In cooperation with MACSEA Ltd., the Univer 
ables,  interfaces  with  DEXTER  (an  agent  based
sity of New Orleans, College of Engineering (UNO
monitoring  system)  and  includes  various  failure
COE)  developed  software  for  automated  machin 
models for fault recognition and diagnostics.  This
ery  diagnostic  reasoning.    This  Condition Based
software system improves engine performance and
Maintenance (CBM) philosophy is a proven strat 
reliability through prudent and robust monitoring.
egy that supports minimum maintenance and in 
The  knowledge  base  of  the  system  is  extended
creases machinery availability.  CBM typically in 
through the continued use of the system due to its
volves  increasing  levels  of  machinery  plant
learning capability.  It reduces user workload for
automation.  The objective was to design an agent
monitoring tasks as well as provides data for fu 
capable of continuous real time machine learning
ture system design improvements.
by  using  an  Artificial  Neural  Network  (ANN),
known as the cerebellar model articulation control 
ler.  An engine simulator that can model both nor 
Wave  Software System
mal and faulty engine operations was used to de 
velop  the  learning  system  controller  in  a  flexible
The University of New Orleans, College of Engi 
and cost efficient manner.
neering collaborated with industry in a prosperous
Eliminating unnecessary maintenance tasks can
manner, which lead to a technology transfer of soft 
save both human and maintenance resources, which
ware development initially designed for Naval ap 
reduces the total ownership cost of ship machinery
plications.  This Wave  software package will al 
plants.  As automation levels increase, the amount
low  industry  to  take  advantage  of  research  and
of machinery data that maintenance personnel must
development efforts performed for the Navy.
track and assimilate can become extensive.  Intelli 
gent,  diagnostic,  and  prognostic  software  agents
The University of New Orleans, College of Engi 
assist  people  in  monitoring  and  troubleshooting
neering (UNO COE) developed software that was
complex machinery processes.  Agents perform te 
designed to store, monitor, and investigate failures
dious, repetitive, and analytically complex diagnos 
experienced from initial factory testing through ship
tic  tasks,  reporting  only  when  exceptions  are  de 
delivery.  This software was developed for Northrop
tected.   They  are  deployed  to  identify  machinery
Grumman Ship Systems in its construction of LPD
conditions that should trigger maintenance activi 
17 (USS San Antonio) for the U.S. Navy.  UNO COE
ties before equipment failures occur.
saw a need in industry for this software to be used
Diagnostic  inference  involves  sensing  perfor 
outside of the military envelope.  The commercial 
mance abnormalities by comparing measured ma 
ized  version  of  the  software  is  called  Wave .
chinery performance to a known baseline.  This com 
Wave   is  the  only  Reliability,  Availability,  and
parison yields symptoms that characterize faults.
Maintainability (RAM) software package designed
The accuracy of the baseline performance has a di 
specifically for the maritime industry.  Its unique
rect impact on the robustness of the diagnostic sys 
features allow maritime operators to lower main 
tem, which in turn can impact the effectiveness of
tenance and repair costs, increase equipment avail 
maintenance decisions.   A real time implementa 
ability, and reduce procurement costs.
tion of an ANN model forms the basis of the learn 
Resurgence Software, headquartered in the Uni 
ing system.  A real time engine simulation code was
versity  of  New  Orleans   Research  &  Technology
used to efficiently develop the learning system by
Park,  delivers  innovative  software  solutions  and
generating  real time  data  streams  in  a  series  of
services that help improve the bottom line for ship
learning  experiments.    While  previous  research
owners and operators. Their product, Wave  Sys 
shows  that  the  ANN  is  suitable  for  the  learning
tem, is an equipment reliability analysis system that
engine, real time issues require careful design and
enables ship owners and operators to optimize the
development considerations.
reliability and financial performance of the fleets
By using the engine simulator, SELENDIA, which
by maximizing vessel uptime, minimizing mainte 
is jointly developed by UNO and Ecole Centrale de
nance  costs,  and  reducing  the  risk  of  equipment
Nantes,  they  were  able  to  compare  these  results
failure.   Resurgence has marketing alliances with
with  actual  measured  engine  characteristics.
other  companies  to  market  the Wave   Software
SELENDIA has a Microsoft Windows graphical in 
System. Most important is the alliance with the clas 
terface, allows real time simulation of engine vari 
sification society, Lloyd s Register.  The agreement,
15
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