Please read the abstract in the section 00front of this document / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2006. / Mechanical and Aeronautical Engineering / unrestricted

The increasing electricity demand in South Africa has lead to a shortage in electricity supply. In response to this problem Eskom has introduced multiple capacity expansion programs. Unfortunately the electricity shortage is expected to continue until Eskom’s capacity expansion programs are completed. Demand Side Management (DSM) is widely accepted as an immediate solution to the high electricity demand of South Africa. Numerous DSM projects implemented by ESCo’s have been successful, but over the years have not been sustainable. Without regular maintenance from the relevant ESCo, many projects have failed to achieve sustainable savings. After the implementation of DSM projects, all installed equipment and software becomes the property of the client. Experience has illustrated that some mines did not always have the expertise or available resources to monitor and maintain the projects. As a result the electrical energy savings of the project would gradually deteriorate. A feasibility study was conducted to determine whether the re-instatement of redundant and debilitated mine DSM projects could be marketed as the “low hanging fruit” of the industry. A key driver for this study, was the fact that costs involved for re-instatement of such DSM projects are generally considerably lower than those of new projects, yet still producing lucrative electricity savings. Three major mining entities discussed in this dissertation have neglected to realise a collaborative cost saving of R 55,5 Million per annum. This loss of opportunity can mainly be attributed to a lack of maintenance and monitoring of operational DSM projects on their mining sites. Three DSM projects related to the water reticulation system of the mine were investigated. It was discerned in all three cases that the successful re-instatement of DSM projects are indeed possible, but only when subjected to continuous monitoring. The maintenance performed on two of the three projects, respectively realised approximately R2,7 Million and R 750 000. This was achieved through the process of load shifting, over a period of one year. Maintenance on the third project realised approximately R1,5 Million through energy efficiency over a three month period. This dissertation illustrates that attractive savings in electricity and cost can be realised when re-instating redundant DSM projects in the mining industry. It also demonstrates the cost and time effectiveness of implementing such projects, compared to the focus on new DSM installations. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013.

The increasing electricity demand in South Africa has lead to a shortage in electricity supply. In response to this problem Eskom has introduced multiple capacity expansion programs. Unfortunately the electricity shortage is expected to continue until Eskom’s capacity expansion programs are completed. Demand Side Management (DSM) is widely accepted as an immediate solution to the high electricity demand of South Africa. Numerous DSM projects implemented by ESCo’s have been successful, but over the years have not been sustainable. Without regular maintenance from the relevant ESCo, many projects have failed to achieve sustainable savings. After the implementation of DSM projects, all installed equipment and software becomes the property of the client. Experience has illustrated that some mines did not always have the expertise or available resources to monitor and maintain the projects. As a result the electrical energy savings of the project would gradually deteriorate. A feasibility study was conducted to determine whether the re-instatement of redundant and debilitated mine DSM projects could be marketed as the “low hanging fruit” of the industry. A key driver for this study, was the fact that costs involved for re-instatement of such DSM projects are generally considerably lower than those of new projects, yet still producing lucrative electricity savings. Three major mining entities discussed in this dissertation have neglected to realise a collaborative cost saving of R 55,5 Million per annum. This loss of opportunity can mainly be attributed to a lack of maintenance and monitoring of operational DSM projects on their mining sites. Three DSM projects related to the water reticulation system of the mine were investigated. It was discerned in all three cases that the successful re-instatement of DSM projects are indeed possible, but only when subjected to continuous monitoring. The maintenance performed on two of the three projects, respectively realised approximately R2,7 Million and R 750 000. This was achieved through the process of load shifting, over a period of one year. Maintenance on the third project realised approximately R1,5 Million through energy efficiency over a three month period. This dissertation illustrates that attractive savings in electricity and cost can be realised when re-instating redundant DSM projects in the mining industry. It also demonstrates the cost and time effectiveness of implementing such projects, compared to the focus on new DSM installations. / Thesis (MIng (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2013.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

The growth of global competition has led to remarkable changes in the way manufacturing companies operate. These changes have affected maintenance and made its role even more crucial for business success. To remain competitive, manufacturing companies must continuously increase the effectiveness and efficiency of their production processes. Furthermore, the introduction of lean manufacturing has increased concerns regarding equipment availability and, therefore, the demand for effective maintenance. That maintenance is becoming more important for the manufacturing industry is evident in current discussions on national industrialization agendas. Digitalization, the industrial internet of things (IoT) and their connections to sustainable production are identified as key enablers for increasing the number of jobs in industry. Agendas such as “Industry 4.0” in Germany and “Smart Industry” in Sweden are promoting the connection of physical items such as sensors, devices and enterprise assets, both to each other and to the internet. Machines, systems, manufactured parts and humans will be closely interlinked to collaborative actions. Every physical object will formulate a cyber-physical system (CPS), and it will constantly be linked to its digital fingerprint and to intensive connection with the surrounding CPSs of its on-going processes. That said, despite the increasing demand for reliable production equipment, few manufacturing companies pursue the development of strategic maintenance. Moreover, traditional maintenance strategies, such as corrective maintenance, are no longer sufficient to satisfy industrial needs, such as reducing failures and degradations of manufacturing systems to the greatest possible extent. The concept of maintenance has evolved over the last few decades from a corrective approach (maintenance actions after a failure) to a preventive approach (maintenance actions to prevent the failure). Strategies and concepts such as condition based maintenance (CBM) have thus evolved to support this ideal outcome. CBM is a set of maintenance actions based on the real-time or near real-time assessment of equipment conditions, which is obtained from embedded sensors and/or external tests and measurements, taken by portable equipment and/or subjective condition monitoring. CBM is increasingly recognized as the most efficient strategy for performing maintenance in a wide variety of industries. However, the practical implementation of advanced maintenance technologies, such as CBM, is relatively limited in the manufacturing industry. Based on the discussion above, the objective of this research is to provide frameworks and guidelines to support the development and implementation of condition based maintenance in manufacturing companies.  This thesis will begin with an overall analysis of maintenance management to identify factors needed to strategically manage production maintenance. It will continue with a focus on CBM to illustrate how CBM could be valued in manufacturing companies and what the influencing factors to implement CBM are. The data were collected through case studies, mainly at one major automotive manufacturing site in Sweden. The bulk of the data was collected during a pilot CBM implementation project. Following the findings from these efforts, a formulated maintenance strategy is developed and presented, and factors to evaluate CBM cost effectiveness are assessed. These factors indicate the benefits of CBM, mostly with regard to reducing the probability of experiencing maximal damage to production equipment and reducing production losses, particularly at high production volumes. Furthermore, a process of CBM implementation is presented. Some of the main elements in the process are the selection of the components to be monitored, the techniques and technologies for condition monitoring and their installation and, finally, the analysis of the results of condition monitoring. Furthermore, CBM of machine tools is presented and discussed in this thesis, focusing on the use of vibration monitoring technique to monitor the condition of machine tool spindle units. / INNOFACTURE - innovative manufacturing development

Please read the abstract in the section, 00front of this document / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / unrestricted

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity

Master’s thesis is concerned with maintenance management in manufacturing companies focusing on TPM. The first part of the thesis deals with the topic theoretically and proposes possible solution in company Siemens Electric Machines s.r.o. based on system analysis. The practical part then describes the current state of maintenance management and recommend various measures to improve autonomous, scheduled and reactive maintenance in this company.