Criticality evaluation to support maintenance management of manufacturing systems

This paper focuses on criticality evaluation for supporting daily equipment maintenance management and the definition of medium and long-term maintenance actions to improve equipment and, therefore, productivity. These two different purposes led to the development of two different methods for criticality evaluation, using criteria adjusted for each case. The first method is based on rules for defining priorities for corrective and preventive maintenance tasks. Since a failure mode of critical equipment is not necessarily critical, priorities for maintenance tasks are assigned to tasks rather than to equipment. The second method uses Analytic Hierarchy Process to prioritize equipment based on its performance. This method is based on the indicators commonly monitored by maintenance departments. In addition to assessing equipment performance, it considers the maintenance effort made to achieve the evaluated performance. The selection of the criticality criteria and the development of the methods was based on literature review and triggered by a case study in a multinational automotive company. With the integration of the proposed methods in a computerized maintenance management system, maintenance technicians and managers are able to know in real time the tasks that should be performed first and to monitor the overall performance of equipment in the plant, focusing improvements where they are more required.POFC - Programa Operacional Temático Factores de Competitividade (UID/CEC/00319/2013

Efficiency management of robotic production processes at automotive industry

© 2016 Czech Technical University in Prague.The article presents a practical technique to improve processes in production systems of the automobile manufacturers industry. The technique is directed at optimization of the performance of processing equipment by improvement of its service system. The offered approach is based on the usage of decision support system (DSS), which gives a possibility to make scientific and reasonable administrative decisions to adjust production system parameters determined by monitoring of technological process parameters, quality control of finished parts, and the analysis of reasons for idle times. A simulation model was proposed as an intelligent heart of the developed system. To verify adequacy of the proposed solution, the model was tested on the example of production of ring gear in a robotized working area. Practical application of the model allows to reduce investments into the equipment and production infrastructure and also to improve plant operations performance due to selection of optimal parameters for the system

Development of Asset Life Cycle Management System in Process Plant

Managing engineering assets can be a challenging task and optimizing the assets usage is very critical. To ensure the assets is effectively manage and utilize, one have to make effective decision regarding the asset life cycle. The asset life cycle management refers to the effective management system monitoring the performance of the assets throughout their life cycle or “cradle-to-grave” ideology, which mean that the monitoring phase is to be done at beginning stage of purchasing the asset until its retirement time. The objective of this project is to develop the asset life cycle management system suitable to be implemented in processing plant with respect to their condition and environment. For this purpose, the author has identify and analyses a few model including those available in the literature as well as the models that already being implemented in other industries. The information obtained through studies and analyses has been squeeze and manipulate in order to come out with the technical framework of the asset life cycle management system in process plant. The framework developed involving five simple steps and suitable to be implemented with respect to the plant condition and environment. This project also focuses on selection of suitable maintenance strategies to be implemented for the specific equipment in order to have optimum strategies that are safe and cost effective. The outcome of this project would help the decision makers in the process plant to effectively monitoring the assets performance and effectiveness through the system developed

A Fuzzy Criticality Assessment System of Process Equipment for Optimized Maintenance Management.

yesIn modern chemical plants, it is essential to establish an effective maintenance strategy which will deliver financially driven results at optimised conditions, that is, minimum cost and time, by means of a criticality review of equipment in maintenance. In this article, a fuzzy logic-based criticality assessment system (FCAS) for the management of a local company’s equipment maintenance is introduced. This fuzzy system is shown to improve the conventional crisp criticality assessment system (CCAS). Results from case studies show that not only can the fuzzy logic-based system do what the conventional crisp system does but also it can output more criticality classifications with an improved reliability and a greater number of different ratings that account for fuzziness and individual voice of the decision-makers

Influence of the Motor Transport on Sustainable Development of Smart Cities

The transport system is one of the fundamental intelligent systems in the Smart City, and one of the main directions to ensure sustainability and safety of the city transport system is the concept of smart vehicles. Herewith, all processes at all stages of the life cycle should be intellectualized. Since the production stage of the life cycle is one of the most important, the introduction of smart technologies (Industry 4.0) in automotive industry will allow not only to optimize the processes and improve product quality but also to establish favorable conditions for the subsequent intellectualization of the automotive service. The benefits of using smart transport in all fields of activities as well as intellectualization of the decision-making process by the example of the automotive industry enterprises are presented in this chapter

Machine criticality assessment for productivity improvement: Smart maintenance decision support

Purpose\ua0The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.Design/methodology/approach\ua0An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.Findings\ua0The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.Originality/value\ua0Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities

Machine-learning-based condition assessment of gas turbine: a review

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Management: A continuing bibliography with indexes

This bibliography lists 344 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1978