Proving MEMS Technologies for Smarter Railway Infrastructure. (2016)

• Record Type: Journal Article
• Authors: Milne, David; Le Pen, Louis; Watson, Geoff; Thompson, David; Powrie, William; Hayward, Mick; Morley, Simon
• Is Part Of: Procedia engineering. Volume 143(2016)
• Page Start: 1077
• Page End: 1084
• Publication Date: 2016
• Subjects: MEMS Technology -- Railway Tack -- Structural Health Monitoring; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2016.06.222
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Quantifying how railway track responds to passing trains in terms of displacement, velocity or acceleration, can provide insights into both the performance and the condition of the track. A number of trackside monitoring technologies have been shown to be capable of providing this information; however these are primarily research tools and tend to be costly hence actual deployments are relatively limited in scope. To assess systematically the changing health of railway track, more cost-effective continuous approaches to monitoring are required. Micro electrical mechanical systems (MEMS) are commonplace sensors in consumer electronics, low cost and can be used to measure acceleration. Thus they have the potential to provide the kind of data required to assess railway track behaviour at a much lower cost and in an environmentally robust small deployment package. However confidence in the quality of the data is required. This paper discusses the criteria for the selection of MEMS devices for this application. Laboratory trials and direct comparison of trackside measurements with well-established monitoring techniques demonstrate the effectiveness of the selected MEMS devices, and show their potential for use in continuous monitoring schemes to evaluate changes in track performance. The paper thus provides evidence that these kinds of low cost technologies are suitable for railway applications, building confidence in their use and enabling their adoption in self-monitoring smart infrastructure.