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  • 1.
    Eklind, Samuel
    University of Skövde, School of Technology and Society.
    Utveckling av gåbord2007Independent thesis Basic level (degree of Bachelor), 15 credits / 22,5 HE creditsStudent thesis
  • 2.
    Galar, Diego
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Kans, Mirka
    Linnaeus University, Kalmar, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining2016In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen, Helena Kortelainen, Jussi Aaltonen,Teuvo Uusitalo, Kari Komonen, Joseph Mathew & Jouko Laitinen, Springer, 2016, 161-171 p.Conference paper (Refereed)
    Abstract [en]

    Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.

  • 3.
    Gerdes, Mike
    et al.
    Aero - Aircraft Design and Systems Group, Hamburg University of Applied Sciences, Hamburg, Germany.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden.
    Fuzzy condition monitoring of recirculation fans and filters2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, 469-479 p.Article in journal (Refereed)
    Abstract [en]

    A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

  • 4.
    Gerdes, Mike
    et al.
    Hamburg University of Applied Sciences Aero - Aircraft Design and Systems Group, Hamburg, Germany / Luleå University of Technology, Division of Operation and Maintenance Engineering, Luleå, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Division of Operation and Maintenance Engineering, Luleå, Sweden.
    Scholz, Dieter
    Hamburg University of Applied Sciences Aero - Aircraft Design and Systems Group, Hamburg, Germany.
    Decision Trees and the Effects of Feature Extraction Parameters for Robust Sensor Network Design2017In: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 19, no 1, 31-42 p.Article in journal (Refereed)
    Abstract [en]

    Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classy different errors with a 75% probability and how different feature extraction options influence the information gain.

  • 5.
    Häggblom, Hanna
    University of Skövde, School of Engineering Science.
    Tillståndsbaserat underhåll på verktygsmaskiner: Utvärdering av det tillståndsbaserade underhållet på verktygsmaskiner hos Volvo Cars i Skövde och hur det kan utvecklas2013Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A company can use different strategies when performing maintenance. One strategy is corrective maintenance where maintenance is carried out after a failure has occurred. Another strategy is preventive maintenance. A combination of the two is often used. The preventive maintenance can be predetermined or condition based. Advantages with the condition based maintenance are e.g. that the replacement of parts can be planned according to observations indicating there’s an increasing risk of failure. When choosing preventive maintenance, the RCM method can be used to determine risk of failure modes and the cause of function failures. Decisions regarding the preventive maintenance should be based on knowledge in new techniques, history of failures and the equipment and competence available.

    Volvo Cars in Skövde is doing more or less the same preventive maintenance on machine tools regardless of whether it’s a new or old machine. The company is using condition based maintenance on some of the components and units. Mostly it consists of human inspections, a subjective control of the machine’s condition, and spindle vibration analysis. Through interviews it has been determined that new techniques are rarely implemented and that the decided maintenance plan for a machine is rarely changed. The company is aware of the development of new techniques for condition based maintenance and wants to investigate whether it is appropriate to evolve their condition based maintenance on machine tools. The techniques further investigated are measurement with Ball bar, monitoring of process parameters and geometrical verification.

    To make accurate decisions regarding the implementation of new techniques, a data collection of the history of frequent failures was done. To limit the amount of data a five year old processing line was studied. Data from the maintenance system Maximo was sorted by long failures from the recent years and by prioritizing with the Pareto principle it showed the recurring failures of ball screws. The component do have a condition based maintenance activity today, there is an inspection once a year. However, the inspection is rarely able to predict or find a failure on the ball screw. Four out of twenty-seven ball screws with fault have been changed after inspection over the last five years. By mapping the components failures on fifteen machine tools and when preventive maintenance has been done, several options for improvements were found.

    The cost of the replacement of a ball screw when a failure has already occurred is as much as ten times the cost of replacement before failure. This is caused by cost of scrap and long down time. A new, improved preventive maintenance task is therefore highly justified. The motif is both reducing the scrap cost related to failure and with increased availability.

    The company is recommended an investment in Ball bar as a new method for condition based maintenance. It should be used to increase the objective control of the mechanical parts of a machine tool. The method is not totally new for the company and relatively easy to implement. The current subjective control on the machine tools should be revised to increase the probability of finding degradation of functions. Today the activity takes rather long time and has therefore not been done in time, it should be shorter. The company is also recommended to investigate new methods for preventive maintenance continually. Today there is no employee with that mission and to evolve maintenance, the company has to be updated on new techniques.

  • 6.
    Linnéusson, Gary
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Investigating Maintenance Performance: A Simulation Study2016In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper (Refereed)
    Abstract [en]

    Maintenance can be performed in multiple procedures, and it is hard to justify investments in preventive work. It is a complex equation between the inherent complexity of maintenance and its tight dependencies with production, but also the aspect of direct cost and consequential costs from activities. A model is presented that quantify dynamics of maintenance performance in order to enable a systems analysis on the total of consequences from different strategies. Simulation offers experimenting and learning on how performance is generated. The model is based on parts of previous research on maintenance modelling, system dynamics, maintenance theory, and mapping of practical information flows in maintenance. Two experiments are presented that both take off from a reactive strategy of maintenance performance, and implement two different strategies for preventive maintenance. Using the model enriches the analysis on how the aspects of maintenance performance work together with different maintenance strategies.

  • 7. Munoz Alonso, Carlos
    et al.
    Unda Garcia, Alejandro
    Design and analysis of a large transportable vacuum insulated cryogenic vessel2010Independent thesis Basic level (degree of Bachelor), 15 credits / 22,5 HE creditsStudent thesis
    Abstract [en]

    This project has been undertaken by Alejandro Unda García and Carlos Muñoz Alonso for Veprox AB (www.veprox.se), a Swedish engineering company, in collaboration with the School of Technology and Society at the University of Skövde (www.his.se). This thesis represents the final project for the Bachelor Degree Exam in Mechanical Engineering during the academic year 2009-2010.

    To achieve this project, we were helped and supported by Peter Höglund, Veprox AB Office Manager; Tomas Walander, Supervisor and M. Sc. student and Karl Mauritsson, Examiner and Lecturer, both belonging to the Department of Mechanical Engineering at the School of Technology and Society of the University of Skövde.

    This project is aimed to study, design and analyze a large transportable vacuum insulated cryogenic vessel that will be attached to a truck in order to keep, maintain and transport by road liquid methane at a temperature of -162 °C.

    Considerations such as different pressure loads, dimensions, materials as well as their mechanical properties, constraints, masses, insulation systems and weather-environmental conditions (Northern Europe) are made in the mechanical analysis. Furthermore, calculations and dimensions satisfy the requirements given by the Swedish Standards Institute (S.I.S) following the standards SS-EN 13530-1: Cryogenic vessels - Large transportable vacuum insulated vessel; Part 1: Fundamental requirements and SS-EN 13530-2: Cryogenic vessels - Large transportable vacuum insulated vessel; Part 2: Design, fabrication, inspection and testing.

    The CAD software Pro/Engineer Wildfire 4.0 is used to visualize the models for the chosen designs. In addition, the finite element module Pro/Mechanica is used to obtain results of mechanical analyses in order to determine if the stresses are within margins.

  • 8.
    Sachdeva, Nitin
    et al.
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Singh, Ompal
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Kapur, P. K.
    Amity University, Noida, India.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 3, 316-324 p.Article in journal (Refereed)
    Abstract [en]

    Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today's dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which required multi criteria decision environment. In this paper we propose a hybrid TOPSIS method combined with intuitionistic fuzzy set to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed intuitionistic fuzzy weighted averaging operator. Lastly sensitivity analysis is performed so as to evaluate the impact of criteria weights on final ranking of alternatives.

  • 9.
    Sandberg, Ulf
    Dept of Engineering Science, University West, Trollhättan, Sweden.
    How an enlarged maintenance function affects the performance of industrial maintenance and maintenance services2013In: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, no 3, 265-275 p.Article in journal (Refereed)
    Abstract [en]

    Today, many companies struggle with maintenance work established by pre–requisites during installation, start–up and use of plants, systems and machines. In search for improvements in existing maintenance organisations, it is easy to conclude that they look like and is a direct consequence of previous engineering decisions and activities. Maintenance work, however, only exists in the operation phase with weak coupling to what created it. Using an empirical knowledge base, several examples of this narrow view on maintenance is given along with the consequences it results in today. By linking together activities in the establishment life–cycle phase with the operation phase it is shown how an enlarged maintenance function could improve the situation. Using references to the leading actors creating the frontier today it is illustrated where most companies could be tomorrow. The paper hopefully will contribute to how maintenance processes and activities are being designed, set–up and carried out.

  • 10.
    Sandberg, Ulf
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Kungliga Tekniska Högskolan.
    Management of factory and maintenance information for multiple production and product life-cycle phases2014In: / [ed] B.K.N.Rao, 2014, 7 s- p.Conference paper (Refereed)
    Abstract [en]

    Maintenance is crucial for future manufacturing systems. An extended local knowledge is essential to increase precision and efficiency, but also for improvements of the maintained object itself. Approaches exist that closes the loop from end-user to vendor, but intra loops are not so well developed.

    This article discusses ways to interconnect and manage data and knowledge flow between work processes in user and vendor life-cycles. It aims to inspire improvements in existing approaches, closer connections between producer and customer, between users, and improved quality of maintenance work via factory-, company-, or group-wide data and knowledge about similar types of equipment.

  • 11.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Information Fusion processes in Prognostics and Health Management2014Conference paper (Other academic)
    Abstract [en]

    Information Fusion plays important role in Prognostics and Health Management, where data and informations from different sources need to be combined, analyzed and finally used or presented for proper maintenance decisions. The objective of this paper is to outline and analyzed the relation between Information Fusion process and data/information processing in Prognostics Condition Based Maintenance. The Data Fusion Information Group Model (DFIGM) is presented as well as distinction between two levels of information fusion: (1) low-level information fusion (LLIF), which addresses the signal processing, object state estimation and characterization, and (2) high-level information fusion (HLIF) focused on control and situational understanding. All this processes are aligned with condition based maintenance processes from data acquisition and processing, through diagnostics, prognostics, up to health management. Presented work is one of the first steps in the research project toward improvements in Condition Based Maintenance with focus on its implementation in the manufacturing industry.

  • 12.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Diego, Galar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Sweden.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH.
    Asset management evolution: from taxonomies toward ontologies2015In: Maintenance, Condition Monitoring and Diagnostics, Maintenance Performance Measurement and Management / [ed] Sulo Lahdelma and Kari Palokangas, Oulu, Finland: POHTO , 2015Conference paper (Refereed)
    Abstract [en]

    This paper addresses the evolution that can be observed in Asset Management in modelling approach. Most traditional Condition Monitoring systems use hierarchical representations of monitored the integration of data from disparate source toward context awareness and Big Data utilization there is a need to include and model more complicated dependencies than hierarchical. Ontology based modelling is gaining recently on popularity in the domain of Condition Monitoring and Asset Management.

  • 13.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Big data in maintenance decision support systems: aggregation of disparate data types2016In: Euromaintenance 2016 ConferenceProceedings, 2016, 503-512 p.Conference paper (Other academic)
    Abstract [en]

    There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.

  • 14.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Context Awareness in Predictive Maintenance2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, 197-211 p.Chapter in book (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

  • 15.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden.
    Galar, Diego
    Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.
    Context preparation for predictive analytics – a case from manufacturing industry2017In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, 341-354 p.Article in journal (Refereed)
    Abstract [en]

    Purpose

    The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

    Design/methodology/approach

    This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

    Findings

    Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

    Originality/value

    This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

  • 16.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Sandberg, Ulf
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering Royal Institute of Technology, Sweden.
    Next Generation Condition Based Predictive Maintenance2014In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson, Mats Björkman, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.

  • 17.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Cloud-enhanced predictive maintenance2016In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

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