Summary: NEURAL NETWORKS IN FAULT DETECTION: A Case Study
D.R. Hush, C.T. Abdallah, G.L. Heileman, and D. Docampo
University of New Mexico,
Albuquerque, NM 87131, USA.
In this paper we study the applications of neural
nets in the area of fault detection. In particular,
neural networks are used for fault detection in real
vibrational data. The study is one of the first to
include a large set of real vibrational data and to
illustrate the potential as well as to the limitations
of neural networks for fault detetction.
There has been considerable work in the areas of
fault detection and isolation which were reviewed in
, , . We first comment on standard designs
to detect faults using hardware redundancy. This
approach relies on duplicating the functionality of