Learning In Engineering Diagnosis
April 9, 1998
CENTER FOR ENGINEERING EDUCATION AND
SCHOOL OF ENGINEERING, UNIVERSITY
||Yi Lu Murphey
Department of Electrical and Computer
||Brennan Hamilton1, Lee
1Advanced Vehicle Technology
- Core and Product Engineering, Ford Motor Company, Dearborn, MI 48121.
2Ford Research Laboratory,
Ford Motor Company, Dearborn, MI 48121.
||Fault diagnosis has been a classic
engineering problem. Particularly, automotive fault diagnosis has the following
Knowledge on most vehicle diagnostic problems
is incomplete and vague due to the complexity of the modern vehicles.
Different vehicle models have different
engineering features, e.g. Ford ThunderBird is very different from Ford
Town Car. Furthermore, ThunderBird 96 may be different from ThunderBird
Often the data available for learning
is not complete, it is important for the fuzzy system to combine human
expert knowledge with the knowledge learned from data samples.
When the vehicle model changes, for example,
vehicles of a new model are manufactured, we have very few data available
to build the fuzzy knowledge base. It is important for the system to have
the capability of accumulating learning.
Data samples of good vehicles and vehicles
having problems are often unbalanced. The data acquired at an assembly
plant generally contains 99% good vehicles and 1% vehicles that have the
problem to be diagnosed.
To solve the problems outlined above utilizing
fuzzy system techniques.
To develop an intelligent fuzzy diagnostic
system for the End-of-Line vehicle test in assembly plants.
To create an intelligent fuzzy system
that can automatically generate the required knowledge base including the
||We applied fuzzy logic and machine
learning techniques to solve this problem.
We have developed a fuzzy intelligent
system for EEC vacuum leak diagnosis.
The system has 2 major components:
learning engineering diagnostic knowledge
engineering fault diagnosis
automatic fuzzy rule generation
automatic fuzzy membership function optimization
Learning diagnosis knowledge
Fuzzy Rule Generation
Fuzzy rules generated from engineering
engineers generate fuzzy rules through
an interactive linguistic interface
engineers modify the fuzzy rules generated
by the system
Automatic fuzzy rule generation from training
Membership function optimization
MSF’s are represented by triangular functions
MSF optimization is to find the best locations
and overlapping of the triangular functions
Optimization methods investigated:
gradient descent method
stochastic annealing method
Fuzzy engineering diagnosis
||An algorithm has been developed for
automatic fuzzy rule generation.
An intelligent fuzzy system has been developed
and implemented for diagnosing EEC vacuum leak. The system is capable of
learning the characteristics of different vehicle models and performing
robust test. The system is in the process of being implemented into Ford
Motor Company's diagnostic systems.
The fuzzy diagnostic system was tested
on data sets of over 20,000 samples, the accuracy of the system was above
96%. We tested the system on two different types of vehicle models:
Ford Thunderbird 95 and 96 models
Ford Lincoln Towncar 96, 97 and 98 models
As a result of this project, we received
a grant from the National Science Foundation to support a project titled
"A Distributed Fuzzy System Model for Automotive Diagnosis." The
duration of the project is two years and the award is in the amount of
$93,450.00. We have also received funding from the Ford Motor Company in
the amount of $40,000.00 for a period of two years.
||We have developed and implemented
a fuzzy model for automotive fault diagnosis. The fuzzy model has two modes,
learning and detection. Within the learning mode, a new rule generation
method and a membership function optimization method are used. The model
has been implemented into a fuzzy diagnostic system that detects the vacuum
leak in Electronic Engine Controller in automobiles. The fuzzy vacuum leak
diagnostic system has been tested on two different vehicle models, Thunderbird
and Lincoln Towncar. The testing results show that the system is effective,
fast and compact, suitable for running on a PC platform.
A course based on this project, "Fuzzy
Diagnosis in Automotive Engineering," was offered to Ford engineers in
May 1996. There were 20 participants in this course.
The result is also used in senior students
project design, graduate courses including ECE 579 Intelligent Systems
and ECE 580 Neural Networks.
Industrial impact: The result of this
project is currently being transferred to a Ford diagnostic program to
be used in Ford assembly plants.
This page is prepared by Jie
Chen, May 11, 1998.