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Abstract



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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