Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation methods (FDI). These techniques are used in order to achieve two things: First, they identify changes in the system which are caused by a fault (fault detection). Second, they localize where in the system the fault has occurred (fault isolation). The idea is to continuously monitor the operability of the train during normal operation. Thus, FDI provides crucial information for maintenance on demand, which could decrease service cost and time significantly. The mechanical model of the considered railway vehicle is described as a multibody system, which is excited randomly due to the wheel-rail interaction. Various parameters, like masses, spring- and damper-characteristics, influence the nonlinear dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and require immediate maintenance. The aim of this work is to detect faults in the nonlinear suspension system of the vehicle. An optimal and robust filter is used in order to estimate the states. The paper describes several steps in the improvement of a FDI process. Linear and nonlinear residual generators are applied for the detection and isolation of faults in the system. A full train model with multiple nonlinear components serves as an example for the described techniques and numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish between different possible sources for the changes in the dynamical behavior.
|Journal||Journal of Vibration Engineering & Technologies|
|Publication status||Published - Dec 2015|
- Multibody dynamics
- Fault detection and isolation
- Nonlinear suspension system
- Railway vehicle dynamics