RT Journal Article
T1 Kernel-based support vector machines for automated health status assessment in monitoring sensor data
A1 Diez-Olivan, Alberto
A1 Pagan, Jose A.
A1 Khoa, Nguyen Lu Dang
A1 Sanz, Ricardo
A1 Sierra, Basilio
AB This paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automatically modelling the normal behaviour from condition monitoring data is probably one of the most challenging problems, specially when there is limited information of real faults. To overcome this difficulty, a data-driven learning framework based on nonparametric density estimation for outlier detection and ν-SVM for normality modelling, with optimal bandwidth selection, is proposed. A health score based on the log-normalisation of the distance to the separating hyperplane is also provided. Experimental results obtained when analysing the propagation of a critical fault over time in a marine diesel engine demonstrate the validity of the algorithm. The predictions of normality models learned were compared to those of the k-nearest neighbours (kNN) method. Low false positive rates on healthy data and improved prediction capacities are achieved.
SN 0268-3768
YR 2018
FD 2018-03-01
LK https://hdl.handle.net/11556/3605
UL https://hdl.handle.net/11556/3605
LA eng
NO Diez-Olivan , A , Pagan , J A , Khoa , N L D , Sanz , R & Sierra , B 2018 , ' Kernel-based support vector machines for automated health status assessment in monitoring sensor data ' , International Journal of Advanced Manufacturing Technology , vol. 95 , no. 1-4 , pp. 327-340 . https://doi.org/10.1007/s00170-017-1204-2
NO Publisher Copyright: © 2017, Springer-Verlag London Ltd.
DS TECNALIA Publications
RD 31 jul 2024