Real time leak detection system applied to oil pipelines using sonic technology and neural networks

2009 ◽  
Author(s):  
Alvaro M. Avelino ◽  
Jose A. de Paiva ◽  
Rodrigo E. F. da Silva ◽  
Gabriell J. M. de Araujo ◽  
Fabiano M. de Azevedo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Oil Pipelines

Remote and Intelligent Diagnostic Methods on Leakage Fault for Crude Oil Pipelines

2006 ◽  
Author(s):  
Lai-Bin Zhang ◽  
Zhao-Hui Wang ◽  
Wei Liang
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Data Exchange ◽  
Detection System ◽  
Leak Detection ◽  
Diagnostic Methods ◽  
Oil Fields ◽  
Detection Methods ◽  
Development Environment ◽  
Oil Pipelines

Oil and gas transportation pipelines are the key equipment in petroleum and chemical industry. At present, with the increase of transportation task in oil fields, real-time leak detection system becomes a demand that petroleum companies need to safeguard routines. At the heart of the leakage monitoring and detection procedures are the report of leakage event timely and of leakage point precisely. This paper presents a more realistic approach for using rarefaction-pressure wave technique in oil pipelines, which aims to two targets, one is the improvement of remote and intelligent degree, and the other is the improvement of the leakage location ability. This paper introduces a new scheme to meet the requirements of real time and high data transferring necessary for remote monitoring and leak detection methods for pipelines. The scheme is based on SCADA framework for remote pipeline leakage diagnosis, in which the Dynamic Data Exchange technology is utilized to construct the data-acquiring component to acquire the real-time information that could perform remote test and analysis. It also introduces a basic concept and structure of the remote leak detection system. Primarily, an embedded leak-detection package is designed to exchange the diagnostic information with the RTU data package of Modbus protocol, and then via fiber network, the SCADA-based remote monitoring and leak detection system is realized. Existing data acquisition apparatus applied in oil fields and city underground water pipeline is used, without changing the structure of pipeline supervisory system. This paper introduces the method of constructing DDE-based hot links between servers and client terminals, using Borland C++ Builder 6.0 development environment, and also explains the universality and friendliness of the method. It can easily access similar Windows’ applications simply by modifying Service names, Topic options and data Items. System feasibility was tested using negative-pressure data from oil-fields. Additionally, the applied results show that the whole running status of pipeline can be monitored effectively, and a higher automation grade and an excellent leak location precision of the system can be obtained.

Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks

2018 ◽  
Author(s):  
Joel Smith ◽  
Jaehee Chae ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Detection System ◽  
High Reliability ◽  
Leak Detection ◽  
Sudden Onset ◽  
False Alarms ◽  
Pipeline System ◽  
Transient Model ◽  
Social License

Demonstrating the ability to reliably detect pipeline ruptures is critical for pipeline operators as they seek to maintain the social license necessary to construct and upgrade their pipeline systems. Current leak detection systems range from very simple mass balances to highly complex models with real-time simulation and advanced statistical processing with the goal of detecting small leaks around 1% of the nominal flow rate. No matter how finely-tuned these systems are, however, they are invariably affected by noise and uncertainties in a pipeline system, resulting in false alarms that reduce system confidence. This study aims to develop a leak detection system that can detect leaks with high reliability by focusing on sudden-onset leaks of various sizes (ruptures), as opposed to slow leaks that develop over time. The expected outcome is that not only will pipeline operators avoid the costs associated with false-alarm shut downs, but more importantly, they will be able to respond faster and more confidently in the event of an actual rupture. To accomplish these goals, leaks of various sizes are simulated using a real-time transient model based on the method of characteristics. A novel leak detection model is presented that fuses together several different preprocessing techniques, including convolution neural networks. This leak detection system is expected to increase operator confidence in leak alarms, when they occur, and therefore decrease the amount of time between leak detection and pipeline shutdown.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Pipeline Diagnostics With Ultrasonic Meters

2016 ◽  
Author(s):  
Nicole Gailey ◽  
Noman Rasool
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Flow Measurement ◽  
Detection System ◽  
Leak Detection ◽  
The United States ◽  
Time Data ◽  
Critical System ◽  
Detection Systems ◽  
Transient Models

Canada and the United States have vast energy resources, supported by thousands of kilometers (miles) of pipeline infrastructure built and maintained each year. Whether the pipeline runs through remote territory or passing through local city centers, keeping commodities flowing safely is a critical part of day-to-day operation for any pipeline. Real-time leak detection systems have become a critical system that companies require in order to provide safe operations, protection of the environment and compliance with regulations. The function of a leak detection system is the ability to identify and confirm a leak event in a timely and precise manner. Flow measurement devices are a critical input into many leak detection systems and in order to ensure flow measurement accuracy, custody transfer grade liquid ultrasonic meters (as defined in API MPMS chapter 5.8) can be utilized to provide superior accuracy, performance and diagnostics. This paper presents a sample of real-time data collected from a field install base of over 245 custody transfer grade liquid ultrasonic meters currently being utilized in pipeline leak detection applications. The data helps to identify upstream instrumentation anomalies and illustrate the abilities of the utilization of diagnostics within the liquid ultrasonic meters to further improve current leak detection real time transient models (RTTM) and pipeline operational procedures. The paper discusses considerations addressed while evaluating data and understanding the importance of accuracy within the metering equipment utilized. It also elaborates on significant benefits associated with the utilization of the ultrasonic meter’s capabilities and the importance of diagnosing other pipeline issues and uncertainties outside of measurement errors.

scholarly journals Real Time Object Detection Canbe Embedded on Low Powered Devices

2019 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Open Source ◽  
Real Time ◽  
Detection System ◽  
Computing Power ◽  
Convolution Neural Networks ◽  
Real Challenge ◽  
New Algorithms

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time objectdetection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

Dynamic Modelling and Real-Time Leak Detection for NGL Pipelines

2004 ◽  
Author(s):  
Brent R. Young ◽  
J. Greg Cooke ◽  
Ron E. Daye ◽  
William Y. Svrcek
Keyword(s):  
Real Time ◽  
Detection System ◽  
Dynamic Modelling ◽  
Leak Detection ◽  
Balance System ◽  
Volume Balance ◽  
Control Scheme ◽  
Dynamic Simulation Model ◽  
Flow Pressure ◽  
Real Time Information

This paper describes the development and use of a dynamic simulation model and the implementation of a novel leak detection system. Experiences from the implementation and operation of the system will also be detailed from a user perspective. The dynamic model may be used for the transient simulation of the pipelines. The model was used to test the real-time leak detection system. The results of the simulation also prompted a change in the control scheme of the pipelines that resulted in less transient operation. The leak detection system is based upon rigorous thermodynamics and dynamic mass balance calculations driven by real-time information from field flow, pressure and temperature sensors. This system was successfully implemented to replace a simple volume balance system for NGL pipelines near Empress, Alberta.

Modeling of Ethylene in Pipeline Leak Detection System

2004 ◽  
Author(s):  
Jakob Bu¨chert
Keyword(s):  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Practical Experience ◽  
Time Model ◽  
Pump Stations ◽  
Pipeline Model ◽  
Improve Determination

This paper describes experiences with an improved equation of state (EOS) for ethylene for an existing real time pipeline model. The main scope of the model is leak detection, batch, contaminant and pig tracking. Altogether the pipeline model includes transportation of batched liquid ethylene, ethane, propane, butane and natural gas liquids (NGL). The pipeline is approximately 1900 miles miles long and includes laterals, 33 pump stations, 9 injection/delivery stations and 5 propane terminals. Originally the model used a BWRS EOS for all the above products. At that time a number of false leak alarms were experienced related to pipeline sections containing ethylene. A case study was carried out, specifically for ethylene, to investigate the effect of replacing the BWRS EOS with a modified Helmholtz EOS. The study showed that replacing the EOS on average would improve determination of the ethylene densities by 1.6%–5.6% with an expected reduction in the alarm rate for ethylene cases by approximately 50%. As a result the modified Helmholtz EOS was implemented in the real time model. Results are presented to show the practical experience with the new EOS gained over the last years.

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