M.Ing. (Engineering Management) / Please refer to full text to view abstract

M.Ing. / Until very recently reactive maintenance was still used in the mining industry. Equipment failures occurred without warning and resulted in catastrophic breakdowns and large production losses and maintenance cost. As a result, the mining industry turned to preventative maintenance that focused on changing parts before they fail. Although preventative maintenance was an improvement over reactive maintenance practices, equipment reliability did not necessarily improve. Next came predictive diagnostics, which monitored the health of components within assemblies, and thereby predicting the life expectancy of assemblies through vibration analysis, infrared thermography, lubrication and oil analysis and ultrasonic detection. However, the level of success could not be achieved. Many hours and a lot of money are spent in developing and implementing a maintenance management system, but without the correct approach, efficient maintenance might not be achieved. The overall objective of this dissertation is to introduce a logical approach to managing the maintenance of mining equipment over the economical life of the equipment. This concept can be defined as Life Cycle Management. The research included in this dissertation is partially aimed at developing the Life Cycle Management program for P&H MinePro Services A division of Joy Global (South Africa) (Pty) Ltd. The dissertation also includes case studies on the P&H Mining Equipment (Blast Hole Drills) and their alliance partners (LeTourneau Front-end Loaders). The dissertation focuses on the cradle to grave approach of maintenance for mining machinery, referred to as the Life Cycle Management of Mining Machinery.

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The purpose of this study was to ascertain the most economically feasible method for high volume machining of a discharge hole in a power metal compressor cylinder.

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This thesis is an attempt to develop a theoretical microeconomic model which can be used to examine the short-run demand for the timeliness of farm machinery repairs. This analysis focuses on the timing of the repair after a breakdown has occurred. The nonstochastic model developed allows the incorporation of the timing of the repair as a variable input into a production function. A yield function (a function which gives the instantaneous rate of output in bushels per acre as a function of the date of harvest) is used in deriving this production function. From the production function a demand curve for the timeliness of repairs can be derived. A constrained input demand curve (CIDC) is used to examine the demand for timely repairs. A specific functional form of the yield function is used in order to allow an easier examination of how various parameters affect the CIDC. Several testable hypotheses which result from the model are presented. An attempted test of one of the hypotheses is discussed. / Graduation date: 1971

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M.Ing. (Engineering Management) / Please refer to full text to view abstract

Maintenance is constantly challenged with increasing productivity by maximizing up-time and reliability while at the same time reducing expenditure and investment. In the last few years it has become evident through the development of maintenance concepts that maintenance is more than just a non-productive support function, it is a profit- generating function. In the past decades, hundreds of models that address maintenance strategy have been presented. The vast majority of those models rely purely on mathematical modeling to describe the maintenance function. Due to the complex nature of the maintenance function, and its complex interaction with other functions, it is almost impossible to accurately model maintenance using mathematical modeling without sacrificing accuracy and validity with unfeasible simplifications and assumptions. Analysis presented as part of this thesis shows that stochastic simulation offers a viable alternative and a powerful technique for tackling maintenance problems. Stochastic simulation is a method of modeling a system or process (on a computer) based on random events generated by the software so that system performance can be evaluated without experimenting or interfering with the actual system. The methodology developed as part of this thesis addresses most of the shortcomings found in literature, specifically by allowing the modeling of most of the complexities of an advanced maintenance system, such as one that is employed in the airline industry. This technique also allows sensitivity analysis to be carried out resulting in an understanding of how critical variables may affect the maintenance and asset management decision-making process. In many heavy industries (e.g. airline maintenance) where high utilization is essential for the success of the organization, subsystems are often of a rotable nature, i.e. they rotate among different systems throughout their life-cycle. This causes a system to be composed of a number of subsystems of different ages, and therefore different reliability characteristics. This makes it difficult for analysts to estimate its reliability behavior, and therefore may result in a less-than-optimal maintenance plan. Traditional reliability models are based on detailed statistical analysis of individual component failures. For complex machinery, especially involving many rotable parts, such analyses are difficult and time consuming. In this work, a model is proposed that combines the well-established Weibull method with discrete simulation to estimate the reliability of complex machinery with rotable subsystems or modules. Each module is characterized by an empirically derived failure distribution. The simulation model consists of a number of stages including operational up-time, maintenance down-time and a user-interface allowing decisions on maintenance and replacement strategies as well as inventory levels and logistics. This enables the optimization of a maintenance plan by comparing different maintenance and removal policies using the Cost per Unit Time (CPUT) measure as the decision variable. Five different removal strategies were tested. These include: On-failure replacements, block replacements, time-based replacements, condition-based replacements and a combination of time-based and condition-based strategies. Initial analyses performed on aircraft gas-turbine data yielded an optimal combination of modules out of a pool of multiple spares, resulting in an increased machine up-time of 16%. In addition, it was shown that condition-based replacement is a cost-effective strategy; however, it was noted that the combination of time and condition-based strategy can produce slightly better results. Furthermore, a sensitivity analysis was performed to optimize decision variables (module soft-time), and to provide an insight to the level of accuracy with which it has to be estimated. It is imperative as part of the overall reliability and life-cycle cost program to focus not only on reducing levels of unplanned (i.e. breakdown) maintenance through preventive and predictive maintenance tasks, but also optimizing inventory of spare parts management, sometimes called float hardware. It is well known that the unavailability of a spare part may result in loss of revenue, which is associated with an increase in system downtime. On the other hand increasing the number of spares will lead to an increase in capital investment and holding cost. The results obtained from the simulation model were used in a discounted NPV (Net Present Value) analysis to determine the optimal number of spare engines. The benefits of this methodology are that it is capable of providing reliability trends and forecasts in a short time frame and based on available data. In addition, it takes into account the rotable nature of many components by tracking the life and service history of individual parts and allowing the user to simulate different combinations of rotables, operating scenarios, and replacement strategies. It is also capable of optimizing stock and spares levels as well as other related key parameters like the average waiting time, unavailability cost, and the number of maintenance events that result in extensive durations due to the unavailability of spare parts. Importantly, as more data becomes available or as greater accuracy is demanded, the model or database can be updated or expanded, thereby approaching the results obtainable by pure statistical reliability analysis.