BSTRACT: In real industrial processes
continuous production is required to
achieve productivity and profitability
requirements. As a result, stopping a
production line suddenly in the middle
of a process, to fix or replace a faulty
sensor, may produce significant economic
losses. Therefore, the current fault
management strategy challenge is not
only to detect and isolate faults, but
also to accommodate them, to keep the
safe operation in the plant while
maintenance can be scheduled without
significantly disturbing the process.
This research extends the generalized
parity vector (GPV) approach originally
proposed by Viswanadham, Taylor and Luce
and continued by Omana and Taylor, to
offer a complete sensor fault detection,
isolation and accommodation (FDIA)
technique viable for implementation in
real industrial applications. Fault
detection and isolation is also provided
for actuators. A new systematic approach
to implement a recursive on-line
transformation matrix computation block
using optimization is developed. The
calculation of this transformation
matrix represents an important
contribution to the FDI field using
directional residuals because it
eliminates the restriction on the number
of faults that previous researches were
able to isolate and significantly
increases FDI robustness. The special
case for sensor-actuator faults and the
hyperplane intersection problem are
identified and solved by extending the
objective function during the
optimization process to compute the
transformation matrix. This modification
significantly improves the isolation
results by reducing the ambiguous cases
produced by these inevitable special
geometrical situations given by the
system dynamics. This is a major
contribution, because it identifies and
overcomes these critical limitations of
FDI using directional residuals that
previous researchers were not aware of.
The plant model availability issue is
overcome by incorporating an on-line
system identification module before
executing the FDIA block. This shows
that while the GPV approach is a
model-based FDI technique, it is still
viable for those plants where an '
a-priori' mathematical model is not
available. A fault management strategy
is implemented using a novel fault-size
estimation, classification and
accommodation method based on the static
GPV magnitude signature. The proposed
fault accommodation technique not only
preserves closed-loop stability, but
also compensates the actual variable
affected by the faulty sensor. A
initialization section is introduced to
make this FDIA technique capable of
handling model and operating point
changes. The FDI robustness is
significantly improved by incorporating
an on-line threshold computation block
and combining the strengths of the
static and dynamic GPV implementations
during the decision-making process. In
this work the FDIA technique is
successfully analyzed and simulated on a
gravity three-phase separation process
used in oil production facilities. This
model closely simulates a large scale
process, which allows the GPV technique
to be validated in a higher dimensional
space with more complex system dynamics.
BSTRACT: In real industrial processes
continuous production is required to
achieve productivity and profitability
requirements. As a result, stopping a
production line suddenly in the middle
of a process, to fix or replace a faulty
sensor, may produce significant economic
losses. Therefore, the current fault
management strategy challenge is not
only to detect and isolate faults, but
also to accommodate them, to keep the
safe operation in the plant while
maintenance can be scheduled without
significantly disturbing the process.
This research extends the generalized
parity vector (GPV) approach originally
proposed by Viswanadham, Taylor and Luce
and continued by Omana and Taylor, to
offer a complete sensor fault detection,
isolation and accommodation (FDIA)
technique viable for implementation in
real industrial applications. Fault
detection and isolation is also provided
for actuators. A new systematic approach
to implement a recursive on-line
transformation matrix computation block
using optimization is developed. The
calculation of this transformation
matrix represents an important
contribution to the FDI field using
directional residuals because it
eliminates the restriction on the number
of faults that previous researches were
able to isolate and significantly
increases FDI robustness. The special
case for sensor-actuator faults and the
hyperplane intersection problem are
identified and solved by extending the
objective function during the
optimization process to compute the
transformation matrix. This modification
significantly improves the isolation
results by reducing the ambiguous cases
produced by these inevitable special
geometrical situations given by the
system dynamics. This is a major
contribution, because it identifies and
overcomes these critical limitations of
FDI using directional residuals that
previous researchers were not aware of.
The plant model availability issue is
overcome by incorporating an on-line
system identification module before
executing the FDIA block. This shows
that while the GPV approach is a
model-based FDI technique, it is still
viable for those plants where an '
a-priori' mathematical model is not
available. A fault management strategy
is implemented using a novel fault-size
estimation, classification and
accommodation method based on the static
GPV magnitude signature. The proposed
fault accommodation technique not only
preserves closed-loop stability, but
also compensates the actual variable
affected by the faulty sensor. A
initialization section is introduced to
make this FDIA technique capable of
handling model and operating point
changes. The FDI robustness is
significantly improved by incorporating
an on-line threshold computation block
and combining the strengths of the
static and dynamic GPV implementations
during the decision-making process. In
this work the FDIA technique is
successfully analyzed and simulated on a
gravity three-phase separation process
used in oil production facilities. This
model closely simulates a large scale
process, which allows the GPV technique
to be validated in a higher dimensional
space with more complex system dynamics.
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