Impact of maintenance on replacement investment under technological improvement

International audienceAn unexplored important area in the equipment investment problem under technological improvement is the impact of maintenance policy. In fact, maintenance not only helps to maximize the profitability of the asset, but also prolong its economic life while waiting the apparition of better technology in the near future. Therefore, we propose a model that allows us to consider how replacement investment in a new or improved asset will be influenced by maintenance. The investment decisions are based on information about the profitability of the current asset and the technological environment. For the maintenance process, we also consider the dependency of its cost and efficiency on the deterioration state of asset that is represented by a profit parameter. We use a non-stationary Markov decision process to solve for the optimal investment/maintenance policy and illustrate the potential benefits of integrating maintenance policies in the investment strategy through different numerical analysis

Optimal Maintenance and Replacement Decisions under Technological Change

International audienceThe requirement of equipment improvement in order to satisfy the safety and reliability of system motivates the development of technology. The presence or expectation of technologically better equipment will influence managerial decisions on whether to invest in the maintenance of current equipment, invest in replacement with an equivalent model, replacement with a higher technology model currently available on the market, or wait for a potentially even better technology to appear in the near future. Hence, the consideration of technological change is a very important aspect for maintenance and replacement decisions. This paper aims to define a model that allows us to gain insight into how maintenance/replacement policies will be influenced by the expectation of future technology. We then use stochastic dynamic programming (i.e., Markov decision process) to solve for the optimal maintenance and replacement policy of the equipment as a function of performance and cost. Finally, we illustrate the problem through several numerical example

Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories

International audienceClassical spare parts inventory models assume that the same vintage of technology will be utilized throughout the planning horizon. However, replacement often occurs in the form of a new technology that renders existing spare parts inventories obsolete. This paper aims to study the impact of spare parts inventory on maintenance and replacement decisions under technological change via a Markov decision process formulation. The replacement decision is complex in that one must decide with which technology available on the market to replace the current asset. Under technological change, the do nothing and repair options have significantly more value as they allow the appearance of even better technologies in the future

Information acquisition of new technology performance for maintenance/investment decisions

International audienceThe possibility of new technology occurrence has an important impact on the maintenance/ replacement decision. Therefore a challenge in maintenance decision marking is to determine maintenance policy and replacement investment plan under an uncertainty of technology improvement. In each period, we must decide whether to gather additional information on the potential improvement of a new technology, then chose the appropriate action for the asset (Do Nothing, Maintenance or Replacement). To formulate this scenario, we use a non stationary Markov Decision Process (MDP) model and provide some properties of the optimal policy based on a given set of numerical examples

System-level prognostics based on inoperability input-output model

International audienceNowadays, the modern industry is increasingly demanding the availability and reliability of production systems as well as the reduction of maintenance costs. The techniques to achieving these goals are recognized and discussed under the term of Prognostics and Health Management (PHM). However, the prognostics is often approached from a component point of view. The system-level prognostics (SLP), taking into account interdependencies and multi-interactions between system components, is still an underexplored area. Inspired from the inoperability input-output model (IIM), a new approach for SLP is proposed in this paper. The inoperability corresponds to the component’s degradation, i.e. the reduction of its performance in comparison to an ideal reference state. The interactions between component degradation and the effect of the environment are included when estimating the inoperability of components and also when predicting the system remaining useful life (SRUL). This approach can be applied to complex systems involving multi-heterogeneous components with a reasonable computational effort. Thus, it allows overcoming the lack of scope and scalability of the traditional approaches used in PHM. An illustrative example is presented and discussed in the paper to highlight the performance of the proposed approach

A new dynamic predictive maintenance framework using deep learning for failure prognostics

In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper

Red-emitting Ba2Si5N8Eu2+ conversion phosphor: A new selection for enhancing the optical performance of the in-cup packaging MCW-LEDs

In this research, the influence of the red-emitting Ba2Si5N8Eu2+ convention phosphor on the optical performance of the 7,000K and 7,700K in-cup packaging multi-chip white LEDs (MCW-LEDs) is investigated. The effect of the red-emitting Ba2Si5N8Eu2+ convention phosphor is demonstrated based on Mie Theory by Mat Lab and Light Tools software. The research results indicated that the optical performance of MCW-LEDs was crucially affected by the red-emitting Ba2Si5N8Eu2+ phosphor's concentration. This paper provides an essential recommendation for selecting and developing the phosphor materials for MW-LEDs manufacturing.Web of Science51art. no. 148615

Effects of Fertilizers on Biomass, Sugar Content and Ethanol Production of Sweet Sorghum

Sweet sorghum (Sorghum bicolor) is a promising alternative crop for bioethanol production in developing countries. However, to extend the cultivative area of this crop, it needs to develop an appropriate growing protocol for farmers. This chapter describes the examination of different doses of fertilizers combined with manure and micronutrients, in various applied times, on biomass, sugar content and ethanol production of sweet sorghum. It was observed that the application of 90 N + 90 P2O5 + 60 K2O provided maximum stem yield and optimum contents of sugar and ethanol yield, however nontreatment of any among P, P2O5 and K2O caused significant reduction of biomass and ethanol production. Higher fertilization >90 N may provide greater productivity of this crop but it may cause lodging and economic deficit for farmers in developing countries. It was also found that the applied times of fertilization should be at 3–4 to 7–8 leaf stage. In contrast, when the fertilization was as close to the flowering stage caused remarkable reduction of stem yield and ethanol production. The supplementation of (NH4)2MO7O2.4H2O at 5 kg/ha provided an increase of 10–12 tons/ha of stem yield and a remarkable enrichment of ethanol production. Findings of this study are useful for farmers and agricultural extensionists to promote biomass and ethanol productivity of this crop for bioethanol production. This research also highlights a greater possibility of exploiting sweet sorghum cultivation in infertile and hilly, abandoned areas for ethanol production

The Impact of Education on Child Abuse Prevention

This research investigates the impact of education on child abuse prevention in Vietnam by using Vietnamese government's reports (2012 – 2019) on child abuse. In order to analyze the impact of education on child abuse prevention, this study focuses on reviewing the previous policies in preventing child abuse, surveying three main determinants of parents, teachers and children and testing the data collected from the survey. The result shows that education plays an important role in improving the ability to take actions against child abuse. Some recommendations to parents, teachers, children and the government are also proposed for encouraging improvements in child abuse prevention education. Keywords: Child Abuse Prevention, Education, Vietnam DOI: 10.7176/EJBM/12-20-09 Publication date:July 31st 202