This paper presents a method to detect transient
disturbances in a multivariate context, and an extension of that
method to handle multirate systems. Both methods are based
on a time series analysis technique known as nearest neighbors,
and on multivariate statistics implemented as a singular value
decomposition. The motivation for these developments is that
there is an increasing industrial requirement for the analysis
of data sets comprising measurements from industrial processes
together with their associated electrical and mechanical
equipment. These systems are increasingly affected by transient
disturbances, and their measurements are commonly sampled
at different rates. This paper demonstrates superior results
with the multivariate method in comparison with the univariate
approach, and with the multirate method in comparison to
a unirate method, for which the fast-sampled measurements
had to be downsampled. The method is demonstrated on
experimental and industrial case studies.