Chapter 17 An Experimental Investigation of Feature Availability in Nominally Identical Structures for Population-Based SHM Evangelos Papatheou, Robert J. Barthorpe, and Keith Worden Abstract It is perhaps well known that the uncertainty in realistic structures may complicate most efforts for modelling and damage identification. In a population of structures which are considered identical, as in a wind farm for example, it is very often that the accurate modelling of one structure will be inadequate for the robust monitoring of the rest in an SHM approach. This paper presents an exploration of the common features which can be found in nominally identical structures and which can be used for damage identification with the ultimate purpose of population-based SHM. The concept of a population-based approach means that any additional new structures to the population will not need to be fully modelled in order to be monitored. Two different variants of the tail wing of a Piper PA-28 aircraft are used to create two pairs of nominally identical structures by separating the tail wings in half. The new population of four structures thus contains two pairs of them which are similar, but they have different length and different weight. A full modal test is performed in all of the structures and an exploration of possible common features is also done. The results show that common damage-sensitive features exist across the structures, a key requirement if population-based SHM is to be successfull. Keywords Feature selection • Population-based • SHM • Vibration-based • Modal testing 17.1 Introduction The idea of population-based monitoring has been implemented in the medical community as in the disease surveillance field [1], where it has been successful, and several systems are currently operational. The extension of the disease surveillance into structural health monitoring (SHM) has been presented before in [2]. The concept of a population-based approach means that in a population of nominally identical structures, such as in a wind farm, any additional new structures will not need to be fully modelled in order to be monitored. This can prove not only advantageous, but also a cost effective solution to large parts of modern infrastructure. In reality, the uncertainty that even nominally identical structures may present will complicate any such efforts of population-based monitoring. In [3], two different variants of the tail wing of a Piper PA-28 aircraft were used to create two pairs of nominally identical structures by separating them in half. Then the structures were tested and the existence of common patterns among them was explored in their natural frequencies and mode shapes. The study confirmed that while there were differences in the frequencies and modes, as expected, there were also common patterns in their behaviour. The purpose of this paper is to extend this comparison to all four structures and to further explore the potential of common features which can be used for the health monitoring of all of them. The whole concept of population-based SHM can be illustrated in Fig. 17.1. The top part of the diagram, highlighted in green, displays the general framework of a pattern recognition approach to SHM as it is generally defined in [4] for an arbitrary structure. The process eventually defines a mapping between the normal (N1 in the diagram of Fig. 17.1) and a damaged (D1) condition through a feature selection/classification approach. This process is generally adapted to specific structures and in the case of significant variations it will most likely have to be repeated. However, if the structures are related or common patterns can be identified, then a separate mapping among the normal conditions (highlighted in blue in the diagram) can lead to a mapping among the obtained damage indication functionals (highlighted in yellow), which will in turn allow for the identification of unknown damage conditions in new structures (highlighted in brown in Fig. 17.1). The functional which describes the mapping from a normal to a damaged condition will be based on features, and a feature in this concept is a set of data measured or derived from measured data which can be used to individually identify a structure and E. Papatheou ( ) • R.J. Barthorpe • K. Worden Department of Mechanical Engineering, Dynamics Research Group, University of Sheffield, Sir Frederick Mappin Building, Mappin Street, Sheffield, South Yorkshire S1 3JD, UK e-mail: e.papatheou@sheffield.ac.uk © The Society for Experimental Mechanics, Inc. 2015 C. Niezrecki (ed.), Structural Health Monitoring and Damage Detection, Volume 7, Conference Proceedings of the Society for Experimental Mechanics Series, DOI 10.1007/978-3-319-15230-1_17 185
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