scholarly journals Development the method of pipeline bending strain measurement based on microelectromechanical systems inertial measurement unit

2020 ◽  
Vol 103 (2) ◽  
pp. 003685042092523
Author(s):  
Rui Li ◽  
Zhensheng Wang ◽  
Pengchao Chen
Keyword(s):  
Inertial Measurement Unit ◽  
Oil And Gas ◽  
Microelectromechanical Systems ◽  
Small Diameter ◽  
Measurement Unit ◽  
Additional Stress ◽  
Routine Practice ◽  
Inertial Measurement ◽  
Bending Strain ◽  
Key Factor

With the development of pipeline construction, the additional stress and strain becomes the key factor to induce the damage for oil and gas pipeline. The in-line inspection of pipeline bending strain which is based on high-end tactical-grade inertial measurement unit has become routine practice for the oil and gas pipelines over recent years. However, these accurate inertial measurement units are large size and high cost limit to use in small diameter pipelines of bending strain inspection. Microelectromechanical systems–based inertial navigation has been applied to mapping the centerline of the small size pipeline, and the accurate trajectory and attitude information become key factors to calculate the bending strain of pipelines. This article proposed a method not only to calculate the pipeline bending strain but also to improve the accuracy for the bending strain based on the wavelet analysis. Tests show that this method can be effectively used in the calculation and optimization of the bending strain, and it will increase the accuracy to within 19.1% of the actual bending strain.

scholarly journals Modeling and Calculation of Dent Based on Pipeline Bending Strain

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingshan Feng ◽  
Rui Li ◽  
Hong Zhang
Keyword(s):  
Inertial Measurement Unit ◽  
Oil And Gas ◽  
Measurement Unit ◽  
Mapping Data ◽  
Long Distance ◽  
Bending Strain ◽  
Strain Data ◽  
Integrity Management ◽  
Novel Method ◽  
The Mean

The bending strain of long-distance oil and gas pipelines can be calculated by the in-line inspection tool which used inertial measurement unit (IMU). The bending strain is used to evaluate the strain and displacement of the pipeline. During the bending strain inspection, the dent existing in the pipeline can affect the bending strain data as well. This paper presents a novel method to model and calculate the pipeline dent based on the bending strain. The technique takes inertial mapping data from in-line inspection and calculates depth of dent in the pipeline using Bayesian statistical theory and neural network. To verify accuracy of the proposed method, an in-line inspection tool is used to inspect pipeline to gather data. The calculation of dent shows the method is accurate for the dent, and the mean relative error is 2.44%. The new method provides not only strain of the pipeline dent but also the depth of dent. It is more benefit for integrity management of pipeline for the safety of the pipeline.

scholarly journals A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 840
Author(s):  
Lianwu Guan ◽  
Xiaodan Cong ◽  
Qing Zhang ◽  
Fanming Liu ◽  
Yanbin Gao ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Urban Areas ◽  
Inertial Measurement Unit ◽  
Integrated Management ◽  
Small Diameter ◽  
Cost Effective ◽  
Measurement Unit ◽  
Comprehensive Review ◽  
Inertial Measurement ◽  
Defects Inspection

It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D < 30 cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.

scholarly journals A Sporting Chance

2011 ◽  
Vol 133 (07) ◽  
pp. 40-45
Author(s):  
Noel C. Perkins ◽  
Kevin King ◽  
Ryan McGinnis ◽  
Jessandra Hough
Keyword(s):  
Inertial Measurement Unit ◽  
Wireless Sensors ◽  
Microelectromechanical Systems ◽  
Angular Rate ◽  
Rigid Body Dynamics ◽  
The Body ◽  
Measurement Unit ◽  
Golf Club ◽  
Sports Training ◽  
Inertial Measurement

This article discusses using wireless sensors to improve sports training. One example of wireless sensors is inertial sensors that were first developed for automotive and military applications. They are tiny accelerometers and angular rate gyros that can be combined to form a complete inertial measurement unit. An inertial measurement unit (IMU) detects the three-dimensional motion of a body in space by sensing the acceleration of one point on the body as well as the angular velocity of the body. When this small, but rugged device is mounted on or embedded within sports gear, such as the shaft of a golf club, the IMU provides the essential data needed to resolve the motion of that equipment. This technology—and sound use of the theory of rigid body dynamics—is now being developed and commercialized as the ingredients in new sports training systems. It won’t be too long before microelectromechanical systems based hardware and sophisticated software combine to enable athletes at any level to get world-class training.

scholarly journals A novel algorithm for pipeline displacement and bending strain of in-line inspection based on inertia measurement technology

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881675
Author(s):  
Shucong Liu ◽  
Dezhi Zheng ◽  
Tianhao Wang ◽  
Mengxi Dai ◽  
Rui Li ◽  
...  
Keyword(s):  
Strain Measurement ◽  
Inertial Measurement Unit ◽  
Spline Interpolation ◽  
Measurement Unit ◽  
Measurement Technology ◽  
Safe Operation ◽  
Inertial Measurement ◽  
Filtering Algorithm ◽  
Measuring Devices ◽  
Bending Strain

The in-line inspection tool with Inertial Measurement Unit tool is becoming a routine and important practice for many pipeline companies and is effective for whole-line bending strain measurement. However, the measurements of Inertial Measurement Unit tool are always affected by noises and errors, which are caused by inherent inaccuracies and deficiencies of the experimental techniques and measuring devices. For the calculations of the bending strain, the results are very sensitive to the noises and errors. A filtering algorithm based on cubic spline interpolation was proposed for Inertial Measurement Unit data processing to eliminate noises and errors for bending strain, and the effectiveness is validated through the pipeline field test. The results showed that the average pipeline displacement deviation declined 13.82% in the three tests, and the bending strain error reduced from 0.037% to 0.014%. The proposed method effectively improves the inspection accuracy and provides an effective method for pipeline displacement and strain inspection, which ensures the safe operation of the pipeline.

scholarly journals Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3865 ◽  
Author(s):  
Rodrigo Gonzalez ◽  
Paolo Dabove
Keyword(s):  
Automotive Industry ◽  
Inertial Measurement Unit ◽  
Microelectromechanical Systems ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Mass Market ◽  
Inertial Measurement ◽  
Mems Imu

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

Recommended Procedures for Evaluation and Synthesis of Pipelines Subject to Multiple IMU Tool Surveys

2020 ◽  
Author(s):  
James D. Hart ◽  
Nasir Zulfiqar ◽  
Ed McClarty
Keyword(s):  
Inertial Measurement Unit ◽  
Three Dimensional ◽  
Ground Movement ◽  
Measurement Unit ◽  
Profile Data ◽  
Inertial Measurement ◽  
Bending Strain ◽  
Primary Focus ◽  
High Degree ◽  
Strapdown Inertial Measurement Unit

Abstract This paper summarizes recommended procedures for evaluation and synthesis of geometry data from pipelines subjected to repeat in-line inspections (ILI) using strapdown Inertial Measurement Unit (IMU) tools. The paper provides an overview of IMU tool instrumentation (gyroscopes, accelerometers and odometers), a brief summary of IMU tool data processing and discusses IMU tool sensitivity and accuracy specifications. The paper also provides a review of the approaches that are used to identify ground movement signatures in IMU data profiles. Based on a single IMU survey, it can be difficult to determine with certainty if a given bending strain feature was induced during construction of the pipeline (e.g., by conforming the pipeline profile to an uneven trench profile) or was the result of post-construction movement of the pipeline (e.g., due to geohazards along the alignment such as landslides, karst, etc.). Because such a large percentage of vendor identified bending strain anomalies in IMU surveys are the result of construction, comparison of pipeline geometry changes between two (or more) inertial inspections is normally a more reliable method of identifying areas of actual post-construction movement of the pipeline. This is because a high degree of repeatability is observed in overlays of the pipeline geometry data signatures obtained from different IMU surveys at construction-induced bending features whereas the geometry signals associated with real pipeline movement frequently exhibit change patterns in out-of-straightness, pitch and heading and curvature/bending strain profiles that correspond to increasing pipe curvature and deformation. The primary focus of this paper is to summarize recommended data deliverables and plots of three-dimensional pipeline geometry profile data from different IMU tool surveys at vendor-identified bending strain anomalies including multi-panel overlay plot packages as well as survey-to-survey difference or change profiles that greatly assist in distinguishing if the feature is the result of construction or due to post-construction movement and to aid in the evaluation of pipeline deformation states.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

Sign in / Sign up

Export Citation Format

Share Document