We propose a novel method to identify the transfer functions of single-input-single-output linear time invariant (SISO-LTI) dynamic systems. Our approach makes use of the operator based generalization of Prony's method. In particular, the operator based Prony algorithm is used to reconstruct the transfer function of the system as a linear combination of rational basis functions. A considerable benefit of the proposed method is its robustness against the estimated system order. That is, if system order is over estimated, the correct system order can be found naturally. Another important benefit is that the proposed method is shown to be asymptotically robust towards zero expectation noise with the correct choice of certain evaluation functionals. The effectiveness of the proposed method is demonstrated through numerical experiments.

In this work, we consider the problem of detecting the phenomenon of brake disc runout, which is usually caused by deformations of the brake disc in passenger vehicles. We introduce a novel measurement system that collects and processes information on the vibration profile of the vehicle, as well as the pressure of the brake fluid. Our novel processing method for the collected signals relies on model-driven adaptive wavelet neural networks. We demonstrate that the proposed signal processing methodology outperforms previous approaches to detect aberrations of the brake disc. In addition, the proposed neural network models are defined by interpretable parameters and have reduced complexity when compared to traditional and similar approaches. These characteristics make the proposed construction suitable for automotive applications.