This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Branagan, L. A. Search for Related Content Social Bookmarking                         What's this?

Introductory Use of Probabilistic Neural Networks for Spike Detection from an On-Line Vibration Diagnostic System

Pacific Gas & Electric Co. 3400 Crow Canyon Road San Ramon, CA 94583

We developed a two-part algorithm for detecting spikes in trended data from a vibra tion diagnostic monitoring system. Deviation from an exponential forecasting technique provides an initial screening of data for abnormal amplitudes. These abnormal events are classified as NOR MAL (i.e., no spikes), positive-going spikes (PSPIKE), or negative-going spikes (NSPIKE) by a PNN calculation trained on synthetic data using a Gaussian probability density function (PDF) esti mation. This screening reduces computational time and results in fewer false spike classifications compared with a probabilistic neural network (PNN) calculation alone. Results from sample field data sets showed that spike detection can be readily achieved, but that steps in the data lead to false spike classifications.

Journal of Intelligent Material Systems and Structures, Vol. 5, No. 2, 165-171 (1994)
DOI: 10.1177/1045389X9400500203


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?