Fault detection and isolation for a nonlinear railway vehicle suspension with a Hybrid Extended Kalman filter

• Published in: Vehicle system dynamics Vol. 51; no. 10; pp. 1489 - 1501
• Main Authors: Jesussek, Mathias, Ellermann, Katrin
• Format: Journal Article
• Language: English
• Published: Colchester Taylor & Francis 01.10.2013
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Dampers; Exact sciences and technology; Extended Kalman filter; Fault detection; fault detection and isolation; Ground, air and sea transportation, marine construction; Hybrid Extended Kalman filter; Industrial metrology. Testing; Machine components; maintenance; Mechanical engineering. Machine design; Modelling and identification; nonlinear suspension system; Nonlinearity; Railroads; Railway engineering; Railway transportation and traffic; railway vehicle dynamics; Railways; Springs and dampers; Suspension systems (vehicles); Dependability; Rupture; Hybrid Extended Kalman filter; Non linear control; Rail vehicle; Maintenance; System reliability; Non linear system; Vehicle dynamics; Modeling; Extended Kalman filter; nonlinear suspension system; Vehicle suspension; railway vehicle dynamics; Observer; Rail transportation; System identification; Non linear effect; Damper; Fault diagnostic; Yawing; fault detection and isolation; Defect detection
• ISSN: 0042-3114; 1744-5159
• DOI: 10.1080/00423114.2013.810764

Fault detection is considered to be one way to improve system reliability and dependability for railway vehicles. The secondary lateral and anti-yaw dampers are the most critical parts in railway suspension systems. So far, the dampers have been modelled as linear components in the fault detection and isolation observer design. In this work, a Hybrid Extended Kalman filter is used to capture the nonlinear characteristics of the dampers. In order to detect and isolate faults, a nonlinear residual generator is developed, which can distinguish clearly between different types of faults. A lateral half train model serves as an example for the proposed technique. The results show that failures in the nonlinear suspension system can be detected and isolated accurately.