1999 AAAI Spring Symposium on AI in Equipment Service & Support
Equipment Service M&D
Background • In actionDescription
Organizing Committee
Symposium
Dates, times, and places
Why a symposium now?
Who should attend?
Format
List of Accepted Papers
Paper Submission
About AAAI
Equipment Service M&D
Artificial
Intelligence
Challenge problems
Software
and Data
Existing software
Useful datasets
References
FAQ
Web sites
Bibliography
Contact
Comments
Subscriptions
Background
Monitoring and Diagnosis have a long history in artificial intelligence. On the forefront was medical diagnosis with the historical MYCIN which pioneered the field of expert systems. Since then, almost any part of AI research has been applied to monitoring tasks. This includes Bayesian networks, neural nets, fuzzy logic, genetic algorithms, etc.Monitoring and diagnosis tasks can be categorized in segmentation, classification,
prediction, and decision making.
In action
Below, we will maintain an open list of examples of equipment service applications, to expose potential participants to the breadth and depth of equipment service tasks and approaches. Contact the symposium organizers if you wish to suggest one or more such applications. Join our announcement list if you wish to be notified when examples are posted.IDS
The Integrated Diagnostic Systems (IDS) uses advanced information technologies
and artificial intelligence techniques. It enhances time critical troubleshooting
and prognostic capabilities in the context of the maintenance of complex
machinery and equipment. The inputs and building blocks will be real-time
and historical data, encoded expertise and data interpretation algorithms.
The system provides a framework for the addition of new diagnostic techniques,
knowledge, and technologies. In addition, it interacts with other client
decision support software systems and functions.
As currently envisioned, IDS supports four main functions:
Provide Accurate Diagnosis
Advise Optimal Repair Strategy
Assess Equipment Health/Predict Incipient
Failures
Establish Maintenance/Overhaul Workscope"
DX-Testbed
The DX-Testbed is a prototype research demo of an Internet/Java/CORBA
client-server remote diagnosis system and architecture.
ECANSE
E C A N S E, Environment for Computer Aided Neural Software Engineering
realizes a concept of soft-computing on a high-abstraction level, where
the software systems are designed visually by integrating a wide selection
of predefined functional components or modules on a graphical interface.
The modules in ECANSE range from data interfaces, signal generators,
mathematical and statistical functions, script language, and graphical
displays to new soft-computing technologies including Neural Networks,
Fuzzy Logic, Genetic Algorithms, Chaostheoretical Methods and Hybrid Combinations
thereof. ECANSE provides a framework for evolutionary and rapid prototyping.
By monitoring the system performance through various tools (statistical
and visual) during simulation, the user may interact at any time and the
results will be seen immediately.
Engine Health Monitoring
Engine Health Monitoring (EHM) program is a tool which integrates vibration,
performance, and component life monitoring for detecting and classifying
developing engine faults necessary to reduce engine operating costs while
optimizing the life of critical engine components. The modular EHM system
has been developed for the USAF and is capable of sensor validation, vibration
and performance diagnostics, "virtual" sensing, and real-time life consumption.
Engine data currently sensed and recorded for post flight processing is
analyzed in a continuous, real-time mode. The measured data is validated/trended
and then passed through redundant anomaly detection routines for analyzing
both performance and vibration related faults. These routines are based
on extensive knowledge of how a healthy engine operates over the entire
flight envelope, and any deviation from these "normal" patterns of engine
operation will be detected and further analyzed. Once an engine anomaly
is detected, a complete fault diagnostic analysis is performed in a real-time
mode utilizing advanced fault pattern recognition schemes and fuzzy-logic
decision analysis. The EHM system has been developed for the Rolls Royce
F405 engine (Adour) which is used on the Navy's T-45 trainer aircraft.
The system can be economically customized for other engine applications.
Jena &Jade
Jena enables operators to maintain operational flight data on all of
their aircraft and engines, regardless of their type or model, in one program
interface. Data may be entered as knee board trend data or digitally recorded
monitor data as well as exceedance events. Jena also has the ability to
analyze this data in real time using various data management and analysis
tools. These tools include ECCA™ analysis and exceptional reporting, ECCM™
and Trending. Basic flight information as well as squawks and maintenance
actions, are recorded for future retrieval. Jena also provides the operator
the ability to set alarms and specify reporting levels, as well as define
who may receive reports on an engine.
Jade is the Jena Application Development Environment. Jade allows a
repair facility, an engine manufacturer, or a commercial fleet operator
to build new engine application data sructures. They can define exceedance
events to be used with an engine monitor as well as specify report levels
for their operational personnel. It is within Jade that classes are defined
and developed, and data structures are evaluated. Sensa Technologies works
closely with Jade users to help them understand ECCA™ concepts. Jade is
also used to record engine test cell runs, engine ground runds and engine
rebuild configurations. Each test contains a detailed header to record
test run specifics. Jade has an assortment of analysis tools. Among those
tools are ECCA™ and PPA™ analysis.