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Electrochemical corrosion failure analysis of large complex engineering structures by using micro-LPR sensors

机译:使用微型LPR传感器的大型复杂工程结构的电化学腐蚀失效分析

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This paper presents the effects of three major parameters; temperature, relative humidity and hygroscopic salts contaminants on the atmospheric corrosion of large steel structures. The effects of these three parameters have been analysed by using micro-sized LPR sensors to continuously monitor the corrosion rate of a degrading large structure under varying parameters. A long term, three years study was performed by deploying μ LPRs on strategically selected large military vehicles (main battlefield tanks), which are stationed in the Tank Museum at Bovington, UK. These vehicles are operational and are of historic significance with cultural biography, however structural deterioration through corrosion, corrosion fatigue, stress corrosion cracking and mechanical failures are a threat to these vehicles in terms of their conservation. A set of vehicles operational (uncontrolled environment) and non-operational (controlled environment) was selected for comparative analysis in context of corrosion rate. This research is founded on a novel real-time corrosion monitoring technique that enables to better understand the relationship between varying environmental parameters and corrosion rate of large steel-based mobile structures during operation. This research provides a synthesis of real time corrosion data, which has been accumulated over a period of three years. An overview of structural deterioration is presented and derived from a significantly large data, therefore it provides a more reliable and highly accurate assessment of failures due to corrosion.
机译:本文介绍了三个主要参数的影响。温度,相对湿度和吸湿性盐污染物对大型钢结构的大气腐蚀。通过使用微型LPR传感器在变化的参数下连续监控降解大型结构的腐蚀速率,可以分析这三个参数的影响。一项为期三年的长期研究是通过在战略性选择的大型军用车辆(主战场坦克)上部署μLPR进行的,这些驻军位于英国Bovington的坦克博物馆。这些车辆可操作,具有文化传承的历史意义,但是由于腐蚀,腐蚀疲劳,应力腐蚀开裂和机械故障而导致的结构恶化,对这些车辆的保护构成了威胁。选择了一组车辆运行(非受控环境)和非运行(受控环境)进行腐蚀速率的比较分析。这项研究基于一种新颖的实时腐蚀监测技术,该技术能够更好地了解操作过程中大型钢基移动结构的环境参数与腐蚀速率之间的关系。这项研究提供了实时腐蚀数据的综合,该数据已累积了三年。给出了结构退化的概述,并从大量数据中得出,因此,它提供了更可靠,更准确的腐蚀归因评估。

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