Kick Detection Using Downhole Accelerometer Data

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Robello Samuel
Keyword(s):  
High Frequency ◽  
Damping Ratio ◽  
Energy Operator ◽  
Damping Factor ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Time Data ◽  
Essential Information ◽  
Vibration Data

Abstract The high-frequency downhole vibration data include a greater amount of hidden information than the low-frequency surface data. This paper proposes the monitoring of high-frequency acceleration data for early kick detection. The trend of accelerator sensor values is monitored, rather than processed. When the gas, fluid, or oil kick occurs, the fluid influx reduces the viscosity of the fluid in annulus, which causes the degradation of the damping factor. The sensor installed on the drillpipe detects the velocity/acceleration change that results in the damping factor change. This approach includes an analytical model to calculate the effect of the damping ratio on the acceleration calculations. The fluid influx and migration in the wellbore strongly affect the damping factor. The paper presents a method of deconvoluting the sensor values that uses a combination of minimum entropy deconvolution and Teager-Kaiser energy operator to remove the noise, unwanted sensor values, and likelihood of false prediction. It is then proposed to calculate instantaneous jerk and jerk intensity at each depth. The trend of the final intrinsic mode functions (IMF) at each depth is continuously monitored to predict the formation influx, if any. A novel concept of monitoring the incremental IMF and IMF energy at each depth is introduced. This technique is shown to reveal a wealth of information and simplifies the process of monitoring and analyzing the vast amount of available data. The methodology developed is applied to extract the essential information from high-frequency vibration data to make real-time data monitoring straightforward, reliable, and fast.

scholarly journals Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Kedadouche ◽  
Marc Thomas ◽  
Antoine Tahan
Keyword(s):  
Spectrum Analysis ◽  
Signal Analysis ◽  
Energy Operator ◽  
Analysis Tool ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Envelope Spectrum

Amplitude demodulation is a key for diagnosing bearing faults. The quality of the demodulation determines the efficiency of the spectrum analysis in detecting the defect. A signal analysis technique based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD), and Teager Kaiser energy operator (TKEO) is presented. The proposed method consists in enhancing the signal by using MED, decomposing the signal in intrinsic mode functions (IMFs) and selects only the IMF which presents the highest correlation coefficient with the original signal. In this study the first IMF1 was automatically selected, since it represents the contribution of high frequencies which are first excited at the early stages of degradation. After that, TKEO is used to track the modulation energy. The spectrum is applied to the instantaneous amplitude. Therefore, the character of the bearing faults can be recognized according to the envelope spectrum. The simulation and experimental results show that an envelope spectrum analysis based on MED-EMD and TKEO provides a reliable signal analysis tool. The experimental application has been developed on acoustic emission and vibration signals recorded for bearing fault detection.

scholarly journals MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 331 ◽  
Author(s):  
Zhuorui Li ◽  
Jun Ma ◽  
Xiaodong Wang ◽  
Jiande Wu
Keyword(s):  
Feature Extraction ◽  
Energy Operator ◽  
Vibration Signal ◽  
High Energy ◽  
Energy Ratio ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Rolling Bearings ◽  
Teager Energy ◽  
The Imf

In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.

scholarly journals Optimal design of controllers for power network connected SOFC using of multi-objective PSO

2018 ◽  
Vol 15 (2) ◽  
pp. 145-163 ◽  
Author(s):  
Amin Safari ◽  
Hossein Shahsavari ◽  
Farshad Babaei
Keyword(s):  
Damping Ratio ◽  
Damping Factor ◽  
Small Signal ◽  
Small Signal Stability ◽  
Machine System ◽  
Multi Objective ◽  
Signal Stability ◽  
Power Oscillations ◽  
System Stabilizer ◽  
Small Signal Modeling

In this paper, we study the concept and forming manner of Solid Oxide Fuel Cell (SOFC) into the electrical system and then, its effect on small signal stability is investigated. The paper illustrates the essential module, mathematical analysis and small signal modeling of the SOFC joined to single machine system. The aim of this study is to reduce power oscillations in the presence of the SOFC with optimal stabilizer. The multi-objective Particle Swarm Optimization (MOPSO) technique has been used for designing a Power System Stabilizer (PSS) in order to improve the performance of the system. Two objective functions are regarded for the design of PSS parameters in order to maximize the damping factor and the damping ratio of the system. To evaluate the efficiency of the proposed optimal stabilizers, four scenarios are considered and then, its results have been analyzed. The proposed PSS tuning technique can be applied to a multi-machine system connected to the SOFC. The efficiency of MOPSO based proposed PSS on the oscillations the system related to SOFC is illustrated by time-domain simulation and also, the comparison of the MOPSO based proposed PSS with the PSS based-single objective method has been prepared.

scholarly journals Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings

2021 ◽  
Vol 33 (3) ◽  
pp. 629-642
Author(s):  
Sana Talmoudi ◽  
Tetsuya Kanada ◽  
Yasuhisa Hirata ◽  
◽  
Keyword(s):  
Real Time ◽  
Training Data ◽  
High Dimensional ◽  
Time Data ◽  
Early Failure ◽  
Rolling Bearings ◽  
Full Spectrum ◽  
Time Frequency ◽  
Vibration Data ◽  
Visualization Technology

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

Experimental Investigation of Dynamics of Assembly with Typical Mechanical Joint

2016 ◽  
Vol 693 ◽  
pp. 324-331
Author(s):  
Xin Liu ◽  
Bei Bei Sun ◽  
Jian Dong Chen ◽  
Fei Xue ◽  
Ren Qiang Jiao
Keyword(s):  
Dynamic Stiffness ◽  
Damping Ratio ◽  
Bolted Joints ◽  
Damping Capacity ◽  
Bolted Joint ◽  
Vibration Data ◽  
Mechanical Joints ◽  
Vibration Behavior ◽  
Frictional Damping ◽  
Reference Specimens

Mechanical joints have a significant influence on the dynamics of assembled structure due to its discontinuity, uncertainty, frictional contact and micro-slip along the interface. To study the effect of mechanical interface on vibration behavior of industrial product, it is necessary to capture vibration data and investigate modal properties. In order to study effects of typical mechanical joints, double plates coupled with bolted joint are manufactured. Corresponding welded specimen was also manufactured for comparison and reference. Specimens were suspended by two flexible nylon cords for a free–free boundary condition and series of modal tests were carried out. Experimental results reveal that the preload in bolted joint affects the vibration behavior of assembly greatly, and the dynamic stiffness and natural frequency could be enhanced by increasing preload values of specimen. Bolted joints give rise to more frictional damping capacity within lower preload range in this test and welded specimen shows up much higher frequency and similar damping ratio.

Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique

2010 ◽  
Vol 40-41 ◽  
pp. 91-95 ◽  
Author(s):  
Yan Li Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Acoustic Signals ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Marginal Spectrum ◽  
Rock Interface ◽  
Coal Rock ◽  
Instantaneous Energy

A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.

High Frequency Analysis of a Dynamically Coupled Parallel Plate System

2017 ◽  
Author(s):  
Dean R. Culver ◽  
Earl Dowell
Keyword(s):  
Modal Analysis ◽  
Frequency Analysis ◽  
High Frequency ◽  
Parallel Plate ◽  
Damping Ratio ◽  
Parallel Plates ◽  
Linear Spring ◽  
Stiffness And Damping ◽  
Plate System

The behavior of a system comprised of two parallel plates coupled by a discrete, linear spring and damper is studied. Classical Modal Analysis (CMA) is used to illustrate this behavior, while specifically observing the effects of varying the stiffness and damping ratio of the coupling elements. Conditions under which the coupling may be approximated as rigid are identified. Additionally, conditions under which the coupling displacement reaches its maximum and minimum values are identified. This work also lays the groundwork for extending Asymptotic Modal Analysis (AMA) to systems with discrete, elastic, and dissipative coupling.

Automatic Loop Shaping of MIMO Controllers Satisfying Sensitivity Specifications

2005 ◽  
Vol 128 (2) ◽  
pp. 463-471 ◽  
Author(s):  
O. Yaniv
Keyword(s):  
High Frequency ◽  
Damping Factor ◽  
Notch Filters ◽  
Nonminimum Phase ◽  
Loop Shaping ◽  
Pure Delay

An existing automatic loop shaping algorithm for designing SISO controllers is extended to automatic loop shaping of MIMO controllers that is based on the sequential QFT method. The algorithm is efficient and fast and can search for controllers satisfying many types of restrictions, including constraints on each one of the controller’s elements such as hard restrictions on the high-frequency amplitude or damping factor of notch filters. Moreover, the algorithm can be applied to unstructured uncertain plants, be they stable, unstable, or nonminimum phase, including pure delay.

Using Triaxial Accelerometer Data for Vibration Monitoring of Helicopter Gearboxes

2001 ◽  
Author(s):  
Irem Y. Tumer ◽  
Edward M. Huff
Keyword(s):  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Accelerometer Data ◽  
Frequency Content ◽  
Additional Information ◽  
Vibration Data ◽  
Statistical Variation ◽  
Novel Method ◽  
Monitoring And Diagnostics ◽  
Vibration Signatures

Abstract Typical vibration monitoring systems for helicopter gearboxes rely on single-axis accelerometer data. This paper investigates whether triaxial accelerometers can provide crucial flight regime information for helicopter gearbox monitoring systems. The frequency content of the three different directions is compared and analyzed using time-synchronously averaged vibration data. The triaxial data are decorrelated using a mathematical transformation, and compared to the original axes to determine their optimality. The benefits of using triaxial data for vibration monitoring and diagnostics are explored by analyzing the changes in the direction of the principal axis of vibration formed using all three axes of vibration. The statistical variation introduced due to the experimental variables is further analyzed using an Analysis of Variance approach to determine the effect of each variable on the overall signature. The results indicate that triaxial accelerometers can provide additional information about the frequency content of helicopter gearbox vibrations, providing researchers and industry with a novel method of capturing and monitoring changes in the baseline vibration signatures.

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