Structural Health Monitoring: from Structures to Systems-of-Systems★

Almost all engineering disciplines face the problem of damage detection and assessment. Although the terminology might change between disciplines, the problems faced, and the methods of solution, show great commonality. The objective of this paper is to discuss how Structural Health Monitoring (SHM) is carried out in the context of aerospace and civil structural monitoring and to draw parallels with how damage detection is conducted within the electrical, control and process engineering communities. A four-stage methodology for SHM, based on machine learning is discussed. The different levels of diagnostic information available depend on the type of data available for learning and this is also discussed. Detection alone can be carried out using normal condition data only and this is achieved using supervised learning where detection thresholds are critical. If damage state data are available, supervised learning can be applied and more diagnostic information becomes available. The various concepts are discussed in terms of a number of case studies drawn from wind turbine monitoring programmes; this allows the illustration of both SHM methods and machine condition monitoring methods. The possibility of moving towards a population-based approach to SHM applicable to systems-of-systems is outlined via one of the case studies.