Conformance checking of a longwall shearer operation based on low-level events

Conformance checking is a process mining technique that compares a process model with an event log of the same process to check whether the current execution stored in the log conforms to the model and vice versa. This paper deals with the conformance checking of a longwall shearer process. The approach uses place-transition Petri nets with inhibitor arcs for modeling purposes. We use event log files collected from a few coal mines located in Poland by Famur S.A., one of the global suppliers of coal mining machines. One of the main advantages of the approach is the possibility for both offline and online analysis of the log data. The paper presents a detailed description of the longwall process, an original formal model we developed, selected elements of the approach’s implementation and the results of experiments

Application of Process Mining Techniques to Support Maintenance-Related Objectives

The variety of data types generated in manufacturing environments leads to a situation where data-driven approaches for analytical maintenance support no longer have to be limited to the equipment level, but rather can be extended to further perspectives. To this end, this paper examines how process mining(PM) as an approach to extract knowledge about process-related relationships can be applied to support maintenance-related objectives. Our research is carried out by using exemplary data from a manufacturing company, where we successively take different data attributes from various source systems into account and apply selected PM techniques to demonstrate their applicability. As a result, we showcase how different insights can be provided, such as the analysis of a machine\u27s internal behavior, examination of error dependencies across multiple production steps, determination of a machine’s relevance within the equipment network or the discovery of bottlenecks regarding frequencies, cycle times and costs

A spatio-temporal modelling and analysis of digital sensor data for underground mine health and safety

A Research Report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements of the degree of Master of Science 2017Health and safety of employees within their work environment is critical. In the mining industry and especially in underground mines, monitoring and management of health and safety of employees is particularly important Most underground mines today are not fully mechanized, except for coal mines. The industry thus still relies on and employs human personnel. Monitoring and managing these mines and hence personnel health and safety as they undertake their trade is therefore a necessity. Implementation of technology, especially in digital sensor systems and real-time spatial analysis systems, provides a means by which health and safety risk factors can be monitored and information gathered to facilitate determination of prevailing risks or prediction of such risks. Technology therefore can be used to make better decisions and implement specialized emergency response to avert or reduce the extent of injuries, casualties and damages in an underground mine. This research project looks into determination of prominent risk factors in an underground mine, determination of parameters for modeling of such risk factors and the implementation of ESRI’s ArcGIS platform for the retrieval and analysis of streaming sensor data about this parameter from an underground mine. A proof of concept (POC) system is developed that analyses streaming digital sensor data and determines the status of the underground mine environment. The results from this analysis are displayed in a dashboard application for a control room environment. The results and achievements of this research project, especially from a dashboard system perspective, show the possibilities of an integrated GIS-based solution for real-time data processing and determination of the prevailing conditions in an underground mine. This solution also opens up a wide pool of possibilities through which systems integration and its benefits can be achieved, especially in underground mines and focusing on health and safety, as previously silo systems can be integrated at data levels, enabling data sharing, analysis, predictions and making of informed decisions.MT201

A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics

Modern maintenance strategies increasingly focus on vast amounts of diverse data and multifaceted analytical approaches in order to make efficient use of given resources and unveil hidden potentials. While there is often no universal solution approach to a specific case at hand, it is still possible to observe recurring problem classes for which generic solution templates can be applied and thus the establishment of a reusable knowledge base appears beneficial. To this end, we apply a taxonomy development approach to identify and systematize dimensions and characteristics of recurring data analysis problems in data-driven maintenance scenarios. Our research method integrates findings from a systematic literature review and expert interviews with data scientists from industry. Thus, we add descriptive theory to the field of maintenance analytics and propose a taxonomy that distinguishes between analytical maintenance objectives, data characteristics and analytical techniques

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method ApplicationDatengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Applicatio