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 Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is a systematic method forprocedure analyses and risk assessment. It is a structured way to identify potentialfailure modes of a product or process, probability of their occurrence, and their overalleffects. The basic purpose of this analysis is to mitigate the risk and the impactassociated to a failure by planning and prioritizing actions to make a product or aprocess robust to failure. Effective manufacturing and improved quality productsare the fruits of successful implementation of FMEA. During this activity valuableknowledge is generated which turns into product or process quality and efficiency. Ifthis knowledge can be shared and reused then it would be helpful in early identificationof failure points and their troubleshooting, and will also help the quality managementto get decision support in time. But integration and reuse of this knowledge is difficultbecause there are number of challenges e.g., unavailability of unified criteria of FMEAknowledge, lack of semantic organization, natural language text based description ofknowledge, most of the times FMEA is started from scratch instead of using existingknowledge that makes it incomplete for larger systems, and above all its successdepends on the knowledge which is stored in the brains of perfectionists in the formof experience which may or may not be available anytime anywhere. In this article weare proposing an Information and Communication Technology (ICT) based solutionto preserve, reuse, and share the valuable knowledge produced during FMEA. Inproposed system existing knowledge available in repositories and experts head will begathered and stored in a knowledge base using an ontology, and at the time of need thisknowledge base will be inferred to make decisions in order to mitigate the probablerisks. Ontology based approaches are best suited for the knowledge managementsystems, in which human experts are required to model and analyze their expertisein order to feed them in a conceptual knowledge base for its preservation and reuse

FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were detected but require too much human effort to set up. In this paper, we introduce FLAGS, the Fused-AI interpretabLe Anomaly Generation System, and combine both techniques in one methodology to overcome their limitations and optimize them based on limited user feedback. Semantic knowledge is incorporated in a machine learning technique to enhance expressivity. At the same time, feedback about the faults and anomalies that occurred is provided as input to increase adaptiveness using semantic rule mining methods. This new methodology is evaluated on a predictive maintenance case for trains. We show that our method reduces their downtime and provides more insight into frequently occurring problems. (C) 2020 The Authors. Published by Elsevier B.V

Context ontology development for connected maintenance services

The opportunity to shift from corrective and preventive to data-driven Predictive Maintenance has received a significant boost with the deeper penetration of Internet of Things (IoT) technologies in industrial environments. Processing IoT generated data nonetheless creates challenges for data management and actionable data processing. One way to handle such complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. In this paper, context information management is considered on the basis of a valid knowledge construct for reliability-oriented maintenance management. The aim is to produce a viable semantic organization of data for maintenance services. It is applied on an industrial case linked to maintenance of a distributed fleet of connected production grade industrial printers. The complexity of translating the data generated by such production assets to actionable information is significant, as the status of a single asset is characterised by several hundreds of failure modes and a multitude of event codes. To assess the viability of the ontology for the targeted application, a qualitative usability evaluation study of the ontology is performed

Knowledge Reuse Through Electronic Knowledge Repositories: An Empirical Study And Ontological Improvement Effort For The Manufacturing Industry

Knowledge management adoption is growing, and will continue to grow in no small part because of its recent inclusion into the ISO 9001 quality standard. As organizations look towards ways in which to manage their knowledge, the codification of explicit knowledge through Knowledge Management Systems (KMS) and Electronic Knowledge Repositories (EKRs) will undoubtedly gain more interest. An EKR is a form of KMS that emphasizes the codification and storage of organizational expertise for the purposes of Knowledge Reuse (KRU). Unfortunately, the factors surrounding KRU are not well understood. While previous studies have viewed EKR usage from a narrow perspective, a broader and interconnected view of KRU via EKRs has yet to emerge. Additionally, while there have been numerous benefits linked to EKRs, there are still issues that limit their utility, particularly in the manufacturing arena where information complexity and geography have made it increasingly difficult to share knowledge. Hence, this research employed a two pronged approach. First, using a multi-theoretical perspective to model KRU via EKRs, a quantitative study was conducted and identified several socio-technical factors that predicted greater KRU. These factors had not been previously modeled within the context of KRU via EKRs, and hence add to both the theoretical and practical implications of the domain. Additionally, the KRU construct was also tied to a back end resulting outcome view that was informed by the Expectation Confirmation Model (ECM). Through this view, the research quantitatively validated that KRU not only predicted greater performance, but also impacted greater knowledge sharing and continuance of use. This ancillary benefit helps to reinforce the importance of EKRs in that additional gains are manifested along with the core component of KRU. Second, the research extended the capability of manufacturing EKRs by developing a holistic design and process based ontology that connects key concepts within these domains to provide an overall interconnected view. Additionally, to ensure the relevance of the ontology, a mature and globally recognized industry standard was used as the basis to develop it. The ontology was then formalized and tested via Semantic Web tools: Protege, RDF, and SPARQL. The results demonstrate an improved approach to knowledge recall by providing rich and accurate query returns. The ability to use standalone and federated queries to effectively cull the complexity of this interconnected domain is an enhancement to keyword based and traditional relational database approaches. Additionally, to assist with greater industry adoption a systematic and constructive approach for developing and operationalizing the ontology is provided. Finally, in the spirit of the program in which this dissertation is presented, rounding out the research effort are broader organizational management recommendations for overall knowledge management. Referencing industry targeted literature and syncing them with findings from these two research efforts, several pragmatic and sequentially logical approaches to knowledge management are offered

Certifications of Critical Systems – The CECRIS Experience

In recent years, a considerable amount of effort has been devoted, both in industry and academia, to the development, validation and verification of critical systems, i.e. those systems whose malfunctions or failures reach a critical level both in terms of risks to human life as well as having a large economic impact.Certifications of Critical Systems – The CECRIS Experience documents the main insights on Cost Effective Verification and Validation processes that were gained during work in the European Research Project CECRIS (acronym for Certification of Critical Systems). The objective of the research was to tackle the challenges of certification by focusing on those aspects that turn out to be more difficult/important for current and future critical systems industry: the effective use of methodologies, processes and tools.The CECRIS project took a step forward in the growing field of development, verification and validation and certification of critical systems. It focused on the more difficult/important aspects of critical system development, verification and validation and certification process. Starting from both the scientific and industrial state of the art methodologies for system development and the impact of their usage on the verification and validation and certification of critical systems, the project aimed at developing strategies and techniques supported by automatic or semi-automatic tools and methods for these activities, setting guidelines to support engineers during the planning of the verification and validation phases

An Ontology to Support Semantic Management of FMEA Knowledge

Risk mitigation has always been a special concern for organization’s strategic management. Various tools and techniques have been developed to manage risk in an effective way. Failure Mode and Effects Analysis (FMEA) is one of the tools used for effective assessment of risk. It analyzes all potential failure modes, their causes, and effects on a product or process. Moreover it recommends actions to mitigate failures in order to enhance product reliability. Organizations spend their resources and domain experts make their efforts to complete this analysis. It further helps organizations identify the expected risks and plan strategies in advance to tackle them. But unfortunately the analysis produced after spending a lot of organizational assets and experts’ struggles, is not reusable due to its natural language text based description. Information and communication technology experts proposed some solutions but they are associated with some deficiencies. Authors in [13] proposed an ontology based solution to extract and reuse FMEA knowledge from the textual documents, and this article is the first step towards its implementation. In this article we proposed our ontology for Process Failure Mode and Effects Analysis (PFMEA) for automotive domain, along with its implementation, reasoning, and data retrieval through it

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

A Model-Based Approach to Comprehensive Risk Management for Medical Devices

The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15].The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15]