An algorithm is developed to determine the optimal component replacement rules to follow in managing a particular class of equipment. The work follows basically the models developed previously by S.E. Dreyfus and R.A. Howard. However, a different Markov state description has been used to extend the application of these models to systems of more than one component subject to stochastic failure and for which the failure of any component renders the entire system inoperative. The model, in effect, selects optimal replacement alternatives as individual components fail, under the uncertainty of further failures occurring in the same transition interval. The model was programmed for an I.B.M. 360/67 computer and the results for a hypothetical problem were checked through renewal theory. / Forestry, Faculty of / Graduate

This thesis contends that equipment replacement analysis should consider the implications of technological change since a like for like replacement is unrealistic with the current state of technological change. The major effects of improvements are a decrease in salvage value and opportunity expense of not implementing the latest innovations. The improvements appear as gradual changes and as major breakthroughs. Technological forecasting may be accomplished by several different methods including the Delphi method, analogy, and trend interpolation. A discussion of these methods and sources of information are given. The replacement model uses a future worth analysis, continuous discounting, discrete cash flows, a range of planning horizons, and incorporation of tax effects. The model was implemented using a BASIC program with graphics capability. The inputs are current year, initial cost of the equipment, the first years operating cost, regular tax rate, capital gains tax rate, depreciable life (as defined by the ERTA), rates of gradual change, number, dates and effect of major breakthroughs. A discussion of the sensitivity of the model to the various inputs is also given. / Master of Science

There is a world-wide drive to optimize maintenance decisions in an increasingly competitive manufacturing industry. Preventive maintenance if often the most organized and cost efficient strategy to follow, but a decision still has to be made on the optimal instant to perform preventive maintenance. Use based preventive maintenance decisions have been optimized through statistical analysis of failure date while predictive preventive maintenance (condition monitoring) has been optimized by utilizing more sophisticated technology. Very little work has however been done to combine the advantages of the two schools of thought. This thesis originated from a realization of the potential improvement in maintenance practice by combining use based preventive maintenance optimization techniques with high technology condition monitoring. In this thesis an approach is developed to estimate residual life of industrial equipment dynamically by combining statistical failure analysis and sophisticated condition monitoring technology. The approach is based on failure intensity proportions determined from historic survival time information and corresponding diagnostic information such as condition monitoring. Combined Proportional Intensity Models (PIMs) for non-repairable and repairable systems, containing the majority of conventional PIM enhancements as special cases, with numerical optimization techniques to solve for the regression coefficients, are derived. In addition to the residual life estimation approach, a user-friendly graphical method with which residual life estimates can be presented was also developed. This method is natural and easy to comprehend, even by inexperienced data analysts. The residual life estimation approach is applied to a typical data set from a South African industry and results are compared to those obtained from a similar, established maintenance decision support tool. This comparison showed that the approach developed in this thesis is relevant, practical and marginally better than the established decision support tool for certain criteria. / Thesis (PhD (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

Design of a repairable item and its logistic support system requires consideration of several interrelated decision problems. These decision problems concern the variables, controllable by the design engineer and/or system manager, which affect system performance. This research develops a framework for integration of these decision problems and evaluation of system design tradeoffs. These design decision problems are represented in the model base of a decision support system (DSS). Interrelationships between decision problems are defined using data flow diagrams. Data flows within and between these decision problems are integrated in the DSS database. A simulation capability, imbedded into the DSS permits short-term, accelerated time excursions into possible futures for decision-making purposes. Alternative system designs are evaluated using a multicriteria decision model which considers reliability, maintainability, availability, and life cycle costs. The logistic support system is modeled as a multilevel inventory system. These inventories include spare repairable items, spare parts, labor, maintenance equipment, and other support resources. Repairable item and logistic support system design decision problems affect the quantity and location of these inventories. Five decision problems identified by Moore [1986] were selected to demonstrate the utility of this framework. The selected decision problems are: 1) the equipment design problem; 2) the maintenance configuration problem; 3) the spare equipment problem; 4) the level of repair problem; and 5) the replacement policy problem. The framework developed supports integration of these decision problems throughout the item’s life cycle. A repairable item can be systematically divided into subelements until individual repairable components are identified. This systematic subdivision of the item produces an inverted, tree-like structure. This structure is used as the representational view of the DSS database. As the life cycle progresses and the item design becomes more detailed, the structure expands. The DSS database is designed to accommodate this expansion so that the framework can be used throughout the item’s life cycle. The initial fielding and the retirement of the repairable item population produces nonstationary demands on the logistics support system. A multistream model captures the nonstationary aspects of demand, eliminating the need for item-by-item tracking within the model. The framework developed is illustrated using a comprehensive case study. The case study addresses the design of a Side Loadable Warping Tug (SLWT) and its logistics support system. A population of SLWT’s must be deployed to meet demands in two different operating environments. The SLWT is a component of the U.S. Navy’s Container Offloading and Transfer System (COTS). / Ph. D.

published_or_final_version / Industrial Engineering / Master / Master of Science in Engineering

by Chu Yu Lun, Stanley [and] Kwong Kin Hing, Edmund. / Includes bibliographies / Thesis (M.B.A.)--Chinese University of Hong Kong, 1984

Industry and society are now aware of risk more than ever before. Organisations whose activities pose risk to individuals and society are accountable to manage and reduce risk to an acceptable level. In this regard, utilisation of Safety Instrumented Systems (SISs) as an independent protection layer is a practical method of achieving the required risk reduction. The role of a SIS is to maintain the safety of equipment under control by providing a safety-related function. The International Standards, IEC 61508/61511, provide a set of guidelines to promote consistency for implementation of SISs used for risk reduction. In accordance with IEC 61508, the performance of a SIS shall be established by computing the associated Probability of Failure on Demand (PFD) as a reliability measure using a suitable technique. The principal purpose of this research is to provide the basis for reliability assessment of redundant SISs affected by process demand as well as component failures. This dissertation introduces four new reliability models for redundant SISs subject to process demand for the first time using Markov analysis technique. The proposed reliability models 1 and 3 incorporates process demands in conjunction with Common Cause Failure (CCF) and evaluates their impacts on the reliability quantification of 1oo2 redundant configuration using different repair philosophies. In model 3, the proposed Markov model was also compared with the IEC 61508 approach for redundant SISs and a reliability improvement between 9% - 15% were observed. The model 2 on the other hand integrates the Dangerous Detected (DD) failure rates in the unavailability assessment of redundant SISs subject to process demand assuming that CCF does not occur. An additional reliability model was developed in this research for a 1oo3 redundant configuration subject to process demand excluding CCF and its construction was verified using partial verification method. Furthermore, a generic framework for reliability assessment of 1oon redundant SISs is provided in this thesis in conjunction with some guidelines for future researchers as how to conduct reliability assessment of SISs subject to process demands. The accuracy of the proposed Markov models is verified for industrial application case studies. It is demonstrated that the proposed approach provides a sufficiently robust result for all demand rates, demand durations, common cause failures, dangerous detected and undetected failure and associated repair rates for SISs. The effectiveness of the proposed models offers a robust opportunity to conduct reliability assessment of redundant SISs subject to process demands.

No description available.

No description available.

Abrasive wear occurs when a contact associated with stress between a metal surface and a herd particle (frequently of mineral origin) leads to friction between the two. In a very wide range of industrial applications, abrasive wear is the main reason for component and equipment repair or replacement. In most of these applications, especially those of earth moving, construction and mining equipment, digger teeth are used to improve equipment performance. Digger teeth can be produced in different shapes and sizes (mainly by casting) and a wide range of materials are used. This project is concerned with both a field trial of the wear of digger teeth fixed to the front of a bucket used in a gravel pit, and also a laboratory investigation of abrasive wear mechanisms. It was found that the wear of digger teeth increased with increasing working hours, but the wear rate eventually decreased. The dimensions and shape of the front of the tooth changed and gravel removal became more inefficient. Plastic deformation and phase transformation were observed in the worn surfaces of the teeth. In the laboratory study, many parameters were investigated utilising a pin-on disc technique. Wear rate increases linearly with load and decreases with sliding distance. The effect of attack angle on abrasive wear showed that wear volume increases with increasing attack angle up to a certain value (90°) and then decreases. Corrosion increases the initial wear rate, and the amount of material removed in the wet corrosive test was higher than the corresponding dry test. It was difficult to reproduce the same results from the field trial in the laboratory because of the difference in the conditions in the two cases. Optical and scanning electron microscopy were used to study the worn surfaces, abrasive papers and wear debris. Different abrasive wear mechanisms were observed throughout this investigation. A cutting mechanism associated with spiral debris was observed during short pin-on disc tests and with higher attack angles. A ploughing action associated with plate-like debris was observed during longer tests and at lower attack angles. Fragmentation was observed in brittle materials.