Application of Residual Generators to Detect Material Failure of a GFRP Transversal Leaf Spring

Matthias Reiner, Tilman Bünte, Andreas Heckmann, and Andreas Varga


Fault detection, Residual generators, Optimization, GFRP


This paper discusses the application of residual generators to detect material failure of a vehicle transversal leaf spring made of glass-fiber reinforced plastic (GFRP). The residual generators are generated by symbolic nullspace computation and optimization based on linear parameter varying (LPV) models of the dynamics of the transversal leaf spring inside the vehicle. The fault detection method is verified using a complex, highly nonlinear vehicle model in combination with a modal transversal leaf spring model based on standard input data (SID), which is generated directly from finite element models (FEM).

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