Understanding mechanisms of materials deterioration during service life is fundamental for their confident use in the building sector. This work presents analysis of time series of data related to wood weathering acquired at three scales (molecular, microscopic, macroscopic) with different sensors. By using several complementary techniques, the material description is precise and complete; however, the data provided by multiple equipment are often not directly comparable due to different resolution, sensitivity and/or data format. This paper presents an alternative approach for multi-sensor data fusion and modelling of the deterioration processes by means of PARAFAC model. Time series data generated within this research were arranged in a data cube of dimensions samples × sensors × measuring time. The original protocol for data fusion as well as novel meta parameters, such as cumulative nested biplot, was proposed and tested. It was possible to successfully differentiate weathering trends of diverse materials on the basis of the NIR spectra and selected surface appearance indicators. A unique advantage for such visualization of the PARAFAC model output is the possibility of straightforward comparison of the degradation kinetics and deterioration trends simultaneously for all tested materials.
Multi-sensor data fusion and parallel factor analysis reveals kinetics of wood weathering / Sandak, Jakub; Sandak, Anna; Cocchi, Marina. - In: TALANTA. - ISSN 0039-9140. - 225(2020), pp. 122024-122048.
|Data di pubblicazione:||2020|
|Data di prima pubblicazione:||17-dic-2020|
|Titolo:||Multi-sensor data fusion and parallel factor analysis reveals kinetics of wood weathering|
|Autore/i:||Sandak, Jakub; Sandak, Anna; Cocchi, Marina|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.talanta.2020.122024|
|Codice identificativo Scopus:||2-s2.0-85098093349|
|Citazione:||Multi-sensor data fusion and parallel factor analysis reveals kinetics of wood weathering / Sandak, Jakub; Sandak, Anna; Cocchi, Marina. - In: TALANTA. - ISSN 0039-9140. - 225(2020), pp. 122024-122048.|
|Tipologia||Articolo su rivista|
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