Modelling and simulation
MetadataShow full item record
AbstractThis 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.
VersionNo full-text available in the repository
CitationLi, J-P., Thompson, G. (2005). Mechanical failure analysis in a virtual reality environment. Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering. Vol. 219, No. 3, pp. 237-250.
Link to publisher’s version10.1243/095440805X28258
Showing items related by title, author, creator and subject.
An approach to failure prediction in a cloud based environmentAdamu, 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.
The financial performance of small and medium sized companies: A model based on accountancy data is developed to predict the financial performance of small and medium sized companies.Betts, James; Earmia, Jalal Y. (University of BradfordPost-graduate School of Industrial Technology, 2009-09-08)This study is concerned with developing a model to identify small-medium U.K. companies at risk of financial failure up to five years in advance. The importance of small companies in an economy, the impact of their failures, and the lack of failure research with respect to . this population, provided justification for this study. The research was undertaken in two stages. The first stage included a detailed description and discussion of the nature and role of small business in the UK economy, heir relevance, problems and Government involvement in this sector, together with literature review and assessment of past research relevant to this study. The second stage was involved with construction of the models using multiple discriminant analysis, applied to published accountancy data for two groups of failed and nonfailed companies. The later stage was performed in three parts : (1) evaluating five discriminant models for each of five years prior to failure; (2) testing the performance of each of the .five models over time on data not used . in their construction; (3) testing the discriminant models on a validation sample. The purpose was to establish the "best" discriminant model. "Best" was determined according to classification ability of the model and interpretation of variables. Finally a model comprising seven financial ratios measuring four aspects of a company's financial profile, such as profitability, gearing, capital turnover and liquidity was chosen. The model has shown to be a valid tool for predicting companies' health up to five years in advance.
The Development of a Manufacturing Failure Mode Avoidance Framework for Aerospace ManufacturingCampean, I. Felician; Caunce, A.; Goodland, James (University of BradfordFaculty of Engineering and Informatics, 2016)In order to remain competitive in the global market businesses are under ever increasing pressure to ramp up production rates whilst simultaneously improving cost effectiveness to allow continued profitable growth. This requirement is particularly challenging in high value manufacturing which is characterised by expensive product and manufacturing systems and relatively low production volume. This thesis introduces a method for the design of robust and reliable manufacturing processes through the prevention of identified potential failure modes that is based on the principles of the existing Failure Mode Avoidance framework used for automotive system design. The tools and techniques that exist in the literature are reviewed in order to understand the best practice, and subsequently a Manufacturing Failure Mode Avoidance framework is designed. This framework is demonstrated through two unique case studies conducted in a real life manufacturing environment in order to validate its appropriateness to provide robust countermeasures to failure which will allow right first time manufacture. The outcomes of the implementations are discussed, conclusions drawn and opportunities for further research are provided.