Graph-based reasoning in collaborative knowledge management for industrial maintenance Bernard Kamsu-Foguem, Daniel Noyes To cite this version: Bernard Kamsu-Foguem, Daniel Noyes. Graph-based reasoning in collaborative knowledge manage- ment for industrial maintenance. Computers in Industry, Elsevier, 2013, Vol. 64, pp. 998-1013. 10.1016/j.compind.2013.06.013. hal-00881052 HAL Id: hal-00881052 https://hal.archives-ouvertes.fr/hal-00881052 Submitted on 7 Nov 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. 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ISSN 0166-3615 Any correspondence concerning this service should be sent to the repository administrator: [email protected] Graph-based reasoning in collaborative knowledge management for industrial maintenance Bernard Kamsu-Foguem *, Daniel Noyes Laboratory of Production Engineering (LGP), EA 1905, ENIT, INPT, University of Toulouse, 47 Avenue d’Azereix, BP 1629, 65016 Tarbes Cedex, France A B S T R A C T Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of Keywords: several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach Experience feedback is applied to a case study focusing on the maintenance management of a rotary machinery system. Conceptual graph rule Expert knowledge Maintenance management Case-based reasoning Fleet considerations 1. Introduction conceptualization of the mechanisms involved in these collabora- tive exchanges facilitates the integration of knowledge manage- The capitalization of the expertise is now a critical issue for ment in collaborative decision-making systems [6]. The research industrial businesses facing the demographic changes in staffing on collaborative decision-making in maintenance management levels and the valuation problems of intangible assets (business has highlighted the information needs for problem-solving reputation, trained workforce, and noncompetition agreements). methods but few integrate the aspect of knowledge management At the maintenance function, technological and organizational [7]. Tools are available at this level [8,9] but they often require a developments force internal and external collaborations to ensure suitable knowledge formalization so they can be shared in a local and global performance of that maintained system [1]. We context, which moved from reactive centralized processing find this situation in socio-technical, networked and distributed architectures, to distributed intelligent infrastructures. Moreover, systems (communication, power, transportation, etc.), for which the deployment of a sustainable action plan for risk management the resources and maintenance skills are also very often requires the integration of know-how and contextualized experi- distributed, with an increasing reliance on technological network ences to develop and implement appropriate maintenance policies systems [2]. Other emerging collaborative networks are developed [10]. Historically, Experience Feedback (EF) was mainly based on by human professionals that may collaborate in virtual communi- statistical methods to identify some failure laws. However, this ties and constitute virtual teams to deal with specific problems or kind of feedback does not allow the extraction of expert knowledge concerns, and find paths to breakthrough successes [3]. Problems from the technical data. This is made possible by the ‘‘cognitive concerning the exploitation of individual practices are sometimes approach’’ of experience feedback modeling. It models the expert based on the management and the structuring of shared knowledge of the organization and facilitates the enrichment of information by a collective [4]. knowledge repository by using methods from artificial intelli- Maintenance activities involve so often collaborative actions gence. Intelligent data analysis and data mining support the and decision-making in which groups of actors are working extraction of patterns and regularities from the process data together through a common area of problem solving [5]. The collected during maintenance activities [11,12]. The transforma- tion of such patterns into explicit knowledge requires knowledge representation formalisms and tools. The cognitive vision frame- work of experience feedback provides means of understanding, * Corresponding author. Tel.: +33 624302337/562442718. E-mail addresses: [email protected] (B. Kamsu-Foguem), interpreting, storing and indexing the activities of experts [13]. EF [email protected] (D. Noyes). applied to the maintenance management takes place as continuous improvement that emphasizes the ongoing monitoring and decision-making of which the effective assessment of alter- verification of the root causes of problems in the monitored natives is a crucial element of guaranteeing the robustness of the system to eliminate repetitive failures and recurring problems maintenance policies. [14]. The approach proposed in this work is based on the EF exploitation in collaborative decision-making situations. The goal is These industrial motivations integrate existing maintenance to improve collaboration among maintenance actors by the principles with modern Web services and the principles of deployment of knowledge engineering tools to effectively share collaborative networks. Collaboration is also useful for computa- experiences, generating knowledge to better resolve problems. The tional intelligence that needs to be fluently re-configured as guided approach of dynamic capitalization of knowledge supports knowl- by the requirements of tractability and agility. edge validation process among collaborating actors, hence increas- Emerging collaborative maintenance networks are formed by ing the reliability of capitalized knowledge [15]. Problem solving human resources (e.g. operator, sub-contractor, maintainer and will include knowledge reasoning based on the conceptual graphs experts) that may collaborate in distributed organizations with (CG) formalism [16]. The choice of knowledge representation by CG virtual project teams. The collaborative networks are again formed should enable a better understanding of critical situations and by automated resources (e.g. Supervisory Control and Data provide assistance to the appropriate decision-making in order to Acquisition (SCADA), Computerized Maintenance Management anticipate them [17]. The expected result is a better use of System (CMMS), Health and Usage Monitoring System (HUMS)) knowledge and skills distributed among different experts: (1) and they may incorporate information technology modules such as strengthening the collective knowledge with the promotion of acquisition, processing and visualization. A collaborative and user- access by the collaborative actors to relevant information and the friendly CMMS is available on the industrial applications and was enhancement of plans for the maintenance of the target system, (2) developed for the technical departments mainly of production facilitating the sustainable management of experience learning and companies or companies who wish to plan the proactive providing support to the modeling and improvement of the quality maintenance of their critical equipment and machines. Moreover of knowledge sharing within the collaborative organization. ‘‘automated collaboration’’ is advocated through ‘‘technical The paper is structured as followed. Section 2 exposes the intelligence’’ collaboration implemented within HUMS, smart proposed methodology and its main components concerning components (to support Integrated Vehicle Health Management knowledge engineering. Section 3 presents the cognitive experi- (IVHM) concept for example) where it is possible to find master ence feedback approach applied to industrial maintenance and slave. management. Section 4 presents the conceptual graphs operations A HUMS framework employs numerous sensors connected to a used to implement the modeling of