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Background on Diagnosis
A fault is an abnormal state of a
machine or a system, including dysfunction or misfunction of a part, an assembly, or the
whole system. The occurrence of a fault is associated with a number of factors,
which in turn is related to a number of symptoms. Fault diagnostics is the study
of the relationship of fault, factors and symptoms, and it is used to predict and
control the performance of a system, be it a telecommunication system, a semiconductor
manufacturing equipment, or even a human body.
Fault Diagnosis Methods
Many methods are available for fault
diagnosis. They include
Time series analysis - use
history data to predict future events
Fuzzy logic methods - fuzzy
diagnosis matrix, clustering, evaluation, segmentation and learning
Neural networks - system
adaptation by self learning
Multisource &
multisensor data fusion - combination of numeric, logic, linguistic information
Case-based reasoning -
heuristic method using experience and history data
Probability reasoning -
Bayesian networks, Fisher's discrimination functions
Hybrid method - combination
of above methods
Data mining techniques -
seperate data and build a mathematical model in the the sub-space of factors. The model is
used to isolate the fault, identify the factors that contribute to the fault, and provide
advisory for fixing the fault.
Steps in Fault Diagnosis
Collect history data (in a
database) diagnosis expertise from experts
Mechanism analysis - study
fault physics
Collect primary cases -
gather samples that prove or disapprove
Establish the relationship
between factors and symptoms
Build the diagnosis
matrix
Determine the inference
rules from known experience and cases
Fix the fault
Update fault database
System self-learning -
update inference rules and the diagnosis matrix
Real-world
examples
Diagnose an
equipment for ST14 steel plate production
Diagnose a
distillation tower to increase gasoline yield
Diagnose a
platinum reforming system for ethylbenzene production
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