scholarly journals Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1443
Author(s):  
Jian Wang ◽  
Xu Liu ◽  
Wen Li ◽  
Fei Liu ◽  
Craig Hancock
Keyword(s):  
Field Test ◽  
Torsion Angle ◽  
Measurement Unit ◽  
Variational Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Oil Platforms ◽  
Offshore Oil Platforms ◽  
Mode Decomposition ◽  
Offshore Oil

Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To solve this, a combined Hilbert–Huang transform (HHT) and variational mode decomposition (VMD) method is proposed to extract multidimensional dynamic response characteristics of time, frequency, and energy of offshore oil platforms. Based on the extracted time–frequency–energy information, the frequency-domain integration approach (FDIA) can be applied to calculate the displacement using accelerometer in the micro inertial measurement unit (MIMU). A complementary filtering algorithm was designed to measure the torsion angle of platforms using six degrees of freedom data from the MIMU to obtain the torsion angle information. The performance of the proposed method was validated using a series of simulation shaking-table tests and a field test conducted on an offshore oil platform at Dongying City, Shandong Province, China. During the field test, seven out of eight collisions were detected in the frequency range 5 Hz to 12 Hz. The intensity of the fifth collision was the highest, and the maximum displacement obtained by the accelerometer was 6 mm. In addition, the results show a correlation between the axes of the accelerometer and gyroscope, and their combination can measure a torsion angle up to 1.1°.

scholarly journals Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

2019 ◽  
Vol 9 (10) ◽  
pp. 2017 ◽  
Author(s):  
Juncai Xu ◽  
Bangjun Lei
Keyword(s):  
Instantaneous Frequency ◽  
Data Interpretation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
The Media ◽  
Ground Penetrating

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

Fouling assemblages on offshore oil platforms: diverse vertical reefs in the Arabian Gulf

2018 ◽  
Author(s):  
Pedro Range ◽  
Rodrigo Riera ◽  
Mustafa Omerspahic ◽  
Jessica Bouwmeester ◽  
Steffen Sanvig Bach ◽  
...  
Keyword(s):  
Arabian Gulf ◽  
Oil Platforms ◽  
Offshore Oil Platforms ◽  
Fouling Assemblages ◽  
Offshore Oil

scholarly journals Common Crossing Structural Health Analysis with Track-Side Monitoring

2019 ◽  
Vol 21 (3) ◽  
pp. 77-84
Author(s):  
Mykola Sysyn ◽  
Olga Nabochenko ◽  
Franziska Kluge ◽  
Vitalii Kovalchuk ◽  
Andriy Pentsak
Keyword(s):  
Gaussian Process Regression ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Resolution ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Structural Health ◽  
Rolling Surface ◽  
Mode Decomposition ◽  
Inertial Measurements ◽  
Common Crossing

Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.

Modelling of methane emissions from offshore oil platforms in the Norwegian sea

2021 ◽  
Author(s):  
Ignacio Pisso ◽  
Amy Foulds ◽  
Grant Allen
Keyword(s):  
Radiative Forcing ◽  
Oil And Gas ◽  
Lagrangian Model ◽  
Model Parameters ◽  
Positive Contribution ◽  
Atmospheric Methane ◽  
Norwegian Sea ◽  
Oil Platforms ◽  
Offshore Oil Platforms ◽  
Offshore Oil

<p>Methane is a major greenhouse gas that has increased since the pre-industrial era and reducing its emissions is potentially an effective way of mitigating the radiative forcing in the short term. The oil & gas industry has a positive contribution to the global atmospheric methane budget with fugitive emissions from infrastructure installations such as offshore oil platforms. As part of the United Nations Climate and Clean Air Coalition (UN CCAC) objective to quantify global CH4 emissions from oil and gas facilities, a series of aircraft campaigns have been carried out in the Norwegian sea among other areas. We report on the Lagrangian modelling activity of the emissions and transport sensitivities used to support the flux assessment. Source identification has been carried out based on backward modelling and has proved useful to interpret observations form the in situ airborne platforms. In addition, forward modelling of the emission plume in high resolution has been applied to constraining the plume height for mass balance methods assessment. Dependency of the resulting uncertainty of the flux estimates on various factors such as the choice of the meteorology and the of the Lagrangian model parameters is also discussed.</p>

White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V307-V317 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Naihao Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
White Noise ◽  
Seismic Data ◽  
Prior Information ◽  
Training Procedure ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition

Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.

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