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  • 1.
    Fornlöf, Veronica
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. GKN Aerospace.Engine Systems.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Almgren, Torgny
    GKN Aerospace Engine Systems.
    Aircraft engines: A maintenance trade-off in a complex system2015Conference paper (Refereed)
    Abstract [en]

    An aircraft engine is a system of systems with several degrees of complexity. It is important to perform the correct amount of maintenance at each individual maintenance event. A mathematical replacement model is used to ensure that the correct amount of maintenance is performed. However, this paper shows that the reliability of this model could be improved if there were a better way to estimate the life length of on-condition maintained engine parts.

  • 2.
    Fornlöf, Veronica
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. GKN Aerospace.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Almgren, Torgny
    GKN Aerospace.
    On-Condition Parts versus life limited parts: A trade off in aircraft engines2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] U. Kumar, A. Ahmadi, A. K. Verma & P. Varde, 2016, 253-262 p.Conference paper (Refereed)
  • 3.
    Fornlöf, Veronica
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. GKN Aerospace Engine Systems, Trollhättan, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Almgren, Torgny
    GKN Aerospace Engine Systems, Trollhättan, Sweden.
    RUL estimation and maintenance optimization for aircraft engines: A system of system approach2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, 450-461 p.Article in journal (Refereed)
    Abstract [en]

    An aircraft engine is a system of systems with several degrees of complexity. It is important to perform the correct amount of maintenance at each individual maintenance event. A mathematical replacement model is used to ensure that the correct maintenance is performed. The reliability of the results from the mathematical replacement model will be improved if there is a better way to estimate the life length for on-condition engine parts.

  • 4.
    Fornlöf, Veronica
    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.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Almgren, Torgny
    GKN Aerospace Engine Systems, Trollhättan, Sweden.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence, Florence, Italy.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence, Florence, Italy.
    Maintenance, prognostics and diagnostics approaches for aircraft engines2016In: 3rd IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2016: Proceedings, IEEE, 2016, 403-407 p.Conference paper (Refereed)
    Abstract [en]

    In avionics application one of the most important competition factors is the reliability, given that the failure occurrence may leads to a critical state for the functioning of the aircraft. Different maintenance, prognostics and diagnostics approaches are possible with the final aim to optimize both system's availability and safety. Aircraft engines represent a safety critical part of the airplane. For this reason it is a key issue to allocate the proper amount of maintenance at each individual maintenance event. In this paper a mathematical replacement model is proposed to guarantee that the correct amount of maintenance is performed.

  • 5.
    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.

  • 6.
    Gerdes, M.
    et al.
    Hamburg Univ Appl Sci, Aeroaircraft Design & Syst Grp, Hamburg, Germany..
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå Univ Technol, Div Operat & Maintenance Engn, Luleå, Sweden.
    Scholz, D.
    Hamburg Univ Appl Sci, Aeroaircraft Design & Syst Grp, Hamburg, Germany.
    Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning2017In: International Journal of Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, no 8, 424-433 p.Article in journal (Refereed)
    Abstract [en]

    Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

  • 7.
    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.

  • 8.
    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.

  • 9.
    Linnéusson, Gary
    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.
    Wickelgren, Mikael
    University of Skövde, School of Business. University of Skövde, Enterprises for the Future.
    A path forward: Systems thinking maintenance as part of shift in mind on added value2015In: / [ed] Sulo Lahdelma & Kari Palokangas, 2015Conference paper (Refereed)
    Abstract [en]

    Abstract: The purpose and novelty with this recently started research is the introduction of a modelling concept that aims to include the interdependencies maintenance have with financial figures, customer behavior, and production, using systems thinking. It suggests on a path forward in acknowledging short- and long term effects from maintenance on the production system and its financial results. Using systems thinking modelling enables learning on consequences from strategies and policies on the studied system; enabling evaluation of future scenarios supporting decision makers in defining sustainable strategies of action on the policy-level. This paper provides a brief outline of the thoughts behind the research project and points the direction for future research by first introducing aspects regarding the problem and possibilities to address, then briefly introduce different modelling approaches that in part address the problem, which is summarized into a path forward, and finally includes an example of a model by the author of a machine strategy problem that connects the physical assets and actions with financial costs.

  • 10.
    Linnéusson, Gary
    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.
    Wickelgren, Mikael
    University of Skövde, School of Business. University of Skövde, Enterprises for the Future.
    In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, 1, 263-282 p.Conference paper (Other academic)
    Abstract [en]

    The problem of maintenance consequential costs has to be dealt with in manufacturing and is core of this paper. The need of sustainable partnership between manufacturing and maintenance is addressed. Stuck in a best practice thinking, applying negotiation as a method based on power statements in the service level agreement, the common best possible achievable goal is put on risk. Instead, it may enforce narrow minded sub optimized thinking even though not intended so. Unfortunately, the state of origin is not straightforward business. Present maintenance cost modelling is approached, however limits to its ability to address the dynamic complexity of production flows are acknowledged. The practical problem to deal with is units put together in production flows; in which downtime in any unit may or may not result in decreased throughput depending on its set up. In this environment accounting consequential costs is a conundrum and a way forward is suggested. One major aspect in the matter is the inevitable need of shift in mind, from perspective thinking in maintenance and manufacturing respectively towards shared perspectives, nourishing an advantageous sustainable partnership.

  • 11.
    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.

  • 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.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Semantic Framework for Predictive Maintenance in a Cloud Environment2017In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, 583-588 p.Conference paper (Refereed)
    Abstract [en]

    Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

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