  We propose a novel change-point detection method based on online Dynamic Mode
Decomposition with control (ODMDwC). Leveraging ODMDwC's ability to find and
track linear approximation of a non-linear system while incorporating control
effects, the proposed method dynamically adapts to its changing behavior due to
aging and seasonality. This approach enables the detection of changes in
spatial, temporal, and spectral patterns, providing a robust solution that
preserves correspondence between the score and the extent of change in the
system dynamics. We formulate a truncated version of ODMDwC and utilize
higher-order time-delay embeddings to mitigate noise and extract broad-band
features. Our method addresses the challenges faced in industrial settings
where safety-critical systems generate non-uniform data streams while requiring
timely and accurate change-point detection to protect profit and life. Our
results demonstrate that this method yields intuitive and improved detection
results compared to the Singular-Value-Decomposition-based method. We validate
our approach using synthetic and real-world data, showing its competitiveness
to other approaches on complex systems' benchmark datasets. Provided guidelines
for hyperparameters selection enhance our method's practical applicability.
