis of nonstationary vibration signals. This provides a new way for analyzing hydropower unit vibration signals.

Vibration signal analysis of a hydropower unit based on adaptive local iterative filtering

2016 ◽  
Vol 231 (7) ◽  
pp. 1339-1353 ◽  
Author(s):  
Xueli An ◽  
Weiwei Yang ◽  
Xuemin An
Keyword(s):  
Vibration Signal ◽  
Hydraulic Turbine ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Nonstationary Vibration ◽  
Empirical Mode Decomposition Method ◽  
Vibration Signal Analysis ◽  
Iterative Filtering ◽  
Strong Vortex

The vibration signals coming from a hydropower unit have strong nonstationary characteristics when strong vortex develops in the hydraulic turbine draft tube. Related to this problem, a new vibration analysis method for a hydropower unit based on adaptive local iterative filtering is proposed. Firstly, adaptive local iterative filtering was used to decompose the complex vibration signal into several intrinsic mode functions. Then, frequency spectrum analysis of these components was performed to obtain the vortex characteristic frequency from the vibration signal. Simulated and real-world signals were used to verify the proposed method. The obtained results show that this method can overcome the problem of mode mixing in the existing empirical mode decomposition method, since it improves the efficiency and accuracy of feature extraction for nonstationary vibration signals from a hydropower unit.

scholarly journals Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

2019 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
Author(s):  
Weifang Zhang ◽  
Meng Zhang ◽  
Yan Zhao ◽  
Bo Jin ◽  
Wei Dai
Keyword(s):  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Noisy Signals ◽  
Band Limited ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

Well-log decomposition using variational mode decomposition in assisting the sequence stratigraphy analysis of a conglomerate reservoir

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B221-B228 ◽  
Author(s):  
Zhaohui Xu ◽  
Bo Zhang ◽  
Fangyu Li ◽  
Gang Cao ◽  
Yuming Liu
Keyword(s):  
Sequence Stratigraphy ◽  
Oil Field ◽  
Well Logs ◽  
Bohai Bay ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Sequence Identification ◽  
Band Limited ◽  
System Tracts

Sequence stratigraphy analysis is one of the most important tasks in evaluating and characterizing the reservoir system within a basin. However, it is very hard to identify the system tracts and lithofacies using well logs for the conglomerate reservoirs because of the strong lithology heterogeneity. Based on the fact that the system tracts and lithofacies usually illustrate cycle features within the basin, we decompose the well logs into different intrinsic modes to characterize the sequence units and lithofacies at different scale. First, we analyze the log response to lithologies to determine the well logs used for sequence analysis. Then, we use variational mode decomposition to decompose the selected well logs into an ensemble of different band-limited intrinsic mode functions, each with its center wavenumber. Finally, we interpret the sequence stratigraphy and lithofacies using corresponding decomposed modes. We validate the effectiveness of our method in the lithofacies and sequence identification for a conglomerate reservoir in the Shengli oil field, Bohai Bay Basin, east China. The decomposed intrinsic modes with a larger center wavenumber perfectly characterize the sequence units at a larger scale, whereas the decomposed intrinsic modes with a smaller center wavenumber reveal the lithofacies changes at a smaller scale. The application illustrates that it is much more convenient and easier for sequence stratigraphy analysis to integrate the original and decomposed logs.

scholarly journals Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 470
Author(s):  
Zijian Guo ◽  
Mingliang Liu ◽  
Huabin Qin ◽  
Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Recognition Rate ◽  
Rotating Machinery ◽  
Bootstrap Sample ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.

scholarly journals Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1567
Author(s):  
Ragavesh Dhandapani ◽  
Imene Mitiche ◽  
Scott McMeekin ◽  
Venkateswara Sarma Mallela ◽  
Gordon Morison
Keyword(s):  
Performance Metrics ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Sensor Data ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Band Limited

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

scholarly journals Order Tracking Using Variational Mode Decomposition to Detect Gear Faults under Speed Fluctuations

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Madhurjya Dev Choudhury ◽  
Liu Hong ◽  
Jaspreet Singh Dhupia
Keyword(s):  
Fault Detection ◽  
Reference Signal ◽  
Vibration Signal ◽  
Operating Speed ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Order Tracking ◽  
Mode Decomposition ◽  
Gearbox Vibration

Fault detection in gearboxes plays a significant role in ensuring their reliability. Vibration signals collected during gearbox operation contain a wealth of valuable condition information that can be exploited for fault detection. However, in an industrial environment machine operating speed always fluctuates around its nominal value, which causes smearing of the gearbox vibration spectrum. Considering operating speed fluctuation and multi-component nature of measured gearbox vibration signals, an order-tracking method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this paper. Firstly, the multi-component vibration signal is decomposed into several intrinsic mode functions (IMFs) using VMD in order to extract a signal component with higher signal-to-noise ratio (SNR). Then, the sensitive fault information carrying IMF is exploited to estimate the instantaneous speed profile in order to construct the shaft rotational vibration signal. The measured vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal of constant shaft rotational frequency. Finally, the gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using simulation results.

The use of variational mode decomposition in assisting sedimentary cyclicity analysis: A case study from an Albian carbonate reservoir, Campos Basin, southeast Brazil

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. B77-B86
Author(s):  
Leandro Hartleben Melani ◽  
Bruno César Zanardo Honório ◽  
Ulisses Miguel da Costa Correia ◽  
Alexandre Campane Vidal
Keyword(s):  
Gamma Ray ◽  
Carbonate Reservoir ◽  
Well Log ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Campos Basin ◽  
Band Limited

The sedimentary cyclicity analysis investigates the cyclic patterns and the different hierarchical orders of cyclicity in the stratigraphic record. The detection of cyclic depositional patterns is a key element of quantitative stratigraphy. It is often based on well-log data, which can be challenging due to the presence of superimposed cycles and nongeologic artifacts. We have developed an approach to assist the detection of sedimentary cyclicity in well-log signals based on a multiscale spectral analysis method. First, we apply variational mode decomposition to decompose the gamma-ray logs into band-limited subsignals, the intrinsic mode functions (IMFs), to investigate different orders of smoothness, signal-to-noise ratio, and the cyclicity embedded in the geologic record. Conventional time-domain analysis is carried out to understand the general trends in the IMFs, which enables us to better identify long-term cycles associated with transgressive-regressive (T-R) sequences. Then, by appropriately selecting a given IMF and extracting the instantaneous frequency (IF) and its mirrored version, we build a cyclicity log that can map expressive behavior change in the time-frequency domain. Because the IF is more sensitive to the signal variations, we could highlight the short-term cycles throughout the formation in detail. The detected short-term cycles are in agreement with the T-R sequence. We apply our method to the Albian carbonate succession of Macaé Group, Campos Basin, Brazil. We understand that our method can be a valuable tool for semiautomated detection of sedimentary cycles, assisting in the characterization of different hierarchical orders of cyclicity.

Depositional sequence characterization based on seismic variational mode decomposition

2017 ◽  
Vol 5 (2) ◽  
pp. SE97-SE106 ◽  
Author(s):  
Fangyu Li ◽  
Bo Zhang ◽  
Rui Zhai ◽  
Huailai Zhou ◽  
Kurt J. Marfurt
Keyword(s):  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Depositional Sequence ◽  
Sequence Characterization ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
Band Limited ◽  
Sequence Boundaries

Subtle variations in otherwise similar seismic data can be highlighted in specific spectral components. Our goal is to highlight repetitive sequence boundaries to help define the depositional environment, which in turn provides an interpretation framework. Variational mode decomposition (VMD) is a novel data-driven signal decomposition method that provides several useful features compared with the commonly used time-frequency analysis. Rather than using predefined spectral bands, the VMD method adaptively decomposes a signal into an ensemble of band-limited intrinsic mode functions, each with its own center frequency. Because it is data adaptive, modes can vary rapidly between neighboring traces. We address this shortcoming of previous work by constructing a laterally consistent VMD method that preserves lateral continuity, facilitating the extraction of subtle depositional patterns. We validate the accuracy of our method using a synthetic depositional cycle example, and then we apply it to identify seismic sequence stratigraphy boundaries for a survey acquired in the Dutch sector, North Sea.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

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