8,542 research outputs found

    Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

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    Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

    Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

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    This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme

    Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

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    BACKGROUND: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. METHODS: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. RESULTS: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. CONCLUSION: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic

    Virtual series-system models of imperfect repair

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    Novel models of imperfect repair are fitted to classic reliability datasets. The models suppose that a virtual system comprises a component and a remainder in series. On failure of the component, the component is renewed, and on failure of the remainder, the component is renewed and the remainder is minimally repaired. It follows that the repair process is a counting process that is the superposition of a renewal process and a Poisson process. The repair effect, that is, the extent to the system is repaired by renewal of the component, depends on the relative intensities of the superposed processes. The repair effect may be negative, when the intensity of the part that is a renewal process is a decreasing function. Other special cases of the model exist (renewal process, Poisson process, superposed renewal process and homogeneous Poisson process). Model fit is important because the nature of the model and corresponding parameter values determine the effectiveness of maintenance, which we also consider. A cost-minimizing repair policy may be determined provided the cost of preventive-repair is less than the cost of corrective-repair and the repairable part is ageing. If the remainder is ageing, then policy needs to be adapted as it ages

    A geometric-process maintenance model for a deteriorating system under a random environment

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    This paper studies a geometric-process maintenance-model for a deteriorating system under a random environment. Assume that the number of random shocks, up to time t, produced by the random environment forms a counting process. Whenever a random shock arrives, the system operating time is reduced. The successive reductions in the system operating time are statistically independent and identically distributed random variables. Assume that the consecutive repair times of the system after failures, form an increasing geometric process; under the condition that the system suffers no random shock, the successive operating times of the system after repairs constitute a decreasing geometric process. A replacement policy N, by which the system is replaced at the time of the failure N, is adopted. An explicit expression for the average cost rate (long-run average cost per unit time) is derived. Then, an optimal replacement policy is determined analytically. As a particular case, a compound Poisson process model is also studied.published_or_final_versio

    Derivation of a cost model to aid management of CNC machine tool accuracy maintenance

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    Manufacturing industries strive to produce improved component accuracy while not reducing machine tool availability or production throughput. The accuracy of CNC production machines is one of the critical factors in determining the quality of these components. Maintaining the capability of the machine to produce in-tolerance parts can be approached in one of two ways: run to failure or periodic calibration and monitoring. The problem is analogous to general machine tool maintenance, but with the clear distinction that the failure mode of general machine tool components results in a loss of production, whereas that of accuracy allows parts to be produced, which are only later detected as non-conforming as part of the quality control processes. This distinction creates problems of cost-justification, since at this point in the manufacturing chain, any responsibility of the machine is not directly evident. Studies in the field of maintenance have resulted in cost calculations for the downtime associated with machine failure. This paper addresses the analogous, unanswered problem of maintaining the accuracy of CNC machine tools. A mathematical cost function is derived that can form the basis of a strategy for either running until non-conforming parts are detected or scheduling predictive CNC machine tool calibrations. This is sufficiently generic that it can consider that this decision will be based upon different scales of production, different values of components etc. Therefore, the model is broken down to a level where these variables for the different inputs can be tailored to the individual manufacturer

    On the Number of Maintenance Cycles in Systems with Critical and Non-Critical Components

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    We present a novel mathematical framework for computing the number of maintenance cycles in a system with critical and non-critical components, where "critical" (CR) means that the component's failure is fatal for the system's operation and renders any more repairs inapplicable, whereas "noncritical" (NC) means that the component can undergo corrective maintenance (replacement or minimal repair) whenever it fails, provided that the CR component is still in operation. Whenever the NC component fails, the CR component can optionally be preventively replaced. We extend traditional renewal theory (whether classical or generalized) for various maintenance scenarios for a system composed of one CR and one NC component in order to compute the average number of renewals of NC under the restriction ("bound") necessitated by CR. We also develop approximations in closed form for the proposed "bounded" renewal functions. We validate our formulas by simulations on a variety of component lifetime distributions, including actual lifetime distributions of wind turbine components.Comment: submitted to IEEE Transactions, in pres
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