This work presents and discusses a general approach for the dynamic modeling and analysis of a passive biped walking robot, with a particular focus on the feet-ground contact interaction. The main purpose of this investigation is to address the supporting foot slippage and viscoelastic dissipative contact forces of the biped robot-walking model and to develop its dynamics equations for simple and double support phases. For this investigation, special attention has been given to the detection of the contact/impact between the legs of the biped and the ground. The results have been obtained with multibody system dynamics applying forward dynamics. This study aims at examining and comparing several force models dealing with different approaches in the context of multibody system dynamics. The normal contact forces developed during the dynamic walking of the robot are evaluated using several models: Hertz, Kelvin-Voight, Hunt and Crossley, Lankarani and Nikravesh, and Flores. Thanks to this comparison, it was shown that the normal force that works best for this model is the dissipative Nonlinear Flores Contact Force Model (hysteresis damping parameter - energy dissipation). Likewise, the friction contact/impact problem is solved using the Bengisu equations. The numerical results reveal that the stable periodic solutions are robust. Integrators and resolution methods are also purchased, in order to obtain the most efficient ones for this model.

In the maintenance of motor driven systems, detection of cracks in shafts play a critical role. Condition monitoring and fault diagnostics detect and distinguish different kinds of machinery faults, and provide a significant improvement in maintenance efficiency. In this study, we apply the discrete wavelet transform theory and multiresolution analysis (MRA) to vibration signals to find characteristic patterns of shafts with a transversal crack. The feature vectors generated are used as input to an intelligent classification system based on artificial neural networks (ANNs). Wavelet theory provides signal timescale information, and enables the extraction of significant features from vibration signals that can be used for damage detection. The feature vectors generated for every fault condition feed a radial basis function neural network (ANN-RBF) and apply supervised learning designed and adapted for different fault crack conditions. Together, MRA and RBF constitute an automatic monitoring system with a fast diagnosis online capability. The proposed method is applied to simulated numerical signals to prove its soundness. The numerical data are acquired from a modified Jeffcott Rotor model with four transverse breathing crack sizes. The results demonstrate that this novel diagnostic method that combines wavelets and an artificial neural network is an efficient tool for the automatic detection of cracks in rotors.

In the present work, different algorithms for contact detection in multibody systems based on smooth contact modelling approaches are presented. Beginning with the simplest ones, some difficult interactions are subsequently introduced. In addition, a brief overview on the different kinds of contact/impact modelling is provided and an underlining of the advantages and the drawbacks of each of them is determined. Finally, some practical examples of each interaction are presented and analyzed and an outline of the issues arisen during the design process and how they have been solved in order to obtain stable and accurate results is given. The main goal of this paper is to provide a resource for the early-stage researchers in the field that serves as an introduction to the modelling of simple contact/impact events in the context of multibody system dynamics.