智能工作与艰苦工作:用于资产完整性监测的无线UT传感器

Steve Strachan
{"title":"智能工作与艰苦工作:用于资产完整性监测的无线UT传感器","authors":"Steve Strachan","doi":"10.32548/2024.me-04409","DOIUrl":null,"url":null,"abstract":"In the 21st century of NDE technology and deployment, monitoring technologies and strategies are completely different than inspection technologies and strategies. Monitoring is no longer sending a technician to a location to take repeated manual data – this is still inspection, just more frequently. Monitoring takes place after a manual/automated inspection has located an anomaly and instrumentation (a sensor in this case), is installed to take recurring readings at that precise location to gather a large sample size of data to be analyzed. These two strategies, when employed in concert, even only on the most critical (10%) circuits/tanks/units, are optimizing asset health, reducing unplanned downtime, and eliminating the need to have people in potentially unsafe situations to the tune of tens of millions of dollars per year. This article will provide real life examples of the ~20% of the best run refineries, chemical, paper, and power plants which have cracked the code and are successfully using an inspect to monitor strategy to change the game of how they implement asset integrity in the 21st century. As you read this article, ask yourself the question, is my facility a part of this ~20%? If not, what can I start doing, and what incremental step changes can I make to my legacy inspection & asset integrity strategy, to use technology to spend my precious budget and resources to be a part of this ~20% in 2024 and beyond?\n\nMany asset owners struggle to identify the root cause of fluctuating corrosion rates due to unreliable, infrequent, or sheer lacks of quantity (and quality) of inspection data to make informed decisions on asset health. Facilities worldwide are tasked with monitoring thousands of Condition Monitoring Locations (CMLs) with established NDE techniques such as manual ultrasonic testing and radiography. While these techniques can provide valuable “snapshots” of the condition of particular locations, limitations and inherent errors can compound leading to ill-advised decision making. Manually taken thickness data can vary greatly and result in unwarranted complacency or excessive and costly inspections. Utilizing long range wireless continuous monitoring systems have drastically improved operators understanding of how process changes influence corrosion rates. Installed UT sensors provide near real-time data with a 0.001” accuracy. This influx of data provides a trend line whereas with manually taken data, sometimes only taken a few times a year, only provides a few points scattered across a graph. Asset owners have realized they can use this wealth of new information to validate and discover the effects of operational changes on corrosion rates and make more informed decisions to impact asset extension, retirement, and increasing the efficiency of assets during its useful life.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Working Smart versus Working Hard: Wireless UT Sensors for Asset Integrity Monitoring\",\"authors\":\"Steve Strachan\",\"doi\":\"10.32548/2024.me-04409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the 21st century of NDE technology and deployment, monitoring technologies and strategies are completely different than inspection technologies and strategies. Monitoring is no longer sending a technician to a location to take repeated manual data – this is still inspection, just more frequently. Monitoring takes place after a manual/automated inspection has located an anomaly and instrumentation (a sensor in this case), is installed to take recurring readings at that precise location to gather a large sample size of data to be analyzed. These two strategies, when employed in concert, even only on the most critical (10%) circuits/tanks/units, are optimizing asset health, reducing unplanned downtime, and eliminating the need to have people in potentially unsafe situations to the tune of tens of millions of dollars per year. This article will provide real life examples of the ~20% of the best run refineries, chemical, paper, and power plants which have cracked the code and are successfully using an inspect to monitor strategy to change the game of how they implement asset integrity in the 21st century. As you read this article, ask yourself the question, is my facility a part of this ~20%? If not, what can I start doing, and what incremental step changes can I make to my legacy inspection & asset integrity strategy, to use technology to spend my precious budget and resources to be a part of this ~20% in 2024 and beyond?\\n\\nMany asset owners struggle to identify the root cause of fluctuating corrosion rates due to unreliable, infrequent, or sheer lacks of quantity (and quality) of inspection data to make informed decisions on asset health. Facilities worldwide are tasked with monitoring thousands of Condition Monitoring Locations (CMLs) with established NDE techniques such as manual ultrasonic testing and radiography. While these techniques can provide valuable “snapshots” of the condition of particular locations, limitations and inherent errors can compound leading to ill-advised decision making. Manually taken thickness data can vary greatly and result in unwarranted complacency or excessive and costly inspections. Utilizing long range wireless continuous monitoring systems have drastically improved operators understanding of how process changes influence corrosion rates. Installed UT sensors provide near real-time data with a 0.001” accuracy. This influx of data provides a trend line whereas with manually taken data, sometimes only taken a few times a year, only provides a few points scattered across a graph. Asset owners have realized they can use this wealth of new information to validate and discover the effects of operational changes on corrosion rates and make more informed decisions to impact asset extension, retirement, and increasing the efficiency of assets during its useful life.\",\"PeriodicalId\":505083,\"journal\":{\"name\":\"Materials Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Evaluation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32548/2024.me-04409\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32548/2024.me-04409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在无损检测技术和部署的 21 世纪,监测技术和策略与检测技术和策略完全不同。监测不再是派遣一名技术人员到某个地点重复采集人工数据--这仍然是检查,只是频率更高。监测是在人工/自动检查发现异常后,安装仪器(这里指传感器),在精确位置重复读取数据,收集大量数据样本进行分析。这两项策略在配合使用时,即使仅在最关键(10%)的回路/储罐/装置上使用,也能优化资产健康状况,减少意外停机时间,避免人员处于潜在的不安全状态,每年可节省数千万美元。本文将举例说明约 20% 运行最好的炼油厂、化工厂、造纸厂和发电厂的真实情况,这些企业已经破解了密码,并成功地使用了检测监控策略,从而改变了他们在 21 世纪如何实施资产完整性的游戏规则。在阅读这篇文章时,请扪心自问,我的工厂是这约 20% 的一部分吗?如果不是,我可以从哪些方面入手,对我的传统检测和资产完整性战略进行哪些渐进式的改变,从而利用技术手段将我宝贵的预算和资源用于在 2024 年及以后成为这 ~20% 的一部分?由于检测数据不可靠、不频繁或数量(和质量)不足,许多资产所有者难以确定腐蚀率波动的根本原因,从而无法对资产健康状况做出明智的决策。世界各地的设施都在使用人工超声波检测和射线照相等成熟的无损检测技术,对成千上万个状态监测点(CML)进行监测。虽然这些技术可以为特定位置的状况提供有价值的 "快照",但其局限性和固有误差会导致决策失误。人工采集的厚度数据可能会有很大差异,从而导致不必要的自满情绪或过度昂贵的检查。长距离无线连续监测系统的使用极大地提高了操作人员对工艺变化如何影响腐蚀率的认识。安装的 UT 传感器可提供精度为 0.001 英寸的近实时数据。大量涌入的数据提供了一条趋势线,而人工采集的数据(有时一年只采集几次)只能提供图表中分散的几个点。资产所有者已经意识到,他们可以利用这些丰富的新信息来验证和发现操作变化对腐蚀率的影响,并做出更明智的决策,从而影响资产的延长、报废,并提高资产在使用寿命内的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Working Smart versus Working Hard: Wireless UT Sensors for Asset Integrity Monitoring
In the 21st century of NDE technology and deployment, monitoring technologies and strategies are completely different than inspection technologies and strategies. Monitoring is no longer sending a technician to a location to take repeated manual data – this is still inspection, just more frequently. Monitoring takes place after a manual/automated inspection has located an anomaly and instrumentation (a sensor in this case), is installed to take recurring readings at that precise location to gather a large sample size of data to be analyzed. These two strategies, when employed in concert, even only on the most critical (10%) circuits/tanks/units, are optimizing asset health, reducing unplanned downtime, and eliminating the need to have people in potentially unsafe situations to the tune of tens of millions of dollars per year. This article will provide real life examples of the ~20% of the best run refineries, chemical, paper, and power plants which have cracked the code and are successfully using an inspect to monitor strategy to change the game of how they implement asset integrity in the 21st century. As you read this article, ask yourself the question, is my facility a part of this ~20%? If not, what can I start doing, and what incremental step changes can I make to my legacy inspection & asset integrity strategy, to use technology to spend my precious budget and resources to be a part of this ~20% in 2024 and beyond? Many asset owners struggle to identify the root cause of fluctuating corrosion rates due to unreliable, infrequent, or sheer lacks of quantity (and quality) of inspection data to make informed decisions on asset health. Facilities worldwide are tasked with monitoring thousands of Condition Monitoring Locations (CMLs) with established NDE techniques such as manual ultrasonic testing and radiography. While these techniques can provide valuable “snapshots” of the condition of particular locations, limitations and inherent errors can compound leading to ill-advised decision making. Manually taken thickness data can vary greatly and result in unwarranted complacency or excessive and costly inspections. Utilizing long range wireless continuous monitoring systems have drastically improved operators understanding of how process changes influence corrosion rates. Installed UT sensors provide near real-time data with a 0.001” accuracy. This influx of data provides a trend line whereas with manually taken data, sometimes only taken a few times a year, only provides a few points scattered across a graph. Asset owners have realized they can use this wealth of new information to validate and discover the effects of operational changes on corrosion rates and make more informed decisions to impact asset extension, retirement, and increasing the efficiency of assets during its useful life.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Machine Vision–Based Tools for Automotive Service and Repair Visual Testing Method Personnel Qualification and Certification: An Overview Robotic Crawlers For Visual Testing RVI For Internal Health Monitoring Of Industrial Gas Turbines Robotic Visual Inspection in Confined Spaces
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1