Fault detection of rail vehicle suspension system based on CPCA
- 1 October 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2013 Conference on Control and Fault-Tolerant Systems (SysTol)
- No. 21621195,p. 700-705
- https://doi.org/10.1109/systol.2013.6693832
Abstract
The suspension system plays a crucial role of the rail vehicles. The fault detection of the suspension system is an effective way to ensure the security of the safe, stable operation of rail vehicles. This paper concerns the fault detection issue of rail vehicle suspension systems with the consensus principle components analysis (CPCA). The signal information used in the fault detection is obtained from the SIMPACK and MATLAB co-simulation environment. In this paper, two typical primary spring and damper fault with coefficient reduction of 5% and 25% are detected successfully using CPCA. Compared with DPCA, the simulation results show that the CPCA method can detect smaller fault with faster response speed.Keywords
This publication has 8 references indexed in Scilit:
- Data-driven fault detection of vertical rail vehicle suspension systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Fault isolation of rail vehicle suspension systems by using similarity measurePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Fault Detection of Railway Vehicle Suspension Systems Using Multiple-Model ApproachJournal of Mechanical Systems for Transportation and Logistics, 2008
- Control and monitoring for railway vehicle dynamicsVehicle System Dynamics, 2007
- Estimation of railway vehicle suspension parameters for condition monitoringControl Engineering Practice, 2007
- Concepts and techniques for railway condition monitoringPublished by Institution of Engineering and Technology (IET) ,2006
- Multivariate process monitoring and fault diagnosis by multi-scale PCAComputers & Chemical Engineering, 2002
- Analysis of multiblock and hierarchical PCA and PLS modelsJournal of Chemometrics, 1998