This article presents a methodology for reliability prediction during the design phase of mechatronic system considered as an interactive dynamic system. The difficulty in modeling reliability of a mechatronic system is mainly due to failures related to the interaction between the different domains called Multi-domain interaction. Therefore in this paper, after a presentation of the state of the art of mechatronic systems reliability estimation methods, we propose a original approach by representing multi domain interactions by influential factors in the dysfunctional modeled by Dynamic Bayesian Networks. A case study demonstrates the interest of the proposed approach.

In this paper, we propose a stochastic model to describe over time the evolution of stress in a bolted mechanical structure depending on different thicknesses of a joint elastic piece. First, the studied structure and the experiment numerical simulation are presented. Next, we validate statistically our proposed stochastic model, and we use the maximum likelihood estimation method based on Euler–Maruyama scheme to estimate the parameters of this model. Thereafter, we use the estimated model to compare the stresses, the peak times, and extinction times for different thicknesses of the elastic piece. Some numerical simulations are carried out to illustrate different results.

In this article, the novel approach to equations of motion for serial manipulators developed in literature by Bertrand and Bruneau (2013) is extended to make it usable for manipulators with general joints (i.e., prismatic and/or rotational). In this method, the dynamic model is explicitly and directly obtained from the structural parameters of the manipulator and matrix algebra without intermediate heavy calculations such as energy derivation. The correctness and efficiency of the described method are demonstrated through simulation of the dynamical equations of a 5-DOF SCARA robot. The simulation results obtained using the new formulation were compared with those derived by Kane’s method, Lagrange–Euler formulation, and GIM (generalized inertia matrix)-Christoffel’s algorithm, which proves the efficiency and correctness of the presented model. It was concluded that the new formulation requires less CPU time to generate explicit closed-form inverse dynamics. Finally, to illustrate the power of the new formulation in real-time control, a trajectory tracking control for the SCARA manipulator based on the numeric and symbolic computation of the inverse dynamic is established, and it is shown that the numeric and symbolic approaches based on our method are equivalent. As a consequence, the applicability of the new formulation in real-time model-based control is proved.

In reliability predicting field, the probabilistic approaches are based on data relating to the components which can be precisely known and validated by the return of experience REX, but in the case of complex systems with high-reliability precision such as mechatronic systems, uncertainties are inevitable and must be considered in order to predict with a degree of confidence the evaluated reliability. In this paper, firstly we present a brief review of the non-probabilistic approaches. Thereafter we present our methodology for assessing the reliability of the mechatronic system by taking into account the epistemic uncertainties (uncertainties in the reliability model and uncertainties in the reliability parameters) considered as a dynamic hybrid system and characterized by the existence of multi-domain interaction between its failed components. The key point in this study is to use an Evidential Network “EN” based on belief functions and the dynamic Bayesian network. Finally, an application is developed to illustrate the interest of the proposed methodology.