Abstract
The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for diverse applications, ranging from power plant operation to homeland security. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden events inside intermittent signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.
| Original language | English |
|---|---|
| Pages (from-to) | 489-510 |
| Number of pages | 22 |
| Journal | Annals of Nuclear Energy |
| Volume | 38 |
| Issue number | 2-3 |
| DOIs | |
| State | Published - Feb 2011 |
Funding
We thank Drs. J. Antonino-Daviu and J. Roger-Folch, Electrical Engineering Department, Universitat Politecnica de Valencia, Spain, for providing the induction machine data. C.C.-P. gratefully acknowledges Dr. Steven R. Long, NASA/GSFC/Wallops Flight Facility for providing the Hilbert Transform Algorithm. The Oak Ridge National Laboratory is managed by UT-Battelle, LLC, under Contract DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains, and the publisher by accepting the article for publication, acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| Oak Ridge National Laboratory | |
| UT-Battelle | DE-AC05-00OR22725 |
Keywords
- Detection
- Non-stationary
- Nonlinear
- Signal