  Detecting structural changes in functional data is a prominent topic in
statistical literature. However not all trends in the data are important in
applications, but only those of large enough influence. In this paper we
address the problem of identifying relevant changes in the eigenfunctions and
eigenvalues of covariance kernels of $L^2[0,1]$-valued time series. By
self-normalization techniques we derive pivotal, asymptotically consistent
tests for relevant changes in these characteristics of the second order
structure and investigate their finite sample properties in a simulation study.
The applicability of our approach is demonstrated analyzing German annual
temperature data.
