Acquisition of new technology information for maintenance and replacement policies

In this paper, we propose the first model that considers the option to acquire information on the profitability of a new technology that is not yet available on the market for asset maintenance and replacement decisions. We consider the uncertainty of future asset characteristics by incorporating information acquisition decisions into a non-stationary Markov decision process framework. Using this framework, we optimise asset maintenance and replacement decisions as well as the optimal timing of new technology adoption. Through mathematical analyses, the monotone properties and convexity of the value function and optimal policy are deduced. Deeper numerical analyses highlight the importance of considering the acquisition of information on future technology when formulating a maintenance and replacement policy for the asset. We also deduce a non-intuitive result: an increase in the arrival probability of new technology does not necessarily make the acquisition of additional information more attractive

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

A risk-oriented degradation model for maintenance of reinforced concrete structure subjected to cracking

This article is within the context of decision models aimed for maintenance of structures and infrastructures in civil engineering. The contribution relies on the construction of a degradation model oriented toward risk analysis. The proposed model can be defined as a meta-model in the sense that it is based on observations while incorporating key features from the degradation process necessary for the maintenance decision. We propose to stimulate the construction of the degradation model based on the crack propagation of a submerged reinforced concrete structure subject to chloride-induced corrosion. Furthermore, a set of numerical illustrations is performed to demonstrate the advantages and applicability of the proposed approach in risk management and maintenance contexts

A Condition-Based Deterioration Model for the Stochastic Dependency of Corrosion Rate and Crack Propagation in Corroded Concrete Structures

Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities

An adaptive condition-based maintenance planning approach: An offshore wind turbine case study

International audienc

CT imaging techniques for two-phase and three-phase in-situ saturation measurements

The aim of this research is to use the SUPRI 3D steam injection laboratory model to establish a reliable method for 3-phase in-situ saturation measurements, and thereafter investigate the mechanism of steamflood at residual oil saturation. Demiral et al. designed and constructed a three dimensional laboratory model that can be used to measure temperature, pressure and heat loss data. The model is also designed so that its construction materials are not a limiting factor for CT scanning. We have used this model for our study. In this study, we saturated the model with mineral oil, and carried out waterflood until residual oil saturation. Steamflood was then carried out. A leak appeared at the bottom of the model. Despite this problem, the saturation results, obtained by using 2-phase and 3-phase saturation equations and obtained from the Cat scanner, were compared with the saturations obtained from material balance. The errors thus obtained were compared with those obtained by an error analysis carried out on the saturation equations. This report gives details of the experimental procedures, the data acquisition and data processing computer programs, and the analysis of a steamflood experiment carried out at residual oil saturation

A degradation test plan for a non-homogeneous gamma process

International audienc

Risks management and cobots. Identifying critical variables

Trabajo presentado en: 29th European Safety and Reliability Conference (ESREL), 22–26 September 2019, HannoverA collaborative robot or a "Cobot" is the name of a robot that can share a workspace with operators in the absence of a protective fence or with only partial protection. They represent a new and expanding sector of industrial robotics. This investigation draws from the latest international rules and safety parameters related to work with collaborative robots. Its detailed research is motivated by the design of a collaborative industrial robot system, hazard elimination, risk reduction, and different collaborative operations, such as power and force limiting, collaborative operation design, and end-effector safety requirements, among others. The purpose of our study is to analyze the most important variables that must be controlled in accordance with the desired use of the Cobot, according to ISO / TS 15066, ISO / TR 20218-1and some other generic safety regulations on machines and industrial robots. A series of observations and appreciations on the use of the Cobot will also be presented

A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke\u27s method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information