## 4 Discussion  
It is important to revisit what proposed in Sections 2-3 from the point of view of the SHM, because the whole methodology has made no assumptions about the structure to simulate the expected life distribution under a specific task. Indeed, this approach is permeated by the experimental assessment of the structural dynamics and of the dynamic loading. But, as demonstrated by the examples of Section 3, the strong variability in the results can be explained only by possessing the whole reliable knowledge of the task-oriented dynamic event. Optical full-field measurement techniques, thanks to their qualities  
Figure 19: SLDV-based _frequency-to-failure distribution_ log-amplitude maps of Equation (15), obtained from _red noise-rap excitation_, as in Equation (12), from shaker 2: top view in (**a**) and 3D view in (**b**).  
Figure 18: SLDV-based _frequency-to-failure distribution_ log-amplitude maps of Equation (15), obtained from _red noise-rap excitation_, as in Equation (12), from shaker 1: top view in (**a**) and 3D view in (**b**).  
mentioned in Section 1, can extend the sensing to a fully-populated spatial domain, without the need of an a-priori selection of locations for the transducers. Furthermore, the shown results--of a single dof (here 550) in the whole map--demonstrate that, without imposing any simplifying assumption that might deteriorate the understanding of the dynamic responses, the selection of lumped sensors becomes quite complex in changing scenarios, if not impracticable. However, the quality of the structural measurements has an impact on the whole simulation chain, as underlined especially in Section 3.1, where at some frequency lines the noisy SLDV _mobilities_ gave misinterpretations of the derived quantities due to _scanning_-related issues during the measurement campaign [18; 24; 29]. Note that the _image_-based _full-field receptances_ proved in [6; 7; 8; 9; 10; 11; 12] to release much spatially smoother datasets than _scanning_-based techniques, without further noise reduction processing.  
As anticipated in Section 1, SHM should extend its assessments of the structural dynamics and loadings during the whole life of the components, especially when ageing, unpredicted accidents or changes happen. These assessments should be run periodically, to change iteratively the expectations on the duration of the parts and on the starting failure location, and to adapt the maintenance programs for them. This is the hard lesson learned from these simulated results: changes to the dynamic behaviour of the structure, as well as on the loadings, result in marked variability of the expected life distribution, therefore of the many managing and processing activities that can follow for a fruitful SHM.  
Also manufacturing can get advantages from this approach, specifically for allocating material where needed in mass optimisations and for determining technological aspects for the most solicited areas. Furthermore, quality assessments to find defects during the production of parts can be based on the same achievements, as in [8; 12].  
Future research activities are needed to assess the validity of the whole approach, e.g. by reaching the failure start and obtain the cumulative damage distribution in simple specimens, like the plate here used, under known excitation and environmental conditions. Once the procedure proves its accuracy on simple test cases, the experiment-based full-field fatigue hybrid predictions and SHM can be extended to more complex parts or assemblies.