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Corrective Maintenance (corrective + maintenance)
Selected AbstractsA fuzzy-based multimodel system for reasoning about the number of software defectsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2005Marek Reformat Software maintenance engineers need tools to support their work. To make such tools relevant, they should provide engineers with quantitative input, as well as the knowledge needed to understand factors influencing maintenance activities. This article proposes an approach leading to multitechnique knowledge extraction and development of a comprehensive meta-model prediction system in the area of corrective maintenance. It dwells on elements of evidence theory and a number of fuzzy-based models. The models are developed using an evolutionary-based approach with different objectives applied to different subsets of data. Evidence theory,based Transferable Belief Model and belief function values assigned to generated models are used for reasoning purposes. The study comprises a detailed case for estimating the number of defects in a medical imaging system. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1093,1115, 2005. [source] Problem management maturity within corrective maintenanceJOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 3 2002Mira Kajko-Mattsson Abstract CM: Problem Management is a first detailed descriptive problem management process model to be utilized within corrective maintenance. It is the result of a long-term empirical study of industrial corrective maintenance processes. It has been developed at ABB and evaluated for its industrial relevance within 17 non-ABB organizations. Playing the role of a descriptive model, CM: Problem Management specifies what a problem management process should look like. It also structures it into three maturity levels, Initial, Defined, and Optimal, where each level offers a different grainedness of process visibility. In this paper, we present the CM levels of problem management process maturity within corrective maintenance and match them against the industrial state of practice. Our goal is to establish the current status of problem management maturity using CM: Problem Management as an evaluation model. Our evaluation results show that the industrial processes today suffice to attend to software problems within corrective maintenance. Very few of them, however, do learn from the past in order to prevent future problems and to improve development or maintenance processes. Copyright © 2002 John Wiley & Sons, Ltd. [source] Modelling fault-proneness statistically over a sequence of releases: a case studyJOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 3 2001Magnus C. Ohlsson Abstract Many of today's software systems evolve through a series of releases that add new functionality and features, in addition to the results of corrective maintenance. As the systems evolve over time it is necessary to keep track of and manage their problematic components. Our focus is to track system evolution and to react before the systems become difficult to maintain. To do the tracking, we use a method based on a selection of statistical techniques. In the case study we report here that had historical data available primarily on corrective maintenance, we apply the method to four releases of a system consisting of 130 components. In each release, components are classified as fault-prone if the number of defect reports written against them are above a certain threshold. The outcome from the case study shows stabilizing principal components over the releases, and classification trees with lower thresholds in their decision nodes. Also, the variables used in the classification trees' decision nodes are related to changes in the same files. The discriminant functions use more variables than the classification trees and are more difficult to interpret. Box plots highlight the findings from the other analyses. The results show that for a context of corrective maintenance, principal components analysis together with classification trees are good descriptors for tracking software evolution. Copyright © 2001 John Wiley & Sons, Ltd. [source] An optimal maintenance policy based on generalized stochastic Petri nets and periodic inspectionASIAN JOURNAL OF CONTROL, Issue 3 2010Ching-Kao Chang Abstract Periodic maintenance of equipment is essential for its optimum performance, thereby enabling production efficiency. In the past, studies on preventive maintenance of automated manufacturing systems (AMS) determined the optimal preventive maintenance policy under different performance indexes. Generally, most hypotheses indicate that equipment reliability can be restored to 1.0 through preventive and corrective maintenance. However, in practical application, the implementation of preventive maintenance results in partial deterioration of equipment; moreover, the reliability of equipment cannot be restored to as-good-as-new. In addition, the greater the complexity of connections of the equipment, the greater is the difficulty in determining the timing for preventive maintenance. On account of these characteristics, generalized stochastic Petri nets (GSPN) are well-suited for the implementation of preventive maintenance. Therefore, this paper applies GSPN for deciding the optimal maintenance policy and constructing models for different levels of maintenance and renewal for an AMS with a serial-parallel layout. As a result of the application of GSPN, the following optimal maintenance policy for an AMS was obtained in this study: Preventive maintenance conducted at intervals of every 240 hours will reduce cost by 46% as opposed to the practice of replacing defective parts when necessary. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | ||||||||||