An inherent difficulty in sensor-data-driven fault detection is that the detection performance
could be drastically reduced under sensor degradation (eg, drift and noise). Complementary …

Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical
faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault …

This paper develops a novel strategy for prediction of lean blowout in gas turbine
combustors based on symbolic analysis of time series data from optical sensors, where the …

This paper presents a new data-driven framework for unsupervised system-wide anomaly
detection for modern distributed complex systems within which there exists a strong …

Tools of sensor-data-driven anomaly detection facilitate condition monitoring of dynamical
systems especially if the physics-based models are either inadequate or unavailable. Along …

The analytic signal (AS) was proposed by Gabor as a complex signal corresponding to a
given real signal. The AS has a one-sided spectrum and gives rise to meaningful spectral …

Phase-space discretization is a necessary step for study of continuous dynamical systems
using a language-theoretic approach. It is also critical for many machine learning …

The concept of symbolic dynamics has been used in recent literature for feature extraction
from time series data for pattern classification. The two primary steps of this technique are …

Phase-space discretization is a necessary step for study of continuous dynamical systems
using a symbolic dynamics and language-theoretic approach. It is also critical for many …

We propose Hilbert transform and analytic signal construction for signals over graphs. This
is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in …