“Digital Pigging” as a Basis for Improved Pipeline Structural Integrity Evaluations

2006 ◽  
Author(s):  
James D. Hart ◽  
Nasir Zulfiqar ◽  
David H. Moore ◽  
Greg R. Swank
Keyword(s):  
Pattern Recognition ◽  
Survey Data ◽  
Structural Integrity ◽  
Field Measurements ◽  
Gage Length ◽  
Basic Pattern ◽  
Bending Strain ◽  
Low Pass ◽  
Actual Geometry ◽  
Geometry Pig

This paper describes the application of “digital pigging” procedures for converting field measurements of pipeline geometry (e.g., top of pipe survey profiles), results from geometry pig surveys, or analytically generated pipeline centerline profiles into corresponding profiles of pipeline curvature and bending strain. Application of digital pigging procedures to pipeline elevation and/or inclination profiles developed from accelerometer based geometry pigs provides a basis for performing the additional calculations required to develop bending strain profiles which may not be a part of the geometry survey deliverable but are required for pipeline structural integrity evaluations. This paper presents examples of digital pig runs over analytical pipe centerline profiles to illustrate the important effects of feature length, pig length and curvature gage length. Comparisons of the results from digital pig runs over actual geometry pig data profiles and digital pig runs over the corresponding known analytical profiles will illustrate how basic pattern recognition concepts can be used as a basis for improved synthesis of real pig data signatures. This paper also presents examples of digital pigging calculations performed on geometry pig survey data that show how low-pass filtering can be used to reduce the effects of noise in the survey data as well as the influence of curvature gage length on the computed curvature/bending strain profiles.

scholarly journals Field Measurements of Vibration on the Car Body-Suspended Equipment for High-Speed Rail Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jinying Guo ◽  
Huailong Shi ◽  
Fansong Li ◽  
Pingbo Wu
Keyword(s):  
High Speed ◽  
Field Measurements ◽  
Railway Vehicle ◽  
Elastic Suspension ◽  
High Speed Rail ◽  
Test Configuration ◽  
Car Body ◽  
Rail Vehicles ◽  
Frequency Domains ◽  
Low Pass

The vibrations in the flexible car bodies of the high-speed electric multiple units (EMUs) and their coupling effects with the bogies and other types of equipment vibrating have lead issues for railway operators and gained interest for researchers. Other than a numerical investigation, field measurements on the vibrating characteristics of the car body (CB) and its suspended equipment (CBSE) for a high-speed railway vehicle were performed to elaborate the vibrating characteristics on the CB and its CBSE. In this long-term tracking test, the running stability of vehicle and wheel-rail interaction were also examined with the increase of operation distance (OD), a total of 2,400,000 km. The test configuration and arrangements are introduced first, followed by the data analysis in time and frequency domains. It is seen that the wheelset conicity increases 0.008 per 10,000 km, which increases approximately linearly with the OD from 0.10 to 0.40. Two types of wheel treads, S1002CN and LMB10, have different ranges in conicity and reprofiling cycles. The lateral accelerations on CB in a downward-running case (0.5 g) are much greater than that in upward-running case (0.2 g) corresponding to the vehicle stability differences. The 15 Hz low-pass filtered acceleration on CB experiences a maximum of 0.10 g and an averaged amplitude around 0.05 g, whereas the frequency spectrum has peaks of 0.01 g on CB and 0.1 g on CBSE. It states that an elastic suspension between the CBSE and the CB prevents the high-frequency vibration from the CB.

scholarly journals A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

2021 ◽  
Author(s):  
Yashodhan Rajiv Athavale ◽  
Sridhar Krishnan
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Systematic Investigation ◽  
Machine Learning Approach ◽  
Low Pass ◽  
Signal Encoding ◽  
Encoding Method ◽  
Personalized Health ◽  
And Storage

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

scholarly journals A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health.

2021 ◽  
Author(s):  
Yashodhan Rajiv Athavale ◽  
Sridhar Krishnan
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Systematic Investigation ◽  
Machine Learning Approach ◽  
Low Pass ◽  
Signal Encoding ◽  
Encoding Method ◽  
Personalized Health ◽  
And Storage

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

Extraterrestrial message construction

2020 ◽  
pp. 239-250
Author(s):  
Sheri Wells-Jensen ◽  
Kimberly Spallinger
Keyword(s):  
Pattern Recognition ◽  
The Other ◽  
Academic Disciplines ◽  
Human Language ◽  
Basic Pattern ◽  
Language Of Instruction ◽  
Key Concepts ◽  
Different Levels

This chapter presents a set of exercises ready for use in the classroom, in which students use basic pattern recognition skills to solve an “alien” message that includes both numbers and other elements. The other elements define relations among the numbers, and students determine how those relations can be mapped to words in a human language—whatever the language of instruction. Rationale for the utility of this approach is discussed, as are ways to modify the instructions to present the exercises to student populations with different levels of sophistication in math and computation, and different levels of linguistic training. The exercise and rationale exemplify one way in which constructed languages can be used to introduce key concepts in linguistics for students from a variety of academic disciplines.

scholarly journals Projective Texturing Uncertain Geometry: silhouette-aware box-filtered blending using integral radial images

2014 ◽  
Vol II-3 ◽  
pp. 17-23
Author(s):  
M. Brédif
Keyword(s):  
Constant Time ◽  
Depth Interval ◽  
Coarse Geometry ◽  
Mobile Mapping ◽  
Image Based Rendering ◽  
Street Level ◽  
Time Out ◽  
Low Pass ◽  
The Cost ◽  
Actual Geometry

Projective texturing is a commonly used image based rendering technique that enables the synthesis of novel views from the blended reprojection of nearby views on a coarse geometry proxy approximating the scene. When scene geometry is inexact, aliasing artefacts occur. This introduces disturbing artefacts in applications such as street-level immersive navigation in mobile mapping imagery, since a pixel-accurate modelling of the scene geometry and all its details is most of the time out of question. The filtered blending approach applies the necessary 1D low-pass filtering on the projective texture to trade out the aliasing artefacts at the cost of some radial blurring. This paper proposes extensions of the filtered blending approach. Firstly, we introduce Integral Radial Images that enable constant time radial box filtering and show how they can be used to apply box-filtered blending in constant time independently of the amount of depth uncertainty. Secondly, we show a very efficient application of filtered blending where the scene geometry is only given by a loose depth interval prior rather than an actual geometry proxy. Thirdly, we propose a silhouette-aware extension of the box-filtered blending that not only account for uncertain depth along the viewing ray but also for uncertain silhouettes that have to be blurred as well.

Regression technique to remove temporal variation from geomagnetic survey data

Geophysics ◽  
1982 ◽  
Vol 47 (10) ◽  
pp. 1437-1443 ◽  
Author(s):  
Eric L. Sander ◽  
Charmaine P. Mrazek
Keyword(s):  
Temporal Variation ◽  
Survey Data ◽  
Field Measurements ◽  
Open Ocean ◽  
Regression Technique ◽  
Geomagnetic Survey ◽  
Regression Techniques

A flexible and effective set of procedures has been developed to reconstruct the temporal variation occurring during a geomagnetic survey. These procedures use regression techniques and the field measurements made at survey track intersections. They have been applied to both ship and aircraft survey data collected in open ocean areas to remove from 60 to 80 percent of the temporal variation. This paper presents these procedures and the results for one ship and one aircraft survey.

Monitoring structures using long gauge length fibre optic sensors

2007 ◽  
Vol 34 (3) ◽  
pp. 422-429 ◽  
Author(s):  
R C Tennyson ◽  
N Banthia ◽  
E Rivera ◽  
S Huffman ◽  
I Sturrock
Keyword(s):  
Thermal Loading ◽  
Structural Integrity ◽  
Local Strain ◽  
Gauge Length ◽  
Fibre Optic ◽  
Hoop Strain ◽  
Concrete Girders ◽  
Bending Strain ◽  
Bond Interface ◽  
Beam Type

Long gauge length fibre optic sensors have been installed on bridges and pipelines to monitor their long-term structural integrity. These sensors measure the average displacement or strain over their gauge length due to mechanical or thermal loading. It is shown that long gauge length sensors can provide an estimate of the maximum bending strain for beam-type structures, such as bridge girders or pipelines, subject to sag. Bending and hoop strain test results are presented for bridges with composite reinforcements bonded to concrete girders and columns that were statically loaded at various locations to assess the integrity of the bond interface. These sensors can also provide information on corrosion-induced wall thinning of pipelines based on changes in the local strain field due to internal pressure in the line. Test data are presented for measuring pipeline corrosion using different fibre optic sensor configurations.Key words: fibre optic sensors, bridges, pipelines, integrity monitoring.

Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada

2020 ◽  
Vol 63 (1) ◽  
pp. 43-59 ◽  
Author(s):  
Tim Webster ◽  
Candace MacDonald ◽  
Kevin McGuigan ◽  
Nathan Crowell ◽  
Jean-Sebastien Lauzon-Guay ◽  
...  
Keyword(s):  
Nova Scotia ◽  
Surface Area ◽  
Survey Data ◽  
Satellite Data ◽  
High Tide ◽  
Field Measurements ◽  
Ground Truth ◽  
Cost Effective ◽  
Satellite System ◽  
Ground Truth Data

AbstractThe ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.

Measuring Pipeline Movement in Geotechnically Unstable Areas Using an Inertial Geometry Pipeline Inspection Pig

1996 ◽  
Author(s):  
Jaroslaw A. Czyz ◽  
Constantino Fraccaroli ◽  
Alan P. Sergeant
Keyword(s):  
Failure Modes ◽  
Field Measurements ◽  
Inspection System ◽  
Buried Pipeline ◽  
Pipeline Inspection ◽  
Bending Strain ◽  
Geometry Inspection ◽  
Accuracy And Repeatability ◽  
Nearly Continuous ◽  
Point Type

NOWSCO’s Inertial Geometry Inspection system (GEOPIG) measures pipeline location coordinates (x,y,h) and provides data for measuring pipeline bending strain and strain changes used for structural analysis and integrity evaluation of pipeline systems in geotechnically unstable areas. This paper reviews the results of work to prove such a system’s accuracy and repeatability against deliberately induced strain events in a 26 inch operating gas pipeline. An inertial geometry pipeline inspection tool provides nearly continuous measurement of pipeline centerline coordinates. Over time, run to run strain comparisons can be made providing information with respect to possible failure modes and timing. Monitoring buried pipeline movements in geotechnically unstable areas using strain gauges and/or monitoring rods can provide incomplete information with regard to true pipeline movement due to the discrete, point type measurement of these systems. If movement occurs outside of areas where such monitoring systems are deployed, information regarding important pipeline strain changes can go unmeasured. This paper reviews work involved in detecting, locating, and determining the magnitude and type of strain and corresponding pipeline movement induced at one unknown location within a 70 km section between two inertial geometry surveys. The inertial geometry results are compared against strain gauge field measurements.

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