Fault Isolation By Manifold Learning
This thesis investigates the possibility of improving black box fault
diagnosis by a process called manifold learning, which simply stated
is a way of finding patterns in recorded sensor data. The idea is that
there is more information in the data than is exploited when using
simple classification algorithms such as k-Nearest Neighbor and
Support Vector Machines, and that this additional information can be
found by using manifold learning methods.
To test the idea of using manifold learning, data from two different
fault di- agnosis scenarios is used: A Scania truck engine and an
electrical system called Adapt. Two linear and one non-linear manifold
learning methods are used: Princi- pal Component Analysis and Linear
Discriminant Analysis (linear) and Laplacian Eigenmaps (non-linear).
Some improvements are achieved given certain conditions on the
diagnosis sce- narios. The improvements for different methods
correspond to the systems in which they are achieved in terms of
linearity. The positive results for the rela- tively linear electrical
system are achieved mainly by the linear methods Principal Component
Analysis and Linear Discriminant Analysis and the positive results for
the non-linear Scania system are achieved by the non-linear method
Laplacian Eigenmaps.
The results for scenarios without these special conditions are not
improved however, and it is uncertain wether the improvements in
special condition scenarios are due to gained information or to the
nature of the cases themselves.
Mårten Thurén
2010
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Senast uppdaterad: 2021-11-10
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