van der Pas, M.C.A.

English

Case-Based Reasoning is a proven method to provide decision support in a manufacturing context. However, data and knowledge relevant for the case representation is often spread over distributed sources, leading to challenges in the case representation and retrieval. Those challenges require different techniques that this PhD project aims to develop. Techniques for data collection and integration during the case representation, as well as similarity measurement during case retrieval. This paper describes the motivating problem, the research methods, and the current state and future plans

Pure OAI Repository

 Developing a Decision Support System leveraging Distributedand Heterogeneous SourcesCitation for published version (APA):van der Pas, M. C. A. (2023). Developing a Decision Support System leveraging Distributed and HeterogeneousSources: Case-Based Reasoning for Manufacturing Incident Handling. In P. Reuss, & J. Schönborn (Eds.),Doctoral Consortium Proceedings of the 30th International Conference on Case-Based Reasoning co-locatedwith the 30th International Conference on Case-Based Reasoning, ICCBR 2022 (CEUR Workshop Proceedings;Vol. 3418). CEUR-WS.org. https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper48.pdfDocument license:CC BYDocument status and date:Published: 13/06/2023Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 05. Oct. 2023Developing a Decision Support System leveragingDistributed and Heterogeneous Sources: Case-BasedReasoning for Manufacturing Incident HandlingM. van der Pas1,2,∗1Eindhoven University of Technology, Department of Industrial Engineering & Innovation Sciences, Eindhoven 5600MB,The Netherlands2Semaku B.V., Eindhoven 5617BC, The NetherlandsAbstractCase-Based Reasoning is a proven method to provide decision support in a manufacturing context.However, data and knowledge relevant for the case representation is often spread over distributedsources, leading to challenges in the case representation and retrieval. Those challenges require differenttechniques that this PhD project aims to develop. Techniques for data collection and integration duringthe case representation, as well as similarity measurement during case retrieval. This paper describesthe motivating problem, the research methods, and the current state and future plans.KeywordsIncident Handling, Traceability, Case-Based Reasoning, Semantic Web, Knowledge Graph, Event Graph1. IntroductionOne of the challenges identified for Case-Based Reasoning (CBR) research is the acquisitionof cases from heterogeneous and distributed data sources [1]. This challenge certainly alsoapplies to complex manufacturing environments, where CBR can be applied to assist engineerswith the handling of quality incidents. In case customers have an issue with a device, theymight initiate a (quality) complaint at the company that produced the device. The companyshould then analyse the complaint and take suitable measures, like containment and correctiveaction. This complaint handling process is taking an increasing amount of effort, caused bythe increasing product and production process complexity. Especially in the semiconductorindustry [2], which is the main motivating use case for this project. During the handling processthere are already some commonality checks done to find historic complaints related to a newcomplaint. However, due to the challenges described below only to limited extend.In manufacturing companies there are many data available about the products and theirproduction process, which can be used to describe a (complaint) case. These data are oftenspread over many different systems. Not only because of the different types of data that are ofinterest, but also because of the complexity of the semiconductor production process, consistingICCBR DC’22: Doctoral Consortium at ICCBR-2022, September, 2022, Nancy, France∗Corresponding author.Envelope-Open m.c.a.v.d.pas@tue.nl (M. v. d. Pas)© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).CEURWorkshopProceedingshttp://ceur-ws.orgISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)1M. van der Pas ICCBR’22 Workshop Proceedingsof many different production steps spread over multiple facilities. At the same time it is costly toindex all data in a central case base. Therefore, a system that supports engineers with identifyingsimilar cases (historic complaints), will have to deal with distributed and heterogeneous sources.These characteristics introduce specific challenges, and this research will try to solve some ofthem, focusing on case representation and retrieval phase of the CBR cycle [3].2. Research Plan, Objectives, and ApproachThe goal of this project is to develop a (decision support) system that assists engineers in thehandling of manufacturing incidents by providing cases similar to the new case they have athand. The system should leverage data and knowledge from distributed and heterogeneoussources. As such, answer the following research question: How to identify related qualityincidents in a manufacturing environment with distributed and heterogeneous dataand knowledge sources?2.1. Sub-projectsThe project is divided into four sub-projects. The topic of the first sub-project is the devel-opment of a general framework for CBR-based decision support leveraging distributed andheterogeneous sources. The other sub-projects focus on specific components in this framework,for case representation and retrieval. More details about the evaluation of the components andsystem can be found in subsection 2.2.I. FrameworkHow to design a system to find related quality incidents in a manufacturing environment withdistributed and heterogeneous data and knowledge sources?The first sub-project focuses on a framework to support the CBR-cycle, more specifically thecase representation based on distributed sources. In distributed decision support systems [4] andCBR systems for knowledge management [5, 6, 7] it is common to use an agent-based approach.Chaudhury et al. [8] proposed a solution for CBR with distributed storage of cases. Similarly,Camarillo [9] proposed a knowledge management framework using CBR in an industrial context.However, both focus on combining cases from distributed case bases, while this research focuseson gathering data from distributed systems for case representation. Therefore, the systemshould be able to collect and integrate data from heterogeneous sources to describe a newcase, refine and enrich the case representation, and provide similar cases back to the user. Anoverview of the main steps can be found in Figure 1. The system will to a large extend rely onSemantic Web Technologies, which are proven to be suitable for combining data and knowledgedriven approaches [10]. The main components of this architecture are investigated as partof the other sub-projects. Once the components are developed, the framework will also beimplemented and evaluated with the (source) systems in a case study.2M. van der Pas ICCBR’22 Workshop ProceedingsFigure 1: Main steps of the CBR-based decision support for quality incidents.II. Case Representation: Trace identificationHow to identify the entities and events that were involved in the production process of a case?During the production process often multiple case identifiers are used and production batchesare split and merged. For example, when multiple semi-finished goods are assembled into onedevice. This results in multi-dimensional event data, and introduces fuzziness and uncertaintyin the trace. Therefore, it is a challenge to collect relevant data and information to build a caserepresentation. The production trace [11] can serve as the foundation for the case representation.The production trace describes the production process of a device and consists of productionevents and related entities. It can be represented as an event graph, which is well suited torepresent multi-dimensional event data [12], combining the time and relation dimension. Incomparison to Esser and Fahland [12], this research aims to enrich the event graphs withknowledge encoded in ontologies. For example, Lee and Park [13] improved the traceabilityusing information about the bill of materials. The developed technique should be able togenerate an event graph consisting of events and entities on different levels of aggregation,which describe the production trace for the case at hand.III. Case Representation: Data integrationHow to integrate data from distributed and heterogeneous sources?The first step of the representation phase introduced by Finnie and Sun [14] is to constructa case description. This research will focus on event and sensor data for describing the case,which are common in the manufacturing domain [15]. As it is costly to integrate and storeall sensor data centrally, a method is required to reduce the data volume and dimensionalityto be able to integrate it into one case representation. Wang et al. [16] propose to aggregatesensor data to events and integrate those events with graph structured context data. Similarly,the system developed by Gundersen et al. [17] abstracts sensor data to events using patternmatching. Those events are subsequently used in CBR to find similar situations from the past.In a similar way, this research aims to use machine learning techniques to extract featuresfrom a series of data points[18]. Those data points are generated by sensors on the productionequipment and describe the production process of one device. The extracted features shouldcorrelate to quality incidents.3M. van der Pas ICCBR’22 Workshop ProceedingsIV. Case Retrieval: Similarity MeasurementHow to find similar quality incidents based on heterogeneous incident descriptions leveragingdomain knowledge?The goal of this sub-project is to develop a technique that can be used to retrieve cases similar toa novel case. Camarillo et al. [6] use a predefined set of attributes to describe the case and its con-text. To deal with the distributed and heterogeneous sources, a more flexible case representationformat is required. Therefore, this research aims to use RDF (Resource Description Framework)knowledge graphs and corresponding graph-based similarity measurement techniques. Zhanget al. [19] also used knowledge graphs, but conclude that more work needs to be done on thesimilarity and knowledge reasoning. Furthermore, domain knowledge is required to conductproper similarity measurement. This knowledge can be represented by taxonomies or ontologies[6, 20, 21, 5, 22]. There are various standards for describing ontologies/taxonomies using RDF,for example OWL1 and SKOS2, which as such can be integrated in the case representation.2.2. Evaluation of the systemThe sub-projects will result in different components of the system, which require differentmethods and data sets to validate and evaluate their functioning.Sub-project II The data collection and integration solution can be validated using simulatedor actual (from a semi-conductor use case) production events. However, there exists no dataset with ’known-good production traces’. Therefore, the aim is to identify and reconstruct anumber of traces for a validation data set, with the help of engineers.Sub-project III The feature extraction technique can be evaluated using sensor data collectedfrom equipment that is used in the production process. After most production steps a qualitycheck is done. The results of this check can be used to validate if the derived features are indeedcorrelated to quality incidents.Sub-project IV The aim is to evaluate the graph-based case comparison technique, using adata set from the semi-conductor use case described in the introduction (handling of customercomplaints). The data set consists of historic complaints, traceability data (production eventsfrom different Manufacturing Execution Systems (MES)), and data from Product Life cycleManagement (PLM) systems. In practice there are already some commonality checks done bythe engineers, which can be used as a benchmark.The integrated system The preferred method of evaluating the integrated system, whichintegrates the techniques developed in the sub-projects, is to combine the data sets used toevaluate those sub-projects. However, the challenge is that only a very small portion of produceddevices result in a complaint, which in turn are only detected months to years after production.Therefore, it will be difficult to construct a data set with relevant sensor and complaint data. A1https://www.w3.org/TR/owl2-primer/2https://www.w3.org/TR/skos-primer/4M. van der Pas ICCBR’22 Workshop Proceedingspossible solution is to use simulated data, based on the data collected before. An alternative isto find a different use case, in which quality incidents occur with higher frequency, such that itis possible to construct a data set that contains related sensor and incident data.3. Progress SummaryAt the time of submission, most work is done on defining the framework for data collectionand integration (sub-project I) in the context of the MAS4AI project. In future, the developedframework will be evaluated in a case study, using data and information sources from a manu-facturing company. Next to the work on sub-project I, two case studies are in progress whichlook into modelling event graphs based on manufacturing events, and feature extraction fromsensor data, respectively contributing to sub-project II and III. Both focus on a specific step inthe semi-conductor production process.AcknowledgementsThis project is supported by the European Union‘s Horizon 2020 research and innovationprogramme under grant agreement No. 957204, the project MAS4AI (Multi-Agent Systems forPervasive Artificial Intelligence for assisting Humans in Modular Production).References[1] A. K. Goel, B. Diaz-Agudo, What’s Hot in Case-Based Reasoning, in: Proceedings of theThirty-First AAAI Conference on Artificial Intelligence, 2017, pp. 5067–5069.[2] J. Aelker, T. Bauernhansl, H. Ehm, Managing complexity in supply chains: A discussion ofcurrent approaches on the example of the semiconductor industry, Procedia CIRP 7 (2013)79–84. doi:10.1016/J.PROCIR.2013.05.014.[3] A. Aamodt, E. Plaza, Case-Based Reasoning: Foundational Issues, Methodological Vari-ations, and System Approaches, AI Communications 7 (1994) 39–59. doi:10.3233/AIC-1994-7104.[4] K. Sycara, D. D. Zeng, Multi-Agent Integration of Information Gathering and DecisionSupport, in: 12th European Conference on Artificial Intelligence, John Wiley & Sons, Ltd.,1996, pp. 549–553.[5] P. Reuss, K.-D. Althoff, A. Hundt, W. Henkel, M. Pfeiffer, Multi-Agent Case-Based Diagnosisin the Aircraft Domain, in: Proceedings of the ICCBR 2015, Frankfurt, 2015, pp. 43–52.[6] A. Camarillo, J. Ríos, K.-D. Althoff, Knowledge-basedmulti-agent system formanufacturingproblem solving process in production plants, Journal of Manufacturing Systems 47 (2018)115–127. doi:10.1016/j.jmsy.2018.04.002.[7] W. Luís, J. Carlos, E. Ferreira, F. Gonzalez, C. Gomes, R. M. Lorenzo, W. Luı´s, L. Mikos,C. Espı´ndola, E. Ferreira, A combined multi-agent and case-based reasoning approachto support collaborative nonconformance problem solving in the thermoplastic injectionmoulding process, https://doi.org/10.1080/09511920903440321 23 (2010) 177–194. doi:10.1080/09511920903440321.5M. van der Pas ICCBR’22 Workshop Proceedings[8] S. Chaudhury, T. Singh, P. S. Goswami, Distributed fuzzy case based reasoning, AppliedSoft Computing 4 (2004) 323–343. doi:10.1016/J.ASOC.2003.10.003.[9] A. Camarillo González, Knowledge Management framework based on an Agents Networkto support Continuous Improvement in Manufacturing integrating Case-Based Reasoningand Product Lifecycle Management, Ph.D. thesis, Universidad Politécnica de Madrid, 2018.doi:10.20868/UPM.thesis.53989.[10] B. Steenwinckel, D. De Paepe, S. Vanden Hautte, P. Heyvaert, M. Bentefrit, P. Moens,A. Dimou, B. Van Den Bossche, F. De Turck, S. Van Hoecke, F. Ongenae, FLAGS: Amethodology for adaptive anomaly detection and root cause analysis on sensor datastreams by fusing expert knowledge with machine learning, Future Generation ComputerSystems 116 (2021) 30–48. doi:10.1016/j.future.2020.10.015.[11] R. Schuitemaker, X. Xu, Product traceability inmanufacturing: A technical review, ProcediaCIRP 93 (2020) 700–705. doi:10.1016/J.PROCIR.2020.04.078.[12] S. Esser, D. Fahland, Multi-Dimensional Event Data in Graph Databases, Journal on DataSemantics 10 (2021) 109–141. doi:10.1007/S13740-021-00122-1/TABLES/6.[13] D. Lee, J. Park, RFID-based traceability in the supply chain, Industrial Management andData Systems 108 (2008) 713–725. doi:10.1108/02635570810883978/FULL/PDF.[14] G. Finnie, Z. Sun, R5 model for case-based reasoning, Knowledge-Based Systems 16 (2003)59–65. doi:10.1016/S0950-7051(02)00053-9.[15] H.-N. Dai, H. Wang, G. Xu, J. Wan, M. Imran, Enterprise Information Systems Big dataanalytics for manufacturing internet of things: opportunities, challenges and enablingtechnologies Big data analytics for manufacturing internet of things: opportunities, chal-lenges and enabling technologies, Enterprise Information Systems 14 (2020) 1279–1303.doi:10.1080/17517575.2019.1633689.[16] H. Wang, P. Nguyen, J. Li, S. Kopru, G. Zhang, S. Katariya, S. Ben-Romdhane, GRANO:Interactive graph-based root cause analysis for cloud-native distributed data platform,Proceedings of the VLDB Endowment 12 (2019) 1942–1945.[17] O. Erik Gundersen, A. Aamodt, P. Skalle, A Real-Time Decision Support System for HighCost Oil-Well Drilling Operations PhD project View project Reproducible AI View project,Ai Magazine 34 (2012) 21. doi:10.1609/aimag.v34i1.2434.[18] F. J. Baldán, J. M. Benítez, Multivariate times series classification through an interpretablerepresentation, Information Sciences 569 (2021) 596–614. doi:10.1016/J.INS.2021.05.024.[19] Y. Zhang, X. Liu, J. Jia, X. Luo, Knowledge representation framework combining case-based reasoning with knowledge graphs for product design, Computer-Aided Design andApplications 17 (2020) 763–782. doi:10.14733/CADAPS.2020.763-782.[20] G. Cândea, S. Kifor, C. Constantinescu, Usage of Case-based Reasoning in FMEA-drivenSoftware, Procedia CIRP 25 (2014) 93–99. doi:10.1016/J.PROCIR.2014.10.016.[21] B. Chebel-Morello, M. K. Haouchine, N. Zerhouni, Reutilization of diagnostic cases byadaptation of knowledge models, Engineering Applications of Artificial Intelligence 26(2013) 2559–2573. doi:10.1016/J.ENGAPPAI.2013.05.001.[22] S. H. El-Sappagh, M. Elmogy, Case Based Reasoning: Case Representation Methodologies,IJACSA) International Journal of Advanced Computer Science and Applications 6 (2015).6

Developing a Decision Support System leveraging Distributed and Heterogeneous Sources:Case-Based Reasoning for Manufacturing Incident Handling

https://pure.tue.nl/ws/files/306590988/ICCBR_2022_DC_paper48.pdf

Developing a Decision Support System leveraging Distributed and Heterogeneous Sources:Case-Based Reasoning for Manufacturing Incident Handling

Abstract

Similar works

Full text

Available Versions

Pure OAI Repository