scholarly journals Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges

2020 ◽  
Vol 206 ◽  
pp. 01024
Author(s):  
Feng Xin ◽  
Li Shaohui ◽  
Feng Qiang ◽  
Liu Shugui
Keyword(s):  
Classification Performance ◽  
Petroleum Exploration ◽  
Classification Method ◽  
Support Vector ◽  
High Signal ◽  
Oil Well ◽  
Well Testing ◽  
Identification Rate ◽  
Time Frequency ◽  
Testing Data

During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.

scholarly journals Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1263
Author(s):  
Chih-Yao Chang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Data Quality ◽  
Classification Performance ◽  
Patent Infringement ◽  
Classification Method ◽  
Training Dataset ◽  
Support Vector ◽  
Classification Problems ◽  
Data Quality Management ◽  
Patent Rights

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

scholarly journals Aircraft Target Classification Method for Conventional Narrowband Radar Based on Micro-Doppler Effect

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Saiqiang Xia ◽  
Chaowei Zhang ◽  
Wanyong Cai ◽  
Jun Yang ◽  
Liangfa Hua ◽  
...  
Keyword(s):  
Doppler Effect ◽  
Classification Problem ◽  
Classification Performance ◽  
Central Moment ◽  
Classification Method ◽  
Support Vector ◽  
Machine Model ◽  
Different Types ◽  
Characteristics Analysis ◽  
The Time Domain

For a conventional narrowband radar system, its insufficient bandwidth usually leads to the lack of detectable information of the target, and it is difficult for the radar to classify the target types, such as rotor helicopter, propeller aircraft, and jet aircraft. To address the classification problem of three different types of aircraft target, a joint multifeature classification method based on the micro-Doppler effect in the echo caused by the target micromotion is proposed in this paper. Through the characteristics analysis of the target simulation echoes obtained from the target scattering point model, four features with obvious distinguishability are extracted from the time domain and frequency domain, respectively, that is, flicker interval, fractal dimension, modulation bandwidth, and second central moment. Then, a support vector machine model will be applied to the classification of the three different types of aircraft. Compared with the conventional method, the proposed method has better classification performance and can significantly improve the classification probability of aircraft target. The simulations are carried out to validate the effectiveness of the proposed method.

scholarly journals Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6677
Author(s):  
Sahand Hajifar ◽  
Saeb Ragani Lamooki ◽  
Lora A. Cavuoto ◽  
Fadel M. Megahed ◽  
Hongyue Sun
Keyword(s):  
Support Vector Machine ◽  
Activity Recognition ◽  
Domain Adaptation ◽  
Classification Performance ◽  
Support Vector ◽  
Standard Support Vector Machine ◽  
Occupational Tasks ◽  
Testing Data ◽  
Occupational Task ◽  
The Impact

Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.

Analysis of Time, Frequency and Wavelet Based Features of Vibration and Current Signals for Fault Diagnosis of Induction Motors Using SVM

2017 ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motors ◽  
Classification Performance ◽  
Operating Conditions ◽  
Support Vector ◽  
Time Frequency ◽  
Mechanical Faults ◽  
Severity Levels ◽  
Electrical Faults

This paper presents a comparative analysis of the time, frequency and time-frequency domain based features of the vibration and current signals for identifying various faults in induction motors (IMs) using support vector machine (SVM). Four mechanical faults (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor), and three electrical faults (broken rotor bars, stator winding fault with two severity levels and phase unbalance with two severity levels) are considered in the present study. The proposed fault diagnosis consists of three steps. In the first step, the vibration in three orthogonal directions and the current in three phases are acquired from the healthy and faulty motors using a machine fault simulator (MFS). In second step, useful statistical features are extracted from the time, frequency and time-frequency domain (continuous wavelet transform (CWT)) of the signal. For the effective fault diagnosis, SVM parameters are optimally selected based on the grid-search method along with 5-fold cross-validation, and the effective fault features are selected based on the wrapper model. Finally, the fault diagnosis of IM is performed using optimal SVM parameters and effective features as input to the SVM. The classification performance of all methodologies developed in three domains is compared for various operating conditions of IMs. The test results showed that the developed methodology could isolate ten IM fault conditions successfully based on features from all three domains at all IM operating conditions; however, time-frequency features give the best results.

ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250003 ◽  
Author(s):  
MIN ZHANG ◽  
YI SHEN
Keyword(s):  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Finite Amplitude ◽  
Support Vector ◽  
Time Frequency ◽  
Original Dataset ◽  
Mode Decomposition ◽  
Nonstationary Data

Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary processes. First, EEMD is applied to each hyperspectral image band and defines the true intrinsic mode function (IMF) components as the mean of an ensemble of trials, each consisting of the signal plus a white noise of finite amplitude. After EEMD is performed to each band, new bands are reconstructed as the sum of IMFs and the trend, and classification is executed over these new bands. Finally, the hyperspectral image with new bands was classified with support vector machine (SVM) to show the classification performance of the proposed approach. Experimental results show that the utilization of the EEMD significantly increases the classification accuracy compared to the dataset processed by empirical mode decomposition (EMD) and the original dataset.

scholarly journals Diagnosis of Ball Bearing Faults Using Double Decomposition Technique

2020 ◽  
Vol 25 (3) ◽  
pp. 327-340
Author(s):  
T, Dovedi ◽  
R. Upadhyay
Keyword(s):  
Classification Performance ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Potential Candidate ◽  
Decomposition Technique ◽  
Complexity Measures ◽  
Bearing Faults ◽  
Time Frequency ◽  
Double Decomposition

The rolling element bearing is one of the most significant components of any rotating machinery. However, the foremost cause of malfunction in any rotating machine is due to defects like cracks, dents, spall, pits, etc. in ball bearings. Early diagnosis of these bearing faults is highly essential to avoid an accidental shutdown of rotating machinery. In the present work, a novel technique of bearing fault diagnosis is proposed following double decomposition of the vibration activity. The experimentally recorded vibration signals are processed through two stages of decomposition viz. Empirical Mode Decomposition and Tunable Q-factor Wavelet Transform based Time-Frequency decomposition. Subsequently, sub-bands of decomposed time-frequency activity are acquired and discriminable features are computed. Fractal Dimension (FD) based features are extracted from each decomposed sub-band as complexity measures of time-frequency sub-bands. In order to classify bearing faults, a Support Vector Machine classifier is trained with acquired features and classification performance is evaluated. The results of classification reveal that the proposed double decomposition technique is a potential candidate in extracting viable vibration signatures for fault identification. The study is conducted on Case Western Reserve University bearing datasets.

scholarly journals Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System

2019 ◽  
Vol 9 (21) ◽  
pp. 4617
Author(s):  
Iksu Seo ◽  
Seongweon Kim ◽  
Youngwoo Ryu ◽  
Jungyong Park ◽  
Dong Seog Han
Keyword(s):  
Ambient Noise ◽  
Roc Curves ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Probability Of Detection ◽  
Support Vector ◽  
High Signal ◽  
Classification Rate ◽  
Active Sonar ◽  
Sonar System

The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

scholarly journals Identifying Medicinal Plant Leaves using Textures and Optimal Colour Spaces Channel

2017 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
C H Arun ◽  
D Christopher Durairaj
Keyword(s):  
Discriminant Analysis ◽  
Medicinal Plant ◽  
Classification Performance ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Identification System ◽  
Dependency Matrix ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Grey Tone

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different  colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT).  Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces.

scholarly journals Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10635
Author(s):  
Tongjing Sun ◽  
Jiwei Jin ◽  
Tong Liu ◽  
Jun Zhang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Classification Performance ◽  
Classification Method ◽  
Support Vector ◽  
Target Classification ◽  
Active Sonar ◽  
Reconstructed Signal ◽  
Sparse Representation Classification ◽  
Value Decomposition

The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

scholarly journals Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4541 ◽  
Author(s):  
Xin Feng ◽  
Qiang Feng ◽  
Shaohui Li ◽  
Xingwei Hou ◽  
Mengqiu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Pressure Gauge ◽  
Robustness Analysis ◽  
Optimal Combination ◽  
Smoothing Method ◽  
Oil Well ◽  
Well Testing ◽  
Kalman Smoothing ◽  
Testing Data

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

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