Reliability monitoring techniques applied to a hot strip steel mill

Reliability engineering techniques have been used in the manufacturing environment for many years. However the reliability analysis of repairable systems is not so widely practised in the steel manufacturing environment. Many different analysis methods have been proposed for the modelling of repairable systems, most of these have had limited application in the manufacturing environment. The current reliability analysis techniques are predominantly used by engineers to construct a “snapshot” in time of a manufacturing system’s reliability status. There are no readily identifiable applications of reliability modelling techniques being applied to repairable systems over a long time period within the manufacturing environment The aim of this work is to construct a method which can analyse and monitor the reliability status of multiple repairable systems within the steel plant over an extended operating period. The developed analysis method is predominantly automated and is facilitated by applying standard reliability analysis techniques to all of the repairable systems failure data sets under review. This Thesis illuminates the methodology used to fulfil the remit of this research by the following sequential steps: Developing a new methodology for the application of reliability analysis techniques to repairable systems within a steel manufacturing facility Utilised an innovative step of combining three reliability analysis methods as complimentary activities Constructed an automated reliability analysis model which fulfils the project remit. In addition the model is capable of the long term monitoring of repairable system reliability The new reliability analysis method has been delivered to Tata Steel and is installed in the Port Talbot Technology Group with a direct link to the Hot Strip Mill (HSM) monitoring database. This reliability analysis method has been tested with four years operational data from the Hot Strip Mill manufacturing area and the analysis has shown that changes and trends in all systems reliability status can be easily identified.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bi-Directional Testing for Change Point Detection in Poisson Processes

Point processes often serve as a natural language to chronicle an event\u27s temporal evolution, and significant changes in the flow, synonymous with non-stationarity, are usually triggered by assignable and frequently preventable causes, often heralding devastating ramifications. Examples include amplified restlessness of a volcano, increased frequencies of airplane crashes, hurricanes, mining mishaps, among others. Guessing these time points of changes, therefore, merits utmost care. Switching the way time traditionally propagates, we posit a new genre of bidirectional tests which, despite a frugal construct, prove to be exceedingly efficient in culling out non-stationarity under a wide spectrum of environments. A journey surveying a lavish class of intensities, ranging from the tralatitious power laws to the deucedly germane rough steps, tracks the established unidirectional forward and backward test\u27s evolution into a p-value induced dual bidirectional test, the best member of the proffered category. Niched within a hospitable Poissonian framework, this dissertation, through a prudent harnessing of the bidirectional category\u27s classification prowess, incites a refreshing alternative to estimating changes plaguing a soporific flow, by conducting a sequence of tests. Validation tools, predominantly graphical, rid the structure of forbidding technicalities, aggrandizing the swath of applicability. Extensive simulations, conducted especially under hostile premises of hard non-stationarity detection, document minimal estimation error and reveal the algorithm\u27s obstinate versatility at its most unerring

Reliability monitoring techniques applied to a hot strip steel mill

Reliability engineering techniques have been used in the manufacturing environment for many years. However the reliability analysis of repairable systems is not so widely practised in the steel manufacturing environment. Many different analysis methods have been proposed for the modelling of repairable systems, most of these have had limited application in the manufacturing environment. The current reliability analysis techniques are predominantly used by engineers to construct a “snapshot” in time of a manufacturing system’s reliability status. There are no readily identifiable applications of reliability modelling techniques being applied to repairable systems over a long time period within the manufacturing environment The aim of this work is to construct a method which can analyse and monitor the reliability status of multiple repairable systems within the steel plant over an extended operating period. The developed analysis method is predominantly automated and is facilitated by applying standard reliability analysis techniques to all of the repairable systems failure data sets under review. This Thesis illuminates the methodology used to fulfil the remit of this research by the following sequential steps: Developing a new methodology for the application of reliability analysis techniques to repairable systems within a steel manufacturing facility Utilised an innovative step of combining three reliability analysis methods as complimentary activities Constructed an automated reliability analysis model which fulfils the project remit. In addition the model is capable of the long term monitoring of repairable system reliability The new reliability analysis method has been delivered to Tata Steel and is installed in the Port Talbot Technology Group with a direct link to the Hot Strip Mill (HSM) monitoring database. This reliability analysis method has been tested with four years operational data from the Hot Strip Mill manufacturing area and the analysis has shown that changes and trends in all systems reliability status can be easily identified

Study of the Reliability Enhancement of Wind Turbines Employing Direct-drive Technology

In traditional wind turbines employing gearboxes, the blades spin a shaft that is connected through a gearbox to the generator. The multiple wheels and bearings in a gearbox are subjected to severe stresses because of wind turbulence and any defect/failure in a single component of the gear system can bring the wind turbine to a halt. The main hypothesis in this work is that the typical generator- gear solution in the wind industry can be replaced by a low speed permanent magnet generator using direct drive wind turbines. In this thesis, development of direct-drive wind energy systems is reviewed where the gearbox is completely eliminated. This work discusses the failure rates and downtime of the subassemblies in a wind turbine and evaluates the contribution of the gearbox towards the same. Analysis in terms of estimated parameters is performed to assess the improvement in reliability obtained with direct drive turbines. Weight and economic comparisons are also discussed briefly for the direct drive and geared turbines.School of Electrical & Computer Engineerin

Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science

Methods for dependability analysis of small satellite missions

The use of small-satellites as platforms for fast-access to space with relatively low cost has increased in the last years. In particular, many universities in the world have now permanent hands-on education programs based on CubeSats. These small and cheap platforms are becoming more and more attractive also for other-than-educational missions, such as for example technology demonstration, science application, and Earth observation. This new objectives require the development of adequate technology to increase CubeSat performances. Furthermore, it is necessary to improve mission reliability. The research aims at studying methods for dependability analysis conducted by small satellites. The attention is focused on the reliability, as main attribute of the dependability, of CubeSats and CubeSats missions. The work has been structured in three main blocks. The first part of the work has been dedicated to the general study of dependability from the theoretical point of view. It has been studied the dependability attributes, the threads that can affect the dependability of a system, the techniques that are used to mitigate the threads, parameters to measure dependability, and models and techniques for dependability modelling. The second part contains a study of failures occurred during CubeSats missions in the last ten years and their observed reliability evaluation have been conducted. In order to perform this analysis a database has been created. This database contents information of all CubeSats launched until December 2013. The information has been gathered from public sources (i.e. CubeSat projects webs, publications on international journals, etc.) and contains general information (e.g. launch date, objectives) and data regarding possible failures. All this information is then used to conduct a quantitative reliability analysis of these missions by means of non-parametric and parametric methods, demonstrating that these failures follow a Weibull distribution. In the third section different methods, based on the concept of fault prevention, removal and tolerance, have been proposed in order to evaluate and increase dependability, and concretely reliability, of CubeSats and their missions. Concretely, three different methods have been developed: 1) after an analysis of the activities conducted by CubeSat’s developers during whole CubeSat life-cycle, it has been proposed a wide range of activities to be conducted during all phases of satellite’s life-cycle to increase mission rate of success, 2) increase reliability through CubeSats verification, mainly tailoring international ECSS standards to be applied to a CubeSat project, 3) reliability rising at mission level by means of implementing distributed mission architectures instead of classical monolithic architectures. All these methods developed in the present PhD research have been applied to a real space projects under development at Politecnico di Torino within e-st@r program. The e-st@r program is being conducted by the CubeSat Team of the Mechanical and AeroSpace Engineering Department. Concretely, e-st@r-I, e-st@r-II, and 3STAR CubeSats have been used as test cases for the proposed methods. Moreover, part of the present research has been conducted within an internship at the European Space research and Technology Centre (ESTEC) of the European Space Agency (ESA) at Noordwijk (The Netherlands). In particular, the partially realisation of the CubeSats database, the analysis of activities conducted by CubeSat developers and statement of activities for mission rate of success increase have been conducted during the internship

Guidelines for Analysis of Data Related to Ageing of Nuclear Power Plant Components and Systems

This guideline is intended to provide practical methods for practitioners to use in analyzing component and system reliability data, with a focus on detection and modeling of ageing. The emphasis is on frequentist and Bayesian approaches, implemented with MS EXCEL and the open-source software package WinBUGS. The methods described in this document can be implemented with other software packages.JRC.F.5-Safety of present nuclear reactor

Endless Data

Small and Medium Enterprises (SMEs), as well as micro teams, face an uphill task when delivering software to the Cloud. While rapid release methods such as Continuous Delivery can speed up the delivery cycle: software quality, application uptime and information management remain key concerns. This work looks at four aspects of software delivery: crowdsourced testing, Cloud outage modelling, collaborative chat discourse modelling, and collaborative chat discourse segmentation. For each aspect, we consider business related questions around how to improve software quality and gain more significant insights into collaborative data while respecting the rapid release paradigm