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Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges

机译：铁路桥梁结构健康监测和故障检测：最新方法和未来挑战

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摘要

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network-based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted.
机译：由于旅客旅行和货物运输的需求不断增长，铁路在运输行业中的重要性不断提高。但是，欧洲300,000座铁路桥梁中超过35％已有100多年的历史，其可靠性直接影响铁路网络的可靠性。需求的增加可能导致与他们的意外故障相关的更高风险，从而给乘客带来安全隐患，并增加资产的整个生命周期成本。因此，评估整个铁路运输系统可靠性的最重要方面之一就是桥梁结构健康监测，它可以通过允许早期发现故障来监测桥梁的健康状态。因此，可以实现桥梁的最佳健康状态的快速，安全和经济高效的恢复，在该状态下可以有效地保持元件退化或故障的水平。在本文中，在介绍了结构健康监测的所需功能之后，对最常用的桥梁故障检测方法进行了介绍。分析主要集中在基于模型的有限元更新策略，基于非模型（数据驱动）的故障检测方法（例如人工神经网络）和基于贝叶斯信念网络的结构健康监测方法。然后，通过分析铁路桥梁的短跨度钢结构，提出了一项旨在研究和比较所审查类型的结构健康监测方法的性能的比较研究。重点介绍了铁路桥梁故障检测方法的机遇和未来的挑战。

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