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  • 1.
    Alzghoul, Ahmad
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Löfstrand, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Increasing availability of industrial systems through data stream mining2011In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 60, no 2, p. 195-205Article in journal (Refereed)
    Abstract [en]

    Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.

  • 2.
    Alzghoul, Ahmad
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Löfstrand, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Backe, Björn
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Data stream forecasting for system fault prediction2012In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 62, no 4, p. 972-978Article in journal (Refereed)
    Abstract [en]

    Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing.In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM).The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.

  • 3.
    Fredin, Johan
    et al.
    Blekinge Institute of Technology School of Engineering Soft Center.
    Jönsson, Anders
    Blekinge Institute of Technology School of Engineering Soft Center.
    Broman, Göran
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Holistic methodology using computer simulation for optimisation of machine tools2012In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 63, no 1, p. 294-301Article in journal (Refereed)
    Abstract [en]

    Virtual machine concepts supporting optimization of machine tools have been developed in earlier work. The virtual machine concept is a tool that can describe the behaviour of a machine tool while considering the interaction between mechanics of the machines and the control system. Considerable amount of work has been done proving the concept and showing the potential of such a design tool in different contexts. Several studies have shown the potential of using the virtual machine concept, although, no work has been found that is exploring the potential of a full optimization study.The aim of this work is to show the potential of the virtual machine concept in an optimisation study of the complete machine tool, including the mechanical system, parameters in the control system, the NC-code as well as choice of servo and drive systems. An efficient optimisation strategy is presented, making it possible to solve the complex optimisation problem within a reasonable amount of time.A combination of optimisation algorithms is used to achieve a fast and accurate way of solving the complex task to optimise the complete machine tool. Genetic algorithms, gradient based algorithms and more traditional hands on engineering are used for solving the optimisation problem. Post processing and data mining is suggested as a way of extracting as much information as possible from optimisation results with the aim to increase the knowledge about the studied system. An important conclusion is that the virtual machine should support the decision making in product development, not replace the product developers as regards decision making.

  • 4. Nilsson, Kristina
    et al.
    Segerstedt, Anders
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Corrections of costs to feasible solutions of economic lot scheduling problems2008In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 54, no 1, p. 155-168Article in journal (Refereed)
    Abstract [en]

    The paper considers the problem of choosing order quantities and a cyclic production pattern for several products that are produced in a common capacity constrained facility. The heuristic method from Segerstedt [Segerstedt, A. (1999). Lot sizes in a capacity constrained facility with available initial inventories. International Journal of Production Economics, 59, 469-475] is modified and improved. The method is compared with the heuristic technique according to Doll [Doll, C. L., & Whybark, D. C. (1973). An iterative procedure for the single-machine multi-product lot scheduling problem. Management Science, 20(1), 50-55; Goyal, S. K. (1975). Scheduling a single-machine multi-product system: A new approach. International Journal of Production Research, 13, 487-493]; the differences and similarities between the methods are illustrated in a common numerical example. It shows that feasible solutions can be found, both with our method and others; where the production can be scheduled during a time interval, the initial inventory level is the same as the final and the schedule can be repeated in a cyclic pattern without shortages. (This definition of feasibility differs from traditional.) However, it shows that the common approximation for the inventory holding costs [.....] does not fit. The real inventory holding cost becomes different compared to the approximated that is used in the calculations. The real inventory holding cost depends on the chosen scheduling, which makes it difficult to find an optimal solution. Different solutions and extensions are discussed.

  • 5.
    Yu, Huan
    et al.
    School of Reliability and Systems Engineering, Beihang University.
    Yang, Jun
    School of Reliability and Systems Engineering, Beihang University.
    Lin, Jing (Janet)
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Zhao, Yu
    School of Reliability and Systems Engineering, Beihang University.
    Reliability evaluation of non-repairable phased-mission common bus systems with common cause failures2017In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 111, p. 445-457Article in journal (Refereed)
    Abstract [en]

    Phased-mission common bus (PMCB) systems are systems with a common bus structure, performing missions with consecutive and non-overlapping phases of operations. PMCB systems are found throughout industry, e.g., power generating systems, parallel computing systems, transportation systems, and are sometimes characterized by their common cause failures. Reliability evaluation of PMCB systems plays an important role in system design, operation, and maintenance. However, current studies have focused on either phased-mission systems or common bus systems because of their complexity. The challenge in practice is to consider phased-mission systems together with common bus structures and common cause failures. To solve this problem, we propose an evaluation algorithm for PMCB systems with common cause failures by coupling the structure function of a common bus performance sharing system and an existing recursive algorithm. To weigh the efficiency of the proposed algorithm, its complexity is discussed. To improve the reliability of PMCB systems, we adopt the genetic algorithm method to search for the optimal allocation strategies of the service elements. We use both analytical and numerical examples to illustrate the application of the proposed algorithm.

  • 6.
    Zanoni, Simone
    et al.
    University of Brescia.
    Segerstedt, Anders
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Tang, Ou
    Linköpings universitet.
    Mazzoldi, Laura
    University of Brescia.
    Multi-product economic lot scheduling problem with manufacturing and remanufacturing using a basic period policy2012In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 62, no 4, p. 1025-1033Article in journal (Refereed)
    Abstract [en]

    In this research we study the multi-product economic lot scheduling problem (ELSP) with manufacturing and remanufacturing opportunities.Manufacturing and remanufacturing operations are performed on the same production line. Both manufactured and remanufactured products have the same quality thus they fulfil the same demand stream.Tang and Teunter (2006) firstly studied this type of economic lot scheduling problem with returns (ELSPR) and presented a complex algorithm for the optimal solution.More recently Teunter et al (2009) proposedseveral heuristics to dealwith the same problem using more computational efficient approaches. However, both studies have limited the attention to the common cycle policy with the assumption that a single (re)manufacturing lot is used for each item in each cycle. Relaxing the constraint of common cycle time and a single (re)manufacturing lot for each item in each cycle, we propose a simple, easy to implementalgorithm, based on Segerstedt (1999), to solve the model using a basic period policy. Several numerical examples show the applicability of the algorithm and the cost savings.

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