Study on the Distribution of Submarine Pipeline Corrosion Defects Based on Internal Inspection Data and Data Mining Method

2020 ◽  
Author(s):  
Guoxi He ◽  
Sijia Chen ◽  
Kexi Liao ◽  
Shuai Zhao
Keyword(s):  
Least Square ◽  
Support Vector ◽  
Submarine Pipeline ◽  
Distribution Rules ◽  
Pipe Segment ◽  
Integrity Management ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Oil Gas ◽  
Pipeline Corrosion

Abstract Submarine pipelines in the sea are applied for oil, gas, water and mixed transportation. Among them, 91% of the pipes contain CO2. Here, based on the existing pipeline internal inspection data of submarine pipeline, the APRIORI algorithm and least-square-support-vector-machine (LSSVM) are applied to analyze the distribution rules and defect characteristics of internal defects along the pipeline. The corrosion defects are divided into 7 types and the pipeline section is divided into 12 intervals. Also, the pipe segment has been defined as J (general pipe), W (weld) and C (close to weld). The contents include the analysis of the characteristics and types of defects, the distribution of defects along the pipe, the severity of the corrosion defects, the size characteristics of defects, and the comparison of the data detected in multiple rounds. The defect depth of four kinds of pipelines is mostly 10%–20% of the wall thickness, hereby the severity of defects is studied via the percentage distribution of corrosion depth. The data of multi-round inspection shows that the corrosions in the mixed pipeline are active and the defects are increasing. The methods and results in this paper can be employed to predict the most likely defect type, mileage location, clock orientation, and shape size of submarine pipeline corrosion. This is helpful for the integrity management of submarine pipelines.

Pipeline Corrosion Growth Modeling for In-Line Inspection Data Using a Population-Based Approach

2015 ◽  
Author(s):  
Markus R. Dann ◽  
Marc A. Maes ◽  
Mamdouh M. Salama
Keyword(s):  
Growth Process ◽  
Probabilistic Approach ◽  
Population Based ◽  
Metal Loss ◽  
Small Subset ◽  
Maintenance And Repair ◽  
Small Set ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

To manage the integrity of corroded pipelines reliable estimates of the current and future corrosion growth process are required. They are often obtained from in-line inspection data by matching defects from two or more inspections and determining corrosion growth rates from the observed growth paths. In practice only a (small) subset of the observed defects are often reliably matched and used in the subsequent corrosion growth analysis. The information from the remaining unmatched defects on the corrosion growth process are typically ignored. Hence, all decisions that depend on the corrosion growth process such as maintenance and repair requirements and re-inspection intervals, are based on the information obtained from the (small) set of matched defects rather than all observed corrosion anomalies. A new probabilistic approach for estimating corrosion growth from in-line inspection data is introduced. It does not depend on defect matching and the associated defect matching uncertainties. The reported defects of an inspection are considered from a population perspective and the corrosion growth is determined from two or more defect populations. The distribution of the reported defect sizes is transformed into the distribution of the actual defect sizes by adjusting it for detectability, false calls, and sizing uncertainties. The obtained distribution is then used to determine the parameters of the assumed gamma-distributed corrosion growth process in order to forecast future metal loss in the pipeline. As defect matching is not required all reported corrosion defects are used in the probabilistic analysis rather than the truncated set of matched defects. A numerical example is provided where two in-line inspections are analyzed.

Time-Dependent Corrosion Growth Modeling Using Multiple In-Line Inspection Data

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Shenwei Zhang ◽  
Wenxing Zhou ◽  
Mohammad Al-Amin ◽  
Shahani Kariyawasam ◽  
Hong Wang
Keyword(s):  
Measurement Errors ◽  
Oil And Gas ◽  
Time Dependent ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Proposed Model ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

This paper describes a nonhomogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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