Evaluating the predicted reliability of mechatronic systems: state of the art

Reliability analysis of mechatronic systems is a recent field and a dynamic branch of research. It is addressed whenever there is a need for reliable, available, and safe systems. The studies of reliability must be conducted earlier during the design phase, in order to reduce costs and the number of prototypes required in the validation of the system. The process of reliability is then deployed throughout the full cycle of development. This process is broken down into three major phases: the predictive reliability, the experimental reliability and operational reliability. The main objective of this article is a kind of portrayal of the various studies enabling a noteworthy mastery of the predictive reliability. The weak points are highlighted. Presenting an overview of all the quantitative and qualitative approaches concerned with modelling and evaluating the prediction of reliability is so important for future reliability studies and for academic research to come up with new methods and tools. The mechatronic system is a hybrid system, it is dynamic, reconfigurable, and interactive. The modeling carried out of reliability prediction must take into account these criteria. Several methodologies have been developed in this track of research. In this regard, the aforementioned methodologies will be analytically sketched in this paper.Comment: 13 page, Mechanical Engineering: An International Journal (MEIJ), Vol. 3, No. 2, May 201

Prise en compte des interactions multi-domaines lors de l’évaluation de la fiabilité prévisionnelle des systèmes mécatroniques

The mechatronic systems are hybrid, dynamic, interactive and reconfigurable. Therefore their dysfunctional modeling is very difficult. Multi-physical interactions between components have impacts on the degradation or on system failures, leading thus to more uncertainty in reliability evaluation. The work presented in this paper aims to improve the integration of multi-domain interactions in the reliability assessment of mechatronic systems. After a presentation of the state of the art of mechatronic systems reliability estimation methods, we propose to represent multi domain interactions by influential factors in the dysfunctional model. We generally use proportional hazard models; in the case of an interaction represented by a temperature stress, Arrhenius model is used

Dynamic Bayesian Network for Reliability of Mechatronic System with Taking Account the Multi-Domain Interaction

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

Evaluation of the mechatronic systems reliability under parametric uncertainties

The main research intent of this paper is to evaluate the predicted reliability of mechatronic system, with take into account the epistemic uncertainties, The work reported here presents a new methodology based on integrating the petri network with the belief functions, in order to create a belief network, and to show how to propagate the parametric uncertainties in reliability models, Some notions of uncertainty related to the reliability systems are presented, subsequently a brief definition of the belief function and its application in reliability studies are detailed and how we integrate it in petri network. To take into account the interactive aspect of mechatronic systems, we introduce the uncertainties associated to this interaction, by implementing the new method proposed by using belief network. Secondly, we study the propagation of these interaction uncertainties in system reliability. Finally, in regard to applicate the methodology, an industrial example "intelligent actuator" is developed

Evidential Networks for Evaluating Predictive Reliability of Mechatronics Systems under Epistemic Uncertainties

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

L’évaluation de la fiabilité prévisionnelle des systèmes mécatroniques avec la prise en compte des interactions multi-domaines

The mechatronic systems are hybrid, dynamic, interactive and reconfigurable. Therefore their dysfunctional modeling is very difficult. Multi-physical interactions between components have impacts on the degradation or on system failures, leading thus to more uncertainty in reliability evaluation. The work presented in this paper aims to improve the integration of multi-domain interactions in the reliability assessment of mechatronic systems.  After a presentation of the state of the art of mechatronic systems reliability estimation methods, we propose to represent multidomain interactions by influential factors in the dysfunctional model. We generally use proportional hazard models ; in the case of an interaction represented by a temperature stress, Arrhenius model is used

Dynamic analysis of large structures with uncertain parameters based on coupling component mode synthesis and perturbation method

This paper presents a methodological approach to compute the stochastic eigenmodes of large FE models with parameter uncertainties based on coupling of second order perturbation method and component mode synthesis methods. Various component mode synthesis methods are used to optimally reduce the size of the model. The statistical first two moments of dynamic response of the reduced system are obtained by the second order perturbation method. Numerical results illustrating the accuracy and efficiency of the proposed coupled methodological procedures for large FE models with uncertain parameters are presented

Modeling and simulation of an underactuated system

One of the most active research areas in mechatronic systems is the control of mechanical systems controlled by electronic systems using computer programs. These programs execute algorithms called control laws. Our study focuses on the control of underactuated mechanical systems: Case of a reversed two-wheeled pendulum. It consists of developing a control law to stabilize this system. This class of system is rich in practical as well as theoretical applications (SEGWAY, Acrobot robots ...) and this is why the control synthesis for underactuated mechanical systems constitutes a very active research axis and still constitues an open domain for technological research

A numerical algorithm for computing the inverse of a Toeplitz pentadiagonal matrix

In the current paper, we present a computationally efficient algorithm for obtaining the inverse of a pentadiogonal toeplitz matrix. Few conditions are required, and the algorithm is suited for implementation using computer algebra systems