Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

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

Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation

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

An Ontology for Sustainability Reporting Based on Global Reporting Initiative (GRI) G4

The aim of this research is to fill the gap by developing ontology for Sustainability Reporting based on GRI G4 Guidelines. The chief research question is: What is the best approach to developing an Ontological Model for the knowledge domain Sustainability Reporting? The main objective of this research is to develop such ontology for Sustainability Reporting based on GRI G4.The developed ontology for Sustainability Reporting was validated by applying it to existing business data

Experience Factory.

INTRODUCTION Reuse of products, processes and experience originating from the system life cycle is seen today as a feasible solution to the problem of developing higher quality systems at a lower cost. In fact, quality improvement is very often achieved by reusing and modifying over and over the same elements, learning about them by direct experience. This article presents an infrastructure, called the experience factory, aimed at capitalization and reuse of life cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from the ones of the development organization. The activities of the development organization and of the experience factory can be outlined in the following way: • The development organization, whose mission is to develop and deliver systems, provides the experience factory with product development and environment characteristics, data, and a diversity of models (resources, quality, product, process) currently used by the projects in order to deliver their capabilities. • The experience factory, through processing this information and other state-ofthe-practice notions, will return direct feedback to each project activity, together with goals and models tailored from previous project increments. It will als

Product-service systems scenarios simulation based on G-DEVS/HLA: Generalized discrete event specification/high level architecture

International audienceIn the past decade, personal customers expected manufacturing companies to provide them with a physical product with, nonetheless, some basic additional services. Currently, customers expect a more comprehensive solution, integrating both a physical product and non-physical services, which explains why companies have started to propose Product-Service Systems (PSS). The underlying objective of profitability can be attained if the system is designed, based on system use, to avoid waste, and if services are developed jointly with products. Although all the requisite conditions are well-known, the optimal way to satisfy them is not formalized or even guided by any clear methodology. This paper proposes to create PSS models to be simulated in different service scenarios based on G-DEVS/HLA. The simulation results provide pointers to help decision maker choose between several PSS design scenarios to be manufactured. A case study from the toy industry is used to illustrate the proposed methodology

Ontology Engineering: a Survey and a Return on Experience

Ontology is a new object of IA that recently came to maturity and a powerful conceptual tool of Knowledge Modeling. It provides a coherent base to build on, and a shared reference to align with, in the form of a consensual conceptual vocabulary, on which one can build descriptions and communication acts. This report presents the object that is called "an ontology" and a state of the art of engineering techniques for ontologies. Then it describes a project for which we developed an ontology and used it to improve knowledge management. Finally it describes the design process and discuss the resulting ontology