BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Mechanical failure analysis in a virtual reality environment

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Publication date
    2009-07-20T10:49:43Z
    Author
    Li, Jian-Ping
    Thompson, Glen P.
    Keyword
    Mechanical failure
    Failure modelling
    Virtual reality
    Modelling and simulation
    Complex failure
    Visualization
    Failure modelling
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    This paper is part of a research theme to develop methods that enhance risk assessment studies by the use of 'automated' failure analysis. The paper presents an approach to mechanical failure analysis and introduces a mechanical failure analysis module that can be used in a virtual reality (VR) environment. The module is used to analyse and predict failures in mechanical assemblies; it considers stress related failures within components, as well as failures due to component interactions. Mechanical failures are divided into two categories in this paper: material failures and interference failures. The former occur in components and the latter happen at the interface between components. Individual component failures can be analysed readily; a contribution of the mechanical failure analysis module is to predict interference failures. A mechanical failure analysis system that analyses and visualizes mechanical failures in a virtual environment has been developed. Two case studies demonstrate how the system carries out failure analysis and visualization as design parameters are changed.
    URI
    http://hdl.handle.net/10454/3059
    Version
    No full-text available in the repository
    Citation
    Li J-P and Thompson GP (2005) Mechanical failure analysis in a virtual reality environment. Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering. 219(3): 237-250.
    Link to publisher’s version
    10.1243/095440805X28258
    Type
    Article
    Collections
    Engineering and Informatics Publications

    entitlement

     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      An approach to failure prediction in a cloud based environment

      Adamu, Hussaini; Bashir, Mohammed; Bukar, Ali M.; Cullen, Andrea J.; Awan, Irfan U. (2017)
      Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers’ data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.
    • Thumbnail

      Failure Analysis Modelling in an Infrastructure as a Service (Iaas) Environment

      Mohammed, Bashir; Modu, Babagana; Maiyama, Kabiru M.; Ugail, Hassan; Awan, Irfan U.; Kiran, Mariam (2018)
      Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average (ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good.
    • Thumbnail

      The Development of a Knowledge-Based Wax Deposition, Three Yield Stresses Model and Failure Mechanisms for Re-starting Petroleum Field Pipelines. Building on Chang and Boger’s Yield Stresses Model, Bidmus and Mehrotra’s Wax Deposition and Lee et al.’s Adhesive-Cohesive Failure Concepts to better Underpin Restart Operation of Waxy Crude Oil Pipelines

      Benkreira, Hadj; Fakroun, Abubaker A. (University of BradfordFaculty of Engineering and Informatics, 2017)
      Twenty years ago, Chang et al. (1998) introduced the three-yield stresses concept (dynamic, static and elastic limits) to describe yielding of waxy crude oils cooled below the wax appearance temperature (WAT). At the time, the limits in rheological instruments were such that they never actually measured the elastic-limit, a key fundamental property. Using modern instruments, this research succeeds in recording for the first time the entire yielding process down to stresses of 10-7 Pa and shear rate of 10-6 min-1 as a function of temperature, cooling rate and stress loading rate using two waxy oils of different origins and wax content. A four-yield stress model is established using derivative data (dynamic fluidity and failure acceleration). In addition, calorimetry (DSC) and microscopy (CPM) helped extract WAT, the gel and pour points and link gel crystal structure and its yielding and breakage to rheological properties. The yielding stresses measured rheologically were tested in laboratory pipelines at two diameter scales, 6.5mm and 13.5mm to compare stresses in uniform and non-uniform cooling. It is demonstrated that rheological instruments can only predict gel breaking pressure when the cooling rate is low, i.e. yielding at the pipe wall. A complementary heat transfer study was performed on a section of pipe statically cooled, both experimentally and theoretically to predict the gel front-liquid oil interface that develops in industrial pipeline where gel breaking occurs. This key information together with rheological data provide the means to predict accurately restart pressures of shut gelled pipelines that have eluded previous research.
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.