Development of optimal accelerated test plan

This paper describes an optimal accelerated test plan considering an economic approach. We introduce a general framework to obtain plans of optimal accelerate tests with a specific objective, such as cost. The optimal test plans are defined by considering prior knowledge of reliability, including the reliability function and its scale and shape parameters, and the appropriate model to characterize the accelerated life. This information is used in Bayesian inference to optimize the test plan. The prior knowledge contains the uncertainty on real reliability of new product. So, the proposed methodology consists of defining an optimal accelerated testing plan while considering an objective function based on economic value, using Bayesian inference for optimizing the test plan, and using the uncertainty of the parameters to obtain a robust, optimal testing plan. The objective function consists of two terms: the cost linked to testing activities and the cost associated with operation of the product. Finally, we will develop our optimal plan by extending our approach to include theoretical formulation of the various degrees of freedom with respect to the parameters. To complete this development, we need to improve the algorithm of optimization. To obtain the best test plan, we propose an optimization procedure using the genetic algorithm. The proposed method will be illustrated by a numerical example based on a well-known problem

What are the effects of the reliability model uncertainties in the maintenance decisions?

Most of the works proposed for the design of reliability test plans  are  devoted  to  the  guaranty  of  the  reliability performance  of  a  product  but  scarce  of  them  tackles maintenance  issues.  On  the  other  hand,  classical maintenance  optimization  criteria  rarely  take  into  account the variability of the failure parameters due to lack of data, especially when the data collection in the operating phase is expensive.  The  objective  of  this  paper  is  to  highlight through a numerical experiment the impact of the test plan design  defined  here  by  the  number  of  the  products  to  be tested and the test duration on the performance of a classical condition-based maintenance (CBM) policy

Optimal accelerated test plan: optimization procedure using Genetic Algorithm

This paper describes an optimization procedure using Genetic Algorithm to define an optimal accelerated test plan considering an economic approach. We introduce a general framework to obtain plans of optimal accelerate tests with a specific objective, such as cost. The objective is to minimize the costs involved in testing without reducing the quality of the data obtained. The optimal test plans are defined by considering prior knowledge of reliability, including the reliability function and its scale and shape parameters, and the appropriate model to characterize the accelerated life. This information is used in Bayesian inference to optimize the test plan. To perform optimization, a specific genetic algorithm is decribed and applied to obtain the best test plan. This procedure is then illustrated on a numerical example

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

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

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

Planification des essais accélérés : optimisation, robustesse et analyse

Qualification of a product during the development phases is an important step in a project. It permits to verify that the performances and reliability achieve the objectives. The qualification tests are often costly in time and number of products tested. Accelerated tests consist of submitting units to stress levels higher than operating condition to reduce the time to failures and allow building faster the reliability function. This requires the determination or development of appropriate life-stress relationship model. In addition, a test plan should be built, accurate the design parameters (number of test levels, stress levels, sample allocation at each stress level ...) to find the best compromise between cost test and quality estimation. The objective of the thesis is to define a methodology to obtain optimal and robust accelerated test plans. So, we have developed a general framework based on the minimization of a global cost function and a Bayesian approach. The prior distributions, from the available knowledge on the parameters of reliability and acceleration model, are used in Bayesian inference and in a Monte Carlo simulation to explore possible reliabilities. The optimal and robust plan is obtained by optimization methods (Response surface and Genetic algorithms). Finally, a methodology of monitoring during test realization is developed. The relevance of the results in relation to prior information is studied using a similarity factor between observed and a priori data. It allows verifying whether the decision on the qualification can be taken more quickly in presence of more reliable product than expected or optimizing the plan under realization

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

Accelerated Life Testing : Analysis and Optimization

This paper describes a methodology to conduct a sequentialtest defined by an optimal accelerated testing plan. This test plan is based on an economic approach defined in previous work, a prior knowledge on reliability parameters (choice of reliability function, scale and shape parameters ...) and acceleration model (choice of model, model parameters ...) to evaluate the proportions of failure at each accelerated level. When conducting a test, it is possible to verify the compatibility of results with prior knowledge from a consistency criterion that measures the compatibility between prior distribution and likelihood provided by the data. We can also reduce the censoring time in case of "Good results" while keeping the same level of risk. This possibility is authorized because the robust test is longer than basic optimal test plan. In the process of product development, we will use the qualification. Qualification is an application-specific process involving the evaluation of the product with respect to its quality and reliability. The purpose of qualification is to define the acceptable range of variability for all critical product parameters affected by design and manufacturing. The methodology will be illustrated by a numerical example