  This article addresses the following problems: 1) First, a nonlinearity
analysis is made looking for the presence of nonlinearities in an early phase
of the identification process. The level and the nature of the nonlinearities
should be retrieved without a significant increase in the amount of measured
data. 2) Next it is studied if it is safe to use a linear system identification
approach, even if the presence of nonlinear distortions is detected. The
properties of the linear system identification approach under these conditions
are studied, and the reliability of the uncertainty bounds is checked. 3)
Eventually, tools are provided to check how much can be gained if a nonlinear
model were identified instead of a linear model.
  Addressing these three questions forms the outline of this article. The
possibilities and pitfalls of using a linear identification framework in the
presence of nonlinear distortions will be discussed and illustrated on
lab-scale and industrial examples.
  In this article, the focus is on nonparametric and parametric black box
identification methods, however the results might also be useful for physical
modeling methods. Knowing the actual nonlinear distortion level can help to
choose the required level of detail that is needed in the physical model. This
will strongly influence the modeling effort. Also, in this case, significant
time can be saved if it is known from experiments that the system behaves
almost linearly. The converse is also true. If the experiments show that some
(sub-)systems are highly nonlinear, it helps to focus the physical modeling
effort on these critical elements.
