Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems

Abstract

In this paper, a means of generating residuals based on a fault isolation observer (FIO) and evaluating them using a support vector machine (SVM) is proposed. The proposed FIO generates the isolated residual signals and they shows robust performance regardless of unknown road surface conditions. This FIO is designed using a linear time-invariant quarter-car model. While quarter-car models have the form of a bilinear system, in this study the authors convert this bilinear model to a linear model with model uncertainty based on the assumption that the control input is limited. Therefore, the proposed FIO can be used regardless of the type of damper or controller. Furthermore, the SVM based residual evaluator without empirically set thresholds is used to evaluate the generated residuals. The proposed fault diagnosis algorithm is expected to reduce the effort required in the design procedure and it can also detect a small amount of sensor fault that cannot be detected by traditional limit-checking method. The proposed fault diagnosis algorithm is verified using low cost production accelerometers and a quarter-car test rig. Consequently, the fault diagnosis algorithm proposed in this paper can detect the faults of a sprung mass accelerometer and an unsprung mass accelerometer independently, and this algorithm can reduce the effort required in designing the diagnosis algorithm greatly.