Diagnosability analysis and FDI system design for uncertain systems
Our society depends on advanced and complex technical systems and machines,
for example, cars for transportation, industrial robots in production lines, satel-
lites for communication, and power plants for energy production. Consequences
of a fault in such a system can be severe and result in human casualties, envi-
ronmentally harmful emissions, high repair costs, or economical losses caused by
unexpected stops in production lines. Thus, a diagnosis system is important, and
in some applications also required by legislations, to monitor the system health
in order to take appropriate preventive actions when a fault occurs. Important
properties of diagnosis systems are their capability of detecting and identifying
faults, i.e., their fault detectability and isolability performance.
This thesis deals with quantitative analysis of fault detectability and isola-
bility performance when taking model uncertainties and measurement noise
into consideration. The goal is to analyze diagnosability performance given a
mathematical model of the system to be monitored before a diagnosis system is
developed. A measure of fault diagnosability performance, called distinguishabil-
ity, is proposed based on the Kullback-Leibler divergence. For linear descriptor
models with Gaussian noise, distinguishability gives an upper limit for the fault
to noise ratio of any linear residual generator. Distinguishability is used to
analyze fault detectability and isolability performance of a non-linear mean value
engine model of gas flows in a heavy duty diesel engine by linearizing the model
around different operating points.
It is also shown how distinguishability is used for determine sensor placement,
i.e, where sensors should be placed in a system to achieve a required fault
diagnosability performance. The sensor placement problem is formulated as
an optimization problem, where minimum required diagnosability performance
is used as a constraint. Results show that the required diagnosability perfor-
mance greatly affects which sensors to use, which is not captured if not model
uncertainties and measurement noise are taken into consideration.
Another problem considered here is the on-line sequential test selection
problem. Distinguishability is used to quantify the performance of the different
test quantities. The set of test quantities is changed on-line, depending on
the output of the diagnosis system. Instead of using all test quantities the
whole time, changing the set of active test quantities can be used to maintain a
required diagnosability performance while reducing the computational cost of
the diagnosis system. Results show that the number of used test quantities can
be greatly reduced while maintaining a good fault isolability performance.
A quantitative diagnosability analysis has been used during the design of
an engine misfire detection algorithm based on the estimated torque at the
flywheel. Decisions during the development of the misfire detection algorithm
are motivated using quantitative analysis of the misfire detectability performance.
Related to the misfire detection problem, a flywheel angular velocity model for
misfire simulation is presented. An evaluation of the misfire detection algorithm
show results of good detection performance as well as low false alarm rate.
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Last updated: 2020-01-27