Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

Memory tracking of the health state of Smart products in their lifecycle.

International audienceProduct Lifecycle Management (PLM) is a strategic approach to manage the product /equipment related information efficiently over the whole product lifecycle. To meet this target, a PLM system is developed in this study to track the status of a product/ equipment and its evolution and to analyze problems that may occur at any stage of its life cycle. This paper deals with the generation of an intelligent product that is capable of monitoring and capitalizing its own heath state during its whole life. An approach of knowledge capitalization was developed to construct a memory that identifies the health state of equipment for its whole life. This memory is distributed into a short term memory located at the RFID tag that is associated with the equipment and a long term memory that capitalizes all the information concerning the equipment. This embedded memory is readable directly thanks to the RFID Reader, and provides the information that helps the decision concerning the recycling or not of products/equipments

A formal ontology for industrial maintenance

International audienceThe rapid advancement of information and communication technologies has resulted in a variety of maintenance support systems and tools covering all sub-domains of maintenance. Most of these systems are based on different models that are sometimes redundant or incoherent and always heterogeneous. This problem has lead to the development of maintenance platforms integrating all of these support systems. The main problem confronted by these integration platforms is to provide semantic interoperability between different applications within the same environment. In this aim, we have developed an ontology for the field of industrial maintenance, adopting the METHONTOLOGY approach to manage the life cycle development of this ontology, that we have called IMAMO (Industrial MAintenance Management Ontology). This ontology can be used not only to ensure semantic interoperability but also to generate new knowledge that supports decision making in the maintenance process. This paper provides and discusses some tests so as to evaluate the ontology and to show how it can ensure semantic interoperability and generate new knowledge within the platform

Dynamic Capitalization and Visualization Strategy in Collaborative Knowledge Management System for EI Process

Knowledge is attributed to human whose problem-solving behavior is subjective and complex. In today's knowledge economy, the need to manage knowledge produced by a community of actors cannot be overemphasized. This is due to the fact that actors possess some level of tacit knowledge which is generally difficult to articulate. Problem-solving requires searching and sharing of knowledge among a group of actors in a particular context. Knowledge expressed within the context of a problem resolution must be capitalized for future reuse. In this paper, an approach that permits dynamic capitalization of relevant and reliable actors' knowledge in solving decision problem following Economic Intelligence process is proposed. Knowledge annotation method and temporal attributes are used for handling the complexity in the communication among actors and in contextualizing expressed knowledge. A prototype is built to demonstrate the functionalities of a collaborative Knowledge Management system based on this approach. It is tested with sample cases and the result showed that dynamic capitalization leads to knowledge validation hence increasing reliability of captured knowledge for reuse. The system can be adapted to various domain

Reutilization of diagnostic cases by adaptation of knowledge models.

International audienceThis paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE)

Generating Knowledge in Maintenance from Experience Feedback

Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

Capitalizing and structuring design knowledge in an SME environment

Small companies can find it difficult to preserve their knowledge, and also to structure a design process. A design methodology is proposed, based on design knowledge reuse and suitable for developing new manufacturing processes in an SME context. This paper describes a knowledge structuring and capitalization method, where a functional description is applied. The purpose is to capitalize technical solutions and the components used to carry out a given function, and to build a knowledge base that could be reused when designing new manufacturing processes. In this way, the time spent on research into design concepts can be reduced. Components are identified using the Converter-Transmitter-Operator-Control classification, based on describing the functional flow path in terms of energy. Produced and induced effects associated with the components are highlighted, by identifying the relevant conjugate variables for the functional flows. The choice of solutions in the reuse phase is thus facilitated by considering these effects. In addition, a task decomposition tool has been developed to simplify the describing of existing manufacturing processes. Existing knowledge capitalization methods proved unsuitable for an SME context. Based on the proposed approach, we applied our capitalization method in an industrial context, with the processes used by our partner company, which had never previously capitalized its design knowledge

Smart Maintenance Decision Support Systems (SMDSS)

Computerized information systems are used in all contemporary industries and have been applied to track maintenance information and history. To a lesser extent, such information systems have also been used to predict or simulate maintenance decisions and actions. This work details two models, a population data analysis, and a system infrastructure, to aid operations and maintenance managers with the difficult resource allocation decisions they face in the field. The first model addresses the consideration of component dependency for series network connections using a Markov Decision Process model and solution algorithm. The second model addresses the prioritization of maintenance activities for a fleet of equipment using an Analytical Hierarchy Process and solution algorithm. A recurrent event data analysis is performed for a population data set. The final element is the information system architecture linking these two models to a marketing information system in order to provide quotations for maintenance services. The specific industry of interest is the electrical power equipment industry with a focus on circuit breaker maintenance decision actions and priorities and the development of quotations for repair and replacement services. This dissertation is arranged in a three paper format in which each topic is self contained to one chapter of this document

Manutenção 4.0 no contexto da Universidade de Brasília - UnB

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2019.Esta pesquisa busca analisar a produção acadêmica internacional relacionada à Manutenção na Indústria 4.0, também conhecida como Smart Maintenance, a fim de compreender a sua evolução ao longo do tempo, além dos artigos mais citados na literatura e ainda a relação entre as palavras chaves mais recorrentes. Os dados utilizados foram coletados nas Bases de Dados Scopus e Web of Science, sendo analisados através de uma pesquisa bibliométrica. Foi possível demonstrar que o campo do conhecimento relacionado à Manutenção 4.0 tem crescido significativamente, especialmente a partir de 2011. Tendo sido constatada que a evolução das palavras chave relacionadas ao tema é compatível com a evolução da Indústria 4.0. Também foi analisado o contexto onde se encontra a manutenção dos equipamentos da UnB, através das percepções dos servidores responsáveis pelos laboratórios e também dos servidores do setor de manutenção de equipamentos, no que tange aos aspectos da manutenção e aquisição de equipamentos, bem como os seus conhecimentos relacionados às Indústria 4.0 e Manutenção 4.0. Foi constatado que mesmo com diferentes graus de conhecimento acerca dos temas Indústria e Manutenção 4.0 tanto os responsáveis pelos laboratórios demonstraram interesse na sua implementação, principalmente nas tecnologias relacionadas ao monitoramento on-line de equipamentos. Além disso, foram identificadas oportunidades de pesquisas posteriores no desenvolvimento de soluções da própria UnB no campo da Manutenção 4.0.This research aim is to analyze the international academic production related to Maintenance in Industry 4.0, also known as Smart Maintenance, in order to understand its evolution along the time, the articles most cited in the literature and also the relation between the most recurrent key words. The data used were collected in the Scopus and Web of Science databases, and analyzed through a bibliometric survey. It was possible to demonstrate that the field of knowledge related to Maintenance 4.0 has grown significantly, especially since 2011. It has been verified that the evolution of the key words related to the theme is compatible with the evolution of the Industry 4.0. It was also analyzed the context where the maintenance of the UnB equipment is, through the perceptions of the servers responsible for the laboratories and also of the workers of the equipment maintenance sector, regarding aspects of equipment maintenance and their acquisition, as well as their knowledge related to Industry 4.0 and Maintenance 4.0. It was found that even with different degrees of knowledge about the subjects Industry and Maintenance 4.0, the responsible for the laboratories showed an interest in their implementation, especially in the technologies related to the online monitoring of equipment. In addition, opportunities for further research on the development of UnB's own solutions in the field of Maintenance 4.0 have been identified

Contribution à la spécification et à l'élaboration d'une plateforme de maintenance orientée connaissances

Le maintien en condition opérationnelle des équipements industriels est un des enjeux importants de l'entreprise, et a fait passer la maintenance d'un centre de coût à un centre de profit, ce qui a eu pour conséquence une éclosion de logiciels d'aide à la maintenance allant de la GMAO aux plateformes de e-maintenance. Ces systèmes d'aide fournissent aux différents acteurs de la maintenance, un support à la décision et un ensemble de services permettant une gestion informatisée d'activités de base appartenant au processus de maintenance (exemple l'intervention, la planification, le diagnostic, etc.). Toutefois, les besoins des utilisateurs évoluent dans le temps en fonction de nouvelles contraintes, de leur expertise, des nouvelles connaissances. Par contre les services fournis n'évoluent pas et nécessitent une réactualisation. Afin de tenir compte de l'évolution de ces connaissances, pour que ces systèmes d'aide puissent répondre aux besoins des utilisateurs et puissent proposer des services à la demande et des services évolutifs nous avons fait le point dans cette thèse sur les avantages et limites des systèmes informatiques d'aide existants notamment les plateformes de e-maintenance (systèmes les plus avancés aujourd'hui en maintenance). Pour pallier le manque des systèmes existants, nous avons proposé le concept de s-maintenance qui est caractérisé principalement par les échanges collaboratifs entre applications et utilisateurs, par des connaissances communes du domaine de maintenance. Pour mettre en œuvre ce concept, nous avons proposé une plateforme orientée connaissances assurant des fonctionnalités auto-x (auto-traçabilité, auto-apprentissage, autogestion) qui permettent de répondre aux caractéristiques de la s-maintenance. L'architecture à base de composants de cette plateforme prend appui sur une base de connaissances partagée entre les différents composants qu'elle intègre au profit de l'interopérabilité sémantique ainsi que de la capitalisation des connaissances. Nous avons par ailleurs développé une ontologie du domaine de maintenance sur laquelle s'appuie cette base de connaissances. Finalement, afin de développer les fonctionnalités auto-x assurées par la plateforme nous avons proposé un système à base de traces exploitant la base de connaissances et l'ontologie associéeOperational condition maintenance of industrial equipment is a principal challenge for the firm production. This fact transfer the maintenance from the cost center to the profit center which has lead to massif development of maintenance support system starting from the GMAO to the e-maintenance platform. These systems provide to the maintenance agent, decision-support, and set of services allowing a computerized management of core activities for maintenance process. (e.g. intervention, planning, diagnostic,...). However, the user request continues evolving in time with respect of their expertise, their renewed knowledge and new constraints. On the other hand, the existing services are not following their requirements and they need to be updated. In this thesis, an overview on the advantage and drawback of existing computerized support system, in particular the e-maintenance platform (the most advanced maintenance system) is presented in order to meet the users needs and propose scalable and on-demand services. To overcome the existing system shortage, we propose the s-maintenance concept characterized by the collaborative exchange between users and applications and the common knowledge of the maintenance field. Thus, to implement this concept, a knowledge-oriented platform is proposed providing the auto-x functionalities (auto-traceability, auto-learning and auto-management) and meeting the s-maintenance characteristics. The architecture based on components of this platform, is also based on shared knowledge between integrated components for the benefit of the semantic interoperability as well as for the knowledge capitalization. Maintenance domain ontology is also developed on which the knowledge base is rested. Finally, in order to develop the auto-x functionalities, provided by the platform, a trace-based system is proposed by exploiting the knowledge base and the associated ontology.BESANCON-Bib. Electronique (250560099) / SudocSudocFranceF