No description available.

M.Phil. / In an ever - increasing competitive business world it is essential to optimise the replacement of expensive mining equipment. The decisions regarding the replacement of assets used in a coal mine are usually based on life cycle costing models. Financial methods such as Net Present Value, Internal Rate of Return or Payback are applied to determine the feasibility of replacement of the asset. Whereas these methods and other models such as life cycle costing, challenger / defender and the Non-Homogeneous Poisson Process models can be applied in most cases, it is deemed to be insufficient as the sole decision making tool for the replacement of mining equipment. The development of another tool to assist in the decision making process is required for specific use by the engineer to be used in conjunction with the traditional financial models. Key performance indicators are used extensively in the mining industry to manage the performance of equipment and are deemed to be essential components in achieving the organisation's objectives. There are certain limitations when using only the traditional financial life cycle costing methods when viewed from the engineer's perspective, since they do not directly incorporate the level of the maintenance function and the performance effectiveness of the asset. The engineer usually requires more insight into the performance of the asset to assess the feasibility of replacement, hence the need for an additional tool that incorporates these key performance indicators. In most cases there are relationships between the various key performance indicators themselves as well as between them and the operating and maintenance cost of the asset. The key performance indicators used are availability, reliability (mean time to failure), maintainability (mean time to repair) and the operability (tons per direct operating hour). There are certain factors that may lead to the excessive operating and maintenance cost of an asset, especially if there is no investigation into the reasons for the excessive cost. Examples are sub-standard maintenance practices and an insufficient level of service from suppliers. Both are issues that can be resolved with a consequent decrease in the cost of ownership of the asset. Cost as the only indicator of the feasibility of replacement may therefore not be a true reflection of the real status of the performance of the asset. Weighting factors are used to allocate values to the key performance indicators in terms of their contribution towards achieving the organisational objectives. The equipment effectiveness is derived from these values to give an indication of how well the equipment is performing against predetermined benchmarks. This dissertation attempts tb find a solution to the problem through the use of the key performance indicators in addition to the existing models that focus on the financial aspect in order to provide a more accurate assessment of the replacement requirement of an asset in an opencast coal mine.

The methodology and the processes that are followed in the maintenance of government-operated district hospitals in the Eastern Cape Province of South Africa are outlined while the strategies that are employed to roll-out the maintenance plan have been investigated. The challenges in managing hospital maintenance are identified so that it can be improved and advice be given on the strategies currently being used. The objectives of the research were to determine the effectiveness of machinery maintenance in government-operated district hospitals. The study has been carried out by investigating 50 district hospitals within the Eastern Cape Province, with the aim of obtaining knowledge of practices in relation to the strategic plans and best practices in the maintenance industry. The following factors identified by the maintenance staff were found to be most important in maintenance management practices; realistic budgets, adherence to occupational health and safety regulations, engagement of qualified and skilled maintenance staff, good record management in relation to the equipment under maintenance, availability of tools and materials and keeping abreast with the latest technologies and trends of machinery advancement. The study also revealed that the majority of the hospitals use a combination of in-house and outsourced personnel during maintenance, the outsourced part being under contract from the National Department of Public Works Repair and Maintenance Plan (RAMP) projects. Service kits and recommended replacement parts for machinery are rarely stocked on the hospital premises while only minor spare parts that include replacement bulbs, electrical fittings, plumbing fittings and paint are readily available. In addition, the maintenance staff members have little or no input in maintenance budgeting, this aspect is controlled by hospital management. For maintenance to be effective, strategic planning that takes into account carefully thought-out maintenance management systems is the first step in the direction of setting out definite tangible objectives and goals. The real challenge lies in the implementation and sustainability of the maintenance management system and the monitoring thereof.

Master of Science

No description available.

We propose a mathematical framework that integrates low-level sensory signals from monitoring engineering systems and their components with high-level decision models for maintenance optimization. Our objective is to derive optimal adaptive maintenance strategies that capitalize on condition monitoring information to update maintenance actions based upon the current state of health of the system. We refer to this sensor-based decision methodology as "sense-and-respond logistics". As a first step, we develop and extend degradation models to compute and periodically update the remaining life distribution of fielded components using in situ degradation signals. Next, we integrate these sensory updated remaining life distributions with maintenance decision models to; (1) determine, in real-time, the optimal time to replace a component such that the lost opportunity costs due to early replacements are minimized and system utilization is increased, and (2) sequentially determine the optimal time to order a spare part such that inventory holding costs are minimized while preventing stock outs. Lastly, we integrate the proposed degradation model with Markov process models to derive structured replacement and spare parts ordering policies. In particular, we show that the optimal maintenance policy for our problem setting is a monotonically non-decreasing control limit type policy. We validate our methodology using real-world data from monitoring a piece of rotating machinery using vibration accelerometers. We also demonstrate that the proposed sense-and-respond decision methodology results in better decisions and reduced costs compared to other traditional approaches.

Este trabalho apresenta o desenvolvimento de uma metodologia de manutenção preditiva para motores elétricos, que utiliza a técnica de monitoramento de variáveis, transmissão de dados pela rede elétrica (Powerline Communication) e um tratamento com os métodos multicritério para classificação (ELECTRE TRI e AHPSort) e ordenação (ELECTRE II) dos motores que apresentam condição de falha incipiente, além do aproveitamento das instalações elétricas existentes para a aquisição de dados do funcionamento de motores elétricos tais como: tensão, corrente, temperatura e vibração. Essas informações podem ser avaliadas e tratadas através de métodos multicritério para alocar os motores em classes que representam estados de funcionamento normal, aceitável e falha incipiente. Assim, nos motores classificados em condição de falha incipiente pode ser realizado um ranking para apontar o motor em pior estado de funcionamento. Neste trabalho, foi conduzido um ensaio de falhas programadas em bancada com um período de aquisição de 1 minuto das variáveis de funcionamento de seis motores. Neste período, pode ser feito o registro do motor que apresentou as condições mais críticas para a falha, considerando as variáveis medidas como critérios na análise. Após um largo período de análise, foram computadas todas as vezes que cada motor foi classificado na classe de falha incipiente e ranqueado em primeiro lugar, significando que ele possui condições fora do padrão normal de funcionamento e piores que dos outros motores analisados. Com isso, foi possível identificar o motor que, por mais vezes, esteve em condição de falha incipiente, refletindo as falhas inseridas durante o ensaio. Essa metodologia possibilitou indicar ao gestor da manutenção, os desvios do funcionamento normal de motores elétricos, considerando mais de uma variável ao mesmo tempo, alinhado aos objetivos do decisor, através dos pesos calculados para os critérios, limites e preferências estabelecidas em cada método multicritério utilizado na metodologia proposta neste trabalho. / This work presents the development of a predictive maintenance methodology for electric motors, which uses the variable monitoring technique, data transmission through the electric network (Powerline Communication) and a treatment with multicriteria methods for sorting (ELECTRE TRI and AHPSort) and ranking (ELECTRE II) electric motors with incipient failure condition and the use of existing electrical installations for the acquisition of data of the operation of electric motors such as voltage, current, temperature and vibration. This information can be evaluated and treated through multicriteria methods to allocate motors in classes that represent normal, acceptable, and incipient failure states. Thus, in electric motors classified as incipient failure condition, a ranking can be performed to detect the engine in the worst operating state. In this work, a bench experiment was conducted with a 1-minute acquisition period of the operating variables in 6 motors. In this period, the electric motor can be registered that presented the most critical conditions for the fault, considering the measured variables as criteria in the analysis. After a longer period of analysis, we computed every time this engine was classified in the incipiente failure and first rank class, which means that it has conditions that are out of the normal operating range and worse than the other engines analyzed. Through this methodology it is possible to indicate to the maintenance manager deviations from the normal operation of electric motors, considering more than one variable at the same time aligned to the objectives of the decision maker, through the weights calculated for the criteria and limits and preferences established in each multicriteria method used in the methodology proposed in this work.

Submitted in fulfillment of the requirements for the degree of Master of Engineering, Durban University of Technology, Durban, South Africa, 2017. / Much equipment in the eThekwini Electricity network has been in use for several decades. Failure of this equipment could critically impact electricity supply to customers, and result in high costs associated with loss of load and/or component replacement. The fundamental motive for any power utility is to plan, operate, and maintain power infrastructure such that customers receive reliable electric services at the minimum expense possible. For this dissertation, the Reliability Centered Maintenance (RCM) model was implemented in the eThekwini Electricity network. This model emphasises the importance of long-term planning and allocation of resources over the life time of a transformer, or any other component. RCM is an ongoing process that entails gathering data from operating systems performance, and using this data to improve design, operation, and maintenance of the system. The eThekwini Power network failure statistics for the previous five years were collected and thoroughly analysed to identify critical components associated with higher failure rates, and associated consequences. Upon examination, it was determined that the power transformer is a critical component of the system. The transformer plays a significant role in the power system due to its remarkable effect on overall reliability, in addition to the fact that it is a major cost factor in the power grid. Transformer management comprises of identifying the appropriate type and frequency of maintenance, and the appropriate time to replace the transformer in a cost-effective manner. The Markov model for ascertaining the transformer’s remaining service life was applied on the identified critical transformer. The transformer deterioration process is modelled by representing the oil insulation by discrete stages. Using the Institute of Electrical and Electronics Engineers (IEEE) standard for interpreting the transformer insulation, the transformer under review was found to be at stage two. Further analysis was performed on system unavailability rates versus mean time to first failure (MTTFF). The analyses indicated that the higher the MTTFF, the longer the system availability whereas the lower the MTTFF, the more reduced the system availability. Improving the MTTFF rates of a system will enhance reliability. The effective application of RCM will optimise the maintenance processes with reasonable expenditures. / M

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.