Successful Offshore Coiled-Tubing Permanent Well-Abandonment Operation Uses Downhole Real-Time Parameters to Set Inflatable Packers with Surgical Precision in Cost-Effective Manner

2019 ◽  
Author(s):  
Cassiano Guimaraes ◽  
Eduardo Delgado ◽  
Lucio Galvao ◽  
Adriano Frotte ◽  
Manoel Gouveia ◽  
...  
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Coiled Tubing ◽  
Effective Manner ◽  
Time Parameters ◽  
Well Abandonment

Real-Time Process Quality Monitoring Using Wireless Sensor-Embedded Rotary Tools for Fastening Operations

2007 ◽  
Author(s):  
Deepak T. Mohan ◽  
Jeffrey Birt ◽  
Can Saygin ◽  
Jaganathan Sarangapani
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Value Added ◽  
Cost Effective ◽  
Base Station ◽  
Embedded Sensors ◽  
Optical Encoder ◽  
Effective Manner ◽  
Rotary Type

Fastening operations are extensively used in the aerospace industry and constitute for more than a quarter of the total cost. Inspection of fasteners is another factor that adds cost and complexity to the overall process. Inspection is usually carried out on a sampling-basis as a stand-alone process after the fastening process is completed. Lack of capability to inspect all fasteners in a cost effective manner and the need to remove non-value added activities, such as inspection by itself, in order to reduce the manufacturing lead time have been the motivation behind this study. This paper presents a novel diagnostics scheme based on Mahalanobis-Taguchi System (MTS) for monitoring the quality of rotary-type fastening operations in real-time. This approach encompasses (1) integrating a torque sensor, a pressure sensor, and an optical encoder on a hand-held rotary-type fastening tool; (2) obtaining process parameters via the embedded sensors and generating process signatures in real-time; and (3) detecting anomalies on the tool using a wireless mote that communicates the decision with a base station. The anomalies investigated in this study are the grip length variations as under grip and normal grip, and presence of re-used fasteners. The proposed scheme has been implemented on prototype rotary tool for bolt-nut type of fasteners and tested under a variety of experimental settings. The experimental results have shown that the proposed approach is successful, with an accuracy of over 95% in detecting grip lengths of fasteners in real-time during the process.

scholarly journals Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms

2006 ◽  
Vol 55 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Alex J. Stephens ◽  
Flavia Huygens ◽  
John Inman-Bamber ◽  
Erin P. Price ◽  
Graeme R. Nimmo ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Population Structure ◽  
Real Time ◽  
Real Time Pcr ◽  
Cost Effective ◽  
Resolving Power ◽  
Nucleotide Polymorphisms ◽  
Methicillin Resistant ◽  
Effective Manner ◽  
Mrsa Strains

The aim of this study was to identify a set of genetic polymorphisms that efficiently divides methicillin-resistant Staphylococcus aureus (MRSA) strains into groups consistent with the population structure. The rationale was that such polymorphisms could underpin rapid real-time PCR or low-density array-based methods for monitoring MRSA dissemination in a cost-effective manner. Previously, the authors devised a computerized method for identifying sets of single nucleotide polymorphisms (SNPs) with high resolving power that are defined by multilocus sequence typing (MLST) databases, and also developed a real-time PCR method for interrogating a seven-member SNP set for genotyping S. aureus. Here, it is shown that these seven SNPs efficiently resolve the major MRSA lineages and define 27 genotypes. The SNP-based genotypes are consistent with the MRSA population structure as defined by eburst analysis. The capacity of binary markers to improve resolution was tested using 107 diverse MRSA isolates of Australian origin that encompass nine SNP-based genotypes. The addition of the virulence-associated genes cna, pvl and bbp/sdrE, and the integrated plasmids pT181, pI258 and pUB110, resolved the nine SNP-based genotypes into 21 combinatorial genotypes. Subtyping of the SCCmec locus revealed new SCCmec types and increased the number of combinatorial genotypes to 24. It was concluded that these polymorphisms provide a facile means of assigning MRSA isolates into well-recognized lineages.

An Advanced Vibration Based Real-Time Machine Health and Process Monitoring System

2006 ◽  
Author(s):  
Chandra Jalluri ◽  
Prashanth Magadi ◽  
Mohan Viswanathan ◽  
Richard Furness ◽  
Werner Kluft ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Process Monitoring ◽  
Cost Effective ◽  
Cnc Machining ◽  
Numerical Control ◽  
Automotive Manufacturing ◽  
Manufacturing Plants ◽  
Effective Manner ◽  
Machine Health

The ever-increasing emphasis on product quality with increased productivity has been driving the automotive manufacturing industry to find new ways to produce high quality products without increasing production time and manufacturing costs. In addition, automotive manufacturing plants are implementing flexible manufacturing strategies with computer numerical control (CNC) machining centers to address excess capacity, shifting consumer trends and future volume uncertainty of products. Over time, plants have used several preventative and predictive maintenance methods to address machine reliability. Such systems include, but are not limited to, scheduling machine down times at regular intervals to check/replace bearings and other spindle/slide components before they can have an adverse affect on part quality. However, most of these methods and traditional systems are not cost effective and cause significant machine down-times, safety concerns and labor overheads and do not reliably monitor other process issues, such as, clamping, incoming stock variations and thermal phenomena. This paper describes an advanced real-time vibration based machine health and process monitoring system that has been developed to address the above issues. The system, called Condition Indicator Analysis Box for CNC (CIAB™-CNC), is easily configurable, and provides real-time data and historical trends of machines, processes and tooling, enabling manufacturing plants to make accurate predictions regarding future production runs. The system also aids in the optimization of preventative maintenance tasks in a cost effective manner. The developed system monitors machine spindle and slide for unbalance, misalignment, damaged/spalled bearings, mechanical looseness, and ball screw issues. Additionally, it performs in-process monitoring during machining as well as non-machining by individual tool and/or feature to detect tool breakages, quality issues and other gross process or machine anomalies. Innovative statistical trending algorithms enable the system to automatically adapt to valid process/parameter changes and significantly reduce the chances of false alarms and warnings. The developed system provides manufacturing plants with a tool to analyze machine tools and their associated components in an effort to gather information they can use effectively to make decisions regarding flexible machines, processes and tooling.

scholarly journals Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1708 ◽  
Author(s):  
Miguel Martin-Abadal ◽  
Ana Ruiz-Frau ◽  
Hilmar Hinz ◽  
Yolanda Gonzalez-Cid
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Warning System ◽  
Cost Effective ◽  
Marine Species ◽  
Global Scale ◽  
Tourism Industry ◽  
Commercial Fishing ◽  
Anthropogenic Pressures ◽  
Effective Manner

During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.

scholarly journals Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

2021 ◽  
Author(s):  
Jiarui Xie
Keyword(s):  
Real Time ◽  
Detection System ◽  
Region Of Interest ◽  
Cost Effective ◽  
Time Warping ◽  
Time Data ◽  
Fused Filament Fabrication ◽  
System A ◽  
Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

Real-Time Virtual Sensing of Component Damage Variables in a Gas Turbine Engine

2017 ◽  
Author(s):  
Juan-Pablo Afman ◽  
J. V. R. Prasad ◽  
Stephen Antolovich
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Financial Incentive ◽  
Cost Effective ◽  
Gas Turbine Engine ◽  
Operational Environment ◽  
Virtual Sensors ◽  
Turbine Engine ◽  
Effective Manner ◽  
Engine Maintenance

Accurate life prediction and monitoring for gas turbine engines has become increasingly important in recent years as commercial aircraft fleets are being offered through guaranteed engine maintenance programs, where plan rates are based on mission profiles, operating environment, operational hours and cycles accumulated. Hence, accurate monitoring and life predictions of critical engine components is associated with a tremendous financial incentive. A state of the art gas turbine engine carries up to 5000 sensors, which can be used to evaluate the performance of the engine. This data can be used to monitor engines in real-time, as well as collecting and analyzing that data after being streamed via satellite during flight, where algorithms can evaluate and prevent technical issues before they occur. The data collected provides engine manufacturers with early warnings related to failure diagnosis, and it enables airlines to schedule engine maintenance efficiently and in a cost effective manner. Due to the nature of the engine’s operational environment, sensors cannot be placed in certain areas of interest inside a gas turbine engine. Furthermore, thermo-mechanical models are often complex and computationally expensive to run in real time. Hence, in this work we describe the development of thermo-mechanical reduced models that can act as virtual sensors, in locations where real sensors cannot survive, and hence approximate damage variables at critical locations on a component of interest, which can be used for real-time diagnostics.

Effective Wind Tunnel Testing of Yacht Sails Using a Real-Time Velocity Prediction Program

2011 ◽  
Author(s):  
David Le Pelley ◽  
Peter Richards
Keyword(s):  
Wind Tunnel ◽  
Real Time ◽  
Full Range ◽  
Cost Effective ◽  
Recognition System ◽  
Wind Tunnel Testing ◽  
Prediction Program ◽  
Effective Manner ◽  
Accuracy And Repeatability ◽  
Tunnel Testing

Wind tunnel testing to determine yacht performance has been carried out for at least the last 50 years. A common perception is that experimental methods do not improve significantly over time. This paper shows how modern wind tunnel testing is still the only realistic way of providing a complete picture of aerodynamic performance over a full range of conditions in a rapid and cost-effective manner. The use of a Real-Time VPP and a sail shape recognition system combine to enhance the accuracy and repeatability of testing. The influence of examining boat speed instead of driving force is investigated.

Risk Management via Digital Dashboards in Statistics Data Centers

2020 ◽  
Vol 13 (1) ◽  
pp. 27-45
Author(s):  
Atif Amin ◽  
Raul Valverde ◽  
Malleswara Talla
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Data Center ◽  
Cost Effective ◽  
Risk Level ◽  
Total Risk ◽  
Counter Measures ◽  
Effective Manner ◽  
Risk Managers

Every system, when connected to a network, is susceptible to threat of being hacked. It is important to protect all systems of an organization in real-time in a cost-effective manner. This article presents a well-designed and integrated database for risk management data using a dashboard interface in real-time risk that makes it easy for risk managers to reach a understanding the level of threats to be able to apply right controls to mitigate them. In this article, a case study of a data center for a statistical management institute is presented that proposes the calculation of total risk at the organization level by using the proposed risk database. A digital dashboard is also designed for presenting the risk level results so that decision makers can apply counter measures. The risk level on a dashboard viewer makes it easy for decision maker to understand the overall risk level at the statistics data center and assists in the creation of a tool to follow-up risk management since the time a threat hits until the time of its mitigation.

scholarly journals FMS Dashboard - Descriptive Analytics and Preventive Maintenance

2019 ◽  
Vol 8 (3) ◽  
pp. 5280-5284
Keyword(s):  
Real Time ◽  
Preventive Maintenance ◽  
Management System ◽  
Cost Effective ◽  
Quality Data ◽  
Fleet Management ◽  
Past Performance ◽  
Effective Manner ◽  
Vehicle Activity ◽  
Iot Devices

Fleet tracking or vehicle tracking allows businesses in a variety of industries to keep track of their vehicle fleet in a convenient and cost-effective manner. But with IOT devices connected to each vehicle, it produces huge amount of data, which is an overload to the users of Fleet Management System (FMS). This data itself is not valuable unless it can be analyzed and interpreted correctly. Quality data can help fleet owners to understand the efficiency, driver safety, expenses and profitability of owning and managing their fleet. In this proposed work, we developed a dashboard for the existing Fleet Management System which will provide descriptive analytics and support in preventive maintenance of the fleet. The FMS Dashboard is a key module of the FMS system. This module uses GPS data like vehicle starts, stops, and idling, fuel consumption, engine running hours, vehicle speeds and location from each vehicle to provide real-time useful insights on vehicle activity, driver behavior and tracking of fleet. Based on historic data, descriptive analytics will summarize past performance of the vehicle to enable users to plan for maintenance to perform. Also preventive maintenance reports helps the fleet owners to estimate, when the vehicle service is due and plan for the same. Apart from this, vehicle can be tracked real-time in Google Maps.

scholarly journals In-house Reverse transcriptase polymerase chain reaction for detection of SARS-CoV2 with increased Sensitivity

2021 ◽  
Author(s):  
Manash Jyoti Kalita ◽  
Kalpajit Dutta ◽  
Gautam Hazarika ◽  
Ridip Dutta ◽  
Simanta Kalita ◽  
...  
Keyword(s):  
Multiplex Pcr ◽  
Real Time ◽  
Cost Effective ◽  
Sybr Green ◽  
Rdrp Gene ◽  
Green Approach ◽  
Resource Limited ◽  
Effective Manner ◽  
Wide Range ◽  
Primer Sets

Abstract Background:With the increasing COVID-19 infection worldwide, economization of the existing RT-PCR based detection assay becomes the need of the hour. Methods: An assessment of optimal PCR conditions for simultaneous amplification for E, S and RdRp gene of SARS-CoV-2 has been made using both fast traditional and multiplex real time PCR using same primer sets. All variables of practical value were studied by amplifying known target-sequences from ten-fold dilutions of archived positive samples of COVID-19. Results: The designed primers for amplification of E, S and RdRp gene of SARS-Cov-2 in single tube Multiplex PCR amplifications have shown efficient amplification of the target region in 37 minutes using thermal cyclers and 169 minutes with HRM based Real time detection using SYBR green master mix, over a wide range of template concentration, and the results were in good concordance with the commercially available detection kits. Conclusion: This fast HRM based Real time multiplex PCR with SYBR green approach offers rapid and sensitive detection of SARS-CoV-2 in a cost effective manner apart from the added advantage of primer pair’s compatibility for use in Traditional multiplex PCR, which offers extended applicability of the assay protocol in resource limited setting.

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