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Data-driven state space models allow accurate descriptions of nonlinear systems, as they represent a general and abstract modeling approach. With the help of the model structure according to the canonical controllable form, it is possible to determine the main nonlinear influences on the process and thus, carry out learning-based categorization. Within this contribution, we explain and test categorization with the help of the Local Model State Space Network and show how conclusions about the underlying process can be drawn. Especially for time-variant behavior, the method provides the possibility to categorize the process in terms of its nonlinear characteristics to enable fault diagnosis.

Nonlinear system categorization for structural data mining with state space models