7th International Electronic Conference on Sensors and Applications -

Session 1. Structural Health Monitoring Technologies and Sensor Networks

Damage and Material-state Diagnostics with Predictor Functions using Data Series Prediction and Artificial Neural Networks

The main objective of this work is the transition of destructive material testing towards non-destructive testing methods and the investigation of predictor functions derived by Machine learning applied to destructive testing methods like tensile tests. The output provides information about the material state and damages in advance, i.e., dealing with functionals *f*(*x*): *x* → *y*, where *x* is the strain (length) and *y* the stress (load force) variable. The scope of this work focuses on learning of predictor functions by using simple commonly used stateless forward and state-based recurrent neural networks providing two outputs separately: (1) The prediction of damage events by past recorded data with functions of the form *f*(*Y*~0~): *Y*~0~ → *x*~dam~, where *Y* is a data point series of the load force (stress) variable *y*, and (2) The prediction of two-dimensional data point series (e.g., strain-stress curves) with functions of the form *f*~Δ~(*Y*): *Y* → *Y*~Δ~ and the aim to predict (extrapolate) the development of the function for a progressive difference Δ*x* of the strain variable. I.e., we derive new function sets 𝔽{*f*~1~,..,*f*~n~} from training data to predict the material behaviour and state transitions (e.g., from elastic to plastic behaviour).