DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

A method for obtaining the preventive maintenance interval in the absence of failure time data

One of the ways to reduce greenhouse gas emissions and other polluting gases caused by ships is to improve their maintenance operations through their life cycle. The maintenance manager usually does not modify the preventive intervals that the equipment manufacturer has designed to reduce the failure. Conditions of use and maintenance often change from design conditions. In these cases, continuing using the manufacturer's preventive intervals can lead to non-optimal management situations. This article proposes a new method to calculate the preventive interval when the hours of failure of the assets are unavailable. Two scenarios were created to test the effectiveness and usefulness of this new method, one without the failure hours and the other with the failure hours corresponding to a bypass valve installed in the engine of a maritime transport surveillance vessel. In an easy and fast way, the proposed method allows the maintenance manager to calculate the preventive interval of equipment that does not have installed an instrument for measuring operating hours installed

A method for obtaining the preventive maintenance interval in the absence of failure time data

One of the ways to reduce greenhouse gas emissions and other polluting gases caused by ships is to improve their maintenance operations through their life cycle. The maintenance manager usually does not modify the preventive intervals that the equipment manufacturer has designed to reduce the failure. Conditions of use and maintenance often change from design conditions. In these cases, continuing using the manufacturer's preventive intervals can lead to non-optimal management situations. This article proposes a new method to calculate the preventive interval when the hours of failure of the assets are unavailable. Two scenarios were created to test the effectiveness and usefulness of this new method, one without the failure hours and the other with the failure hours corresponding to a bypass valve installed in the engine of a maritime transport surveillance vessel. In an easy and fast way, the proposed method allows the maintenance manager to calculate the preventive interval of equipment that does not have installed an instrument for measuring operating hours installed.This research was funded by the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” and by the European Regional Development Fund (FEDER), within the framework of the FEDER of Andalusia 2014-2020 Project Reference UHU-202031, and by the grant Ref. RTI2018-094614-B-I00 (SMASHING Project) into the “Programa Estatal de I+D+i Orientada a los Retos de la Sociedad” funded by MCIN/AEI/ 10.13039/497 501100011033

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Proyecto RTI2018-094614-B-I00 (SMASHING), and the “Programa Estatal de I+D+i Orientada a los Retos de la Sociedad

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Ministerio de Ciencia, Innovación y Universidades (MICINN). España RTI2018-094614-B-I00 (SMASHING

Managed Care

By 1993, over 70% of all Americans with health insurance were enrolled in some form of managed care plan. The term managed care encompasses a diverse array of institutional arrangements, which combine various sets of mechanisms, that, in turn, have changed over time. The chapter reviews these mechanims, which, in addition to the methods employed by traditional insurance plans, include the selection and organization of providers, the choice of payment methods (including capitation and salary payment), and the monitoring of service utilization. Managed care has a long history. For an extended period, this form of organization was discouraged by a hostile regulatory environment. Since the early 1980s, however, managed care has grown dramatically. Neither theoretical nor empirical research have yet provided an explanation for this pattern of growth. The growth of managed care may be due to this organizational form's relative success in responding to underlying market failures in the health care system - asymmetric information about health risks, moral hazard, limited information on quality, and limited industry competitiveness. The chapter next explores managed care's response to each of these problems. The chapter then turns to empirical research on managed care. Managed care plans appear to attract a population that is somewhat lower cost than that enrolled in conventional insurance. This complicates analysis of the effect of managed care on utilization. Nonetheless, many studies suggest that managed care plans reduce the rate of health care utilization somewhat. Less evidence exists on their effect on overall health care costs and cost growth.