Abstract: With increasing complexity in electronic systems
there is a need for system level anomaly detection and fault isolation.
Anomaly detection based on vector similarity to a training set is used
in this paper through two approaches, one the preserves the original
information, Mahalanobis Distance (MD), and the other that
compresses the data into its principal components, Projection Pursuit
Analysis. These methods have been used to detect deviations in
system performance from normal operation and for critical parameter
isolation in multivariate environments. The study evaluates the
detection capability of each approach on a set of test data with known
faults against a baseline set of data representative of such “healthy"
systems.
Abstract: A study was conducted to formally characterize
notebook computer performance under various environmental and
usage conditions. Software was developed to collect data from the
operating system of the computer. An experiment was conducted to
evaluate the performance parameters- variations, trends, and
correlations, as well as the extreme value they can attain in various
usage and environmental conditions. An automated software script
was written to simulate user activity. The variability of each
performance parameter was addressed by establishing the empirical
relationship between performance parameters. These equations were
presented as baseline estimates for performance parameters, which
can be used to detect system deviations from normal operation and
for prognostic assessment. The effect of environmental factors,
including different power sources, ambient temperatures, humidity,
and usage, on performance parameters of notebooks was studied.