Dissertations / Theses on the topic 'Mining machinery - Maintenance and repair'

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.

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.

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.

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.

Thesis (MBA)--Stellenbosch University, 2004.
ENGLISH ABSTRACT: Richards Bay Minerals (RBM) went through a restructuring process in 2002. The effect that these changes had on the life expectancy of the heavy earthmoving equipment (HEME) at the company’s sand dune mining operation situated at Richard’s Bay on the north coast of KwaZulu-Natal, was unknown. This study explores these, and various other factors, that influence the life expectancy of the HEME in this highly corrosive environment. During the last number of years there has been no equipment replacement philosophy at RBM and this has resulted in the average age of HEME at the mine being extremely high. This study suggests that when restructuring is implemented in the wrong way, the process can have significant negative implications. The study determines how the behaviour of especially drivers and maintenance personnel (maintainers) impacts on the expected life of the HEME at RBM. The hidden cost element of driver and maintainer unhappiness, however, is not included as part of this study. In the first part of this study project, the writer focuses on what may have caused the unhappiness, and makes suggestions for further studies aimed at addressing this unhappiness and changing the behaviour of drivers and maintainers. These suggestions should be seen as guidelines, as measures that work in one company may not necessarily work in another. The second part of the study project deals with the replacement philosophy regarding HEME at RBM. The mine has various types of equipment, but the main focus of this research falls on the following: Caterpillar 966- 980 (payloaders), track type equipment Caterpillar D7R/H (dozers), Caterpillar 988- 992 (heavy duty payloaders) and Bell B25 articulated dumptrucks (dumptrucks) Four different replacement models are investigated and discussed in detail. The model that was best aligned with the Caterpillar proposed best replacement practice, as stipulated by the Caterpillar replacement expert, is identified. Finally, a replacement cycle for the different types of equipment is proposed.
AFRIKAANSE OPSOMMING: Richardsbaai Minerale (RBM) het in 2002 deur ‘n herstruktureringsprogram gegaan. Die uitwerking wat die gevolge daarvan op die swaar grondverskuiwingsvoertuie (SGV) gehad het was onbekend. Gedurende die laaste aantal jare was daar geen vervangingsbeleid by RBM nie, en dit het meegebring dat die gemiddelde ouderdom van die SGV op die myn buitengewoon hoog is. Dit is belangrik om te bepaal wat die invloed van menslike gedrag van veral die drywers en herstelpersoneel op die verwagte leeftyd van SGV is. Die versteekte koste wat verbind word met ongelukkige drywers en herstelpersoneel maak nie deel uit van hierdie studie nie. As herstrukturering op die verkeerde manier toegepas word kan dit ‘n negatiewe impak hê. In die studie sal daar aandag gegee word aan wat die negatiewe elemente is en wat is die moontlike oorsaak daarvan. Daar word ook sekere voorstelle gemaak vir toekomstige studies om die ongelukkigheid aan te spreek en ‘n gedragsverandering na vore te bring. Die voorstelle is slegs riglyne en, wat in een maatskappy werk sal nie noodwendig in ‘n ander een werk nie. Die tweede deel van die werkstuk fokus op vervangingsteorieë met betrekking tot die SGV op RBM. Die myn het verskeie tipes SGVs maar die fokus het geval op vier soorte SGV. Hulle is Caterpillar 966- Caterpillar 980 (gemiddelde laaiers), spoor tipe toerusting Caterpillar D7R/H (stootskrapers), Caterpillar 988- Caterpillar 992 (swaargewig mobiele laaiers) en Bell B25 geartikuleerde stortings trokke (stortings trokke). Vier verskillende modelle is ondersoek en deeglik bespreek. Die model met die beste uitslae wat die mas opgekom het ten opsigte van die Caterpillar se voorgestelde beste vervangings praktyk, soos aangedui deur die Caterpillar voertuig vervangings kenner, is ge-identifiseer. Laastens word die vervangingsiklus vir die verskillende tipes toerusting voorgestel.

This thesis is focused on analysis of production facilities utilization and maintenance in the company RAMET C.H.M. a.s. located in Kunovice. In this thesis, there is performed the analysis of present property condition, machinery and equipment used within radar antenna production. Within property maintenance, there are introduced planned and unplanned processes of servicing activities and machine and equipment repair. In a part of proposal for solution, there are recommended processes for keeping up production capabilities based on extensive analysis of size property influence on production volume. The method for reliability estimation of production chain is described for the antenna production, related to limiting production capability and costs to maintain service availability of production facilities.

Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiques
In the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets

This Master thesis deals with the management of long-term tangible property in the company Česko-slezská výrobní, a. s., which is engaged in manufacturing of portable and sanitary cabins. The thesis is divided into three parts. The first part describes the theoretical background, which forms the theoretical base of the whole thesis. The second part is focused on analysis of the current situation of long-term tangible property in the company and property maintenance. The result of the analysis is identification of weaknesses and opportunities for development. In the last part are proposed solution including economic evaluation and time-consuming.

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.

Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná
Este trabalho tem como objetivo desenvolver uma abordagem para auxiliar as montadoras na obtenção de informações sobre os problemas e falhas que ocorrem nos veículos durante o seu uso pelos motoristas, relacionando os dados obtidos com a leitura da telemetria do veículo, assim como as percepções do motorista sobre uma determinada falha. A exploração de técnicas que auxiliam o diagnóstico automotivo é um problema a ser discutido na engenharia. As técnicas de diagnóstico devem fornecer suporte à grande gama de sistemas que surgirão nos próximos anos. Assim, é possível também utilizar a conectividade que os celulares e veículo dispõem na atualidade, com o intuito de evoluir o produto oferecido e melhorar a experiência do cliente enquanto dirige. A inclusão do cliente no processo de diagnóstico, visa permitir que os projetistas identifiquem pontos que podem ser melhorados no carro, mesmo que não apresentem erro aparente. A opinião do cliente pode ser considerada, uma vez que ele é incluído no processo como um novo “sensor" (o mais inteligente e importante de todos) capaz de reportar suas percepções. Considerando este fato, a motivação deste trabalho se encontra em: (i) Procurar alternativas para aplicar de maneira eficiente a conectividade dos veículos no processo de diagnóstico; (ii)Permitir que as montadoras possam obter informações mais concretas dos veículos que comercializam. O objeto é fornecer um sistema de interface entre o usuário e o carro auxiliando a realização dos diagnósticos preventivos mais completos do que apenas usando a telemetria. Neste projeto busca-se que haja uma interação maior com o motorista, e que a experiência das montadoras possa entrar no processo de diagnóstico. Mesmo que o cliente não tenha tanta intimidade com o funcionamento do veículo, o sistema deve orientar o motorista no processo. A entrada de dados do cliente é realizada por texto ou comando de voz. Para a análise desta entrada de dados do cliente foi realizado um estudo de mineração de dados em bases que a montadora já possuía. Estas bases são entrevistas telefônicas nas quais a montadora pergunta ao cliente problemas percebidos no carro, e então as respostas são transcritas para a base. O estudo de mineração de dados foi realizado para a criação do classificador que é responsável por receber os dados de entrada do cliente e classifica-lo em um determinado problema ligado à multimídia ou relacionado ao motor do veículo. O software contido no celular será responsável por solicitar ao sistema de sensores do veículo as leituras necessárias para que o cliente obtenha as informações que deseja. Foi realizado um experimento no qual analistas de diagnóstico responderam a um questionário que buscava analisar se o sistema proposto realmente influenciava no processo de diagnóstico, fazendo com que a solução dos problemas no veículo seja realizada em menos etapas, se comparado com o processo atual. Os resultados mostraram que a abordagem diminuiu o número de etapas necessárias para a obtenção do diagnóstico correto. Dos três estudos de caso realizados, as etapas necessárias para se chegar ao diagnóstico apropriado diminuiu em uma etapa para o primeiro estudo de caso, diminuiu uma etapa para o segundo estudo de caso, e três etapas a menos para o terceiro estudo de caso. Com estes resultados foi possível demonstrar que a abordagem proposta influiu no processo de diagnóstico aplicado atualmente pelas montadoras e concessionárias analisadas.
The goal of this project is to develop an approach to assist automakers in obtaining information about the problems and failures that occur in the vehicles during their use by drivers, relating the data obtained by reading the telemetry of the vehicle, as well as the perceptions of the driver about a particular failure. The exploration of techniques to optimize automotive diagnosis is a problem to be discussed in Engineering. Diagnostic techniques should support the wide range of systems that will emerge in the coming years. Thus, it is also possible to use the connectivity that mobile phones and vehicles have today, in order to evolve the product offered and improve the customer experience while driving. The customer inclusion in the diagnostic process aims to allow engineers identifies points to be improved in the car, even if they dont present an apparent error. The customer opinion should be considered, since he/she is included in the process as a new "sensor"(the most intelligent and important of all) able to report his/her perceptions. Considering this fact, the motivation of this research is: (i) search for alternatives to efficiently apply vehicle connectivity in the diagnostic process, (ii) enable automakers to obtain more concrete information on the vehicles they sell. The object is provide an interface between the user and the car helping them to perform a more complete preventive diagnoses than only using telemetry. In this project it is sought that there is a greater interaction with the driver, and the experience of the automakers could be included the diagnostic process. Even if the customer isnt so accustomed with the operation of the vehicle, the system must guide the driver by the process. The customer input data is performed by text or voice command. The analysis of this customer input data, a data mining study was performed based on a base that automaker already had. These bases are telephone interviews in which the automaker asks the customer for perceived problems in the car, and then the answers are transcribed to the base. The data mining study was performed to create one classifier which was responsible for receiving the input data from the customer and sorting it into a particular problem related with the multimídia or related to the motor of the vehicle. The software contained in the mobile phone will be responsible asking to the vehicle sensor system for the readings necessary for the customer can to obtain the information he/she wants. An experiment was carried out in which diagnostic analysts answered a questionnaire that sought to analyze whether the proposed system actually influenced the diagnostic process, so the solution of the problems in the vehicle was performed on less steps compared to the current process. The results showed the approach reduced the number of steps required to obtain the correct diagnosis. Of the three case studies performed, the steps required to arrive at appropriate diagnosis decreased in one step to the first case study, one step to the second case study decreased, and three less steps to the third case study . With these results it was possible to demonstrate that the proposed approach influenced the diagnostic process currently applied by the automakers and concessionaires analyzed.

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.

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.

碩士
世新大學
資訊管理學研究所(含碩專班)
100
In trends towards the globalization of medical services, how to enhance the specialization of medical services is bound to be a future trend.Nowadays the medical services industry in Taiwan has been in the mature stage. Therefore, according to the increasingly sophisticated development of medical technology and equipments, the Information Division of a medical center is very important. Whether the medical information systems, or the related medical equipments, they must rely on the Information Division to develop and provide information services. This thesis uses the case hospital’s existing repair request system of the medical computer equipment, retrieve the maintenance records from database for analysis, and apply clustering data mining techniques to the maintenance records in 2011, a total of 31748 samples. The cluster analysis adopts the two-stage clustering method, concerning the maintenance attributes of the five clusters. The first stage uses the Ward’s method to find out the number of partitions, and the second stage applies the K-means clustering and C5.0 decision tree classifier to identify the impact factors of the five clusters. It also make recommendations about the future maintenance to reduce the maintenance frequency, shorten mean time between repairs, and Enhance the quality of medical services. Finally, we make some maintenance suggestions regarding the maintenance attributes of the five clusters, such as mean time between repairs, troubleshooting for the medical servers and general administrative computers. According to these different maintenance attributes, we can implement different maintenance strategies, which can provide a reference to the hospital information managers for the later decisions.

M.Phil.
In the modern world maintenance organisations are exposed to rapid changes in equipment complexity and the maintenance thereof. Maintenance people have to adopt completely new ways of thinking and managing. If organisations and their management do not adapt to these changes and challenges they are destined to oblivion. Moubray (1991:1) states that: "Managers everywhere are looking for a new approach in maintenance. They want to avoid the false starts and dead ends which always accompany major upheavals. Instead they seek a strategic framework which synthesizes the new developments into a coherent pattern, so that they can evaluate them sensibly and apply those likely to be of most value to them and their companies" One of the viable alternative business strategies and maintenance approaches in dealing with equipment complexity and hence the management thereof, is by means of maintenance outsourcing and contract setting. If done correctly it reduces the risk of organisations and increases flexibility. During outsourcing initiatives organisations will look for benefits such as improvement of its profit margins by means of inventory reductions, improved reliability, maintenance efficiency and expert skills maintaining their assets. Coetzee (1997:24) explain that the task of the maintenance function is "to support the production process with adequate levels of availability, reliability and operability at an acceptable cost". The same principles apply in adherence to the maintenance contracts where the Original Equipment Manufacturer (OEM) or contractor also needs to support the customer with the adequate levels of availability, reliability and operability at defined costs. This can only be achieved by quality maintenance; "the right level of maintenance performed at the right time to the right equipment by workmen able and willing to deliver quality workmanship and using correct task specifications" (Coetzee, 2000a). The method used in maintenance outsourcing negotiation and binding agreements between the organisation and the contractor is a Repair and Olivier-2008 Department of Mechanical and Manufacturing Engineering - UJ Maintenance Contract; commonly known as R+M or MARC contracts. Essentially a Repair and Maintenance contract is the outsourcing of a long term Service Level Agreement (SLA) or maintenance project, normally to the OEM, or its authorised service organisation. "A maintenance and repair contract is a contractual agreement between the OEM (original equipment manufacturer), or contractor, and the equipment owner or user, where the OEM or contractor carries out all maintenance and repair activities at a guaranteed rate per hour" (Lugtigheid, et al. 2007). When organisations adapt outsourcing as a business strategy, it creates a framework of uncertainty and risk to Original Equipment Manufactures and/or maintenance contractors who has to perform the maintenance services. The focus of manufacturing and selling is thus disrupted by the challenge of the unknown playgrounds of competitiveness

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 research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering Johannesburg 2017
The study assesses the drilling, loading and trucking stages of the mining cycle of an iron ore mine situated in the Northern Cape province of South Africa for the period of 1 January to 30 June 2011. The purpose of this study is to investigate the effect of increasing reliability and maintainability of mining equipment in an effort to increase throughput of iron ore. The data processing indicated that both availability and utilisation are contributing to the sub-optimal performance of the mine. Data regarding reliability (MTBF), maintainability (MTTR), planned and unplanned maintenance was analysed, and this provided input for the simulation model. The intention of the simulation model is to simulate scenarios of proposed improvement in MTBF and MTTR through increasing planned maintenance activities. A simulation model provides insight regarding where to invest in interventions. It is determined that the drilling equipment is the problem area.
MT2018

碩士
中華醫事科技大學
職業安全衛生系暨碩士班
107
Internationalization and trade contacts of the world are convenient and faster, the air transport enterprise playing an important role, create the economic benefits of various countries. However the flight safety is the essential factor to the economics life line. Flight safety refers to all the actions taken every security of maintaining aircrafts, passengers, goods, ground crews. Aircraft maintenance cant be separated from human factor, so it has been the most important parts of aircraft maintenance, and health of maintenance personnel has been repeatedly advocacy in the “DIRTY DOZEN”, maintenance personnel physically and mentally healthy is the safe guarantee of aircraft maintenance, Don’t endanger flight safety by human errors the industry of aircraft maintenance as one of traditional industries, all the procedures consume great labor cost, it can’t be automation but downsizing. There are two main aspects of this research “pushback” and “Procedures for moving ladder ”, the purpose is to research maintenance personnel repeat the movement and procedures lead to great discomfort by muscle and skeleton. Therefore this research is focused on the aircraft push-pull operation and the mobile scaffolding workers. The purpose is to discuss the musculoskeletal (MS) discomfort caused by force and repetitive movements during long periods of time related to aircraft maintenance (AM). Conforming to the relevant surveys and findings, the future of the industry is in the implementation of the Article 6 of the Occupational Safety and Health Law, in order to overcome repetitive operation, and so on, to promote MS disease prevention, reduce labor discomfort, and prevent occupational diseases, and reach a win-win situation. In this study taiwanese AM workers serve as subjects, and is divided in two phases:The first is a survey developed based on The Nordic Musculoskeletal Questionnaire (NMQ), to understand MS problems and analyze hazard-health relationship for each department.In the second phase, Use of human engineering evaluation tools, the Key Indicator Method (KIM) is applied for inspection in field operations and pull-force measurement, and calculate the risks. The findings of this study show that, both groups develop high intensity pain in the shoulder, neck and lower-back caused by repetitive effort. Therefore, it is recommended to the first group, install mechanical benches in four points. By using mechanical devices to lift, pull and turn the structure to make easier the operation. For the second group, it is recommended to integrate the manual work of pulling the scaffold with a machine, thereby reducing the MS discomfort caused by the manual work. Furthermore, facilitate physical activity programmes at the workplace for muscle stretching to contribute with the good health condition of the employees. Look forward to improvements in the discomfort of the workers in the workplace and establish a complete adaptive work system to protect labourer and the aircraft flight safety. Key words：Aircraft repair、Human Factors Engineering、 Nordic Musculoskeletal Questionnaire (NMQ)、 Key Indicator Method (KIM)

The decision sequences used for troubleshooting of complex electro-mechanical systems are often ad hoc and less than optimal. This research investigated Decision Analysis and Bayesian Networks for generating an optimal decision sequence for troubleshooting. The model that was used in this research was the bleed air control system of the Boeing 737 aircraft. The focus of this research, therefore is two-fold. First the construction of behavioral models and multistage decision-making models in Bayesian networks was proposed. Secondly, an efficient, easy-to-implement algorithm was developed to obtain a near optimal decision sequence. This algorithm can be readily adopted by the maintenance personnel for effective troubleshooting.
Graduation date: 2004

碩士
元培醫事科技大學
資訊管理系數位創新管理碩士班
104
In the tendency of the day, integrated medical service for improving the professional and health quality is a global trend in the future. Currently, the quality of Taiwan's medical service is in the top level in the world. Following the medical hardware system keep on upgrading, the IT system in hospital is getting important. No matter connect the information integrated system or the direct/indirect hardware instruments, we all need to rely on the IT department to plan and setup the related services. This study collect the real applied repair data in a regional hospital. Analyze the repair record data from IT department and using the data mining decision tree technology to collect total 942 records of IT system abnormal information from January to December in 2015. Start to do the decision analysis based on the repair categories, and use the C4.5 decision segment to find out usually failure hardware items. And then provide the repair suggestions in the future. We hope we can decrease the failure rate and improve the medical quality based on reducing the repair frequency and time. Our final goal is: A. Analyze the failure history of instrument and find out the corresponding solve methods. B. Using the decision tree to find out the high failure rate instrument and setup a solving model. C. Create the failure solving SOP (standard operation procedure) and upload into KM (knowledge management) platform for helping the users to solve problems easily. Focus on different hardware items and create different repair strategies to systemize/SOP the repair procedures.