Global ETD Search

41	Short Interval Tasks: Observation for Job Enrichment in Maintenance Barker, David W. 08 1900 (has links) This research tests the application of job enrichment in a maintenance short-interval-task oriented organization. Data were collected from nine work groups involving twenty-nine employees. Results indicate enrichment in this type of organization is possible. Criteria were established to measure the fit of each work group to the five core job dimensions of skill variety, task identity, task significance, autonomy, and feedback. A strong correlation between core job dimension scores and productivity levels was indicated. A work control program, compatible with job enrichment, is discussed and used to implement job content changes. Manipulation of the job content for poor performing groups with low core job dimension scores has resulted in significant reductions in nonproductive time. job enrichment maintenance organizations short-interval-tasks work control system motivation-hygiene theory Job enrichment. Work design.
42	Nuclear Power Plant Maintenance Improvement via Implementation of Wearable Technology Mattmuller, Adam 29 September 2016 (has links) No description available. Energy Engineering Mechanical Engineering Nuclear Engineering Radiation Surgery google glass wearable technology glassware nuclear power plant maintenance human reliability human error
43	Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems Weppenaar, De Ville January 2010 (has links) Thesis (M. Tech.) -- Central University of Technology, Free State, 2010 / Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation. Plant maintenance - Management Multiagent systems Intelligent agents (Computer software)
44	Application of the physical asset management in the city of Cape Town water services Mhlongo, Thabani 12 1900 (has links) Thesis (MBA)--University of Stellenbosch, 2010. / The City of Cape Town water services is currently operating on the reactive mode and their maintenance system is not effectively and efficiently utilized. The lack of proper utilization of Physical Asset Management (PAM) has resulted in the technical department failure to meet the departmental objectives of providing planned and scheduled maintenance activities, reducing equipment downtime and ensuring planned delivery of quality spares at the appropriate time. The aim of the report is to provide guidance in the implementation of physical assets management. The report looks at different maintenance strategies available and evaluates the current operating scenarios for the department and further recommends the relevant strategy that will suit the department. Physical asset management as a maintenance program provides an effective planned and scheduled maintenance system to reduce labor and downtime. This ensures that the correct materials and parts are utilized and that the workmanship is of a high quality. A Maintenance Plan provides documented and sequenced tasks with labour and types of material required to execute the plan. Infrastructure assets are systems that serve defined operational needs, where the intention is to maintain the asset for continual use on a certain level. One of the main aspects in infrastructure network is the degree of interdependency not only within a particular asset network but also among networks. The failure of one component within a network may undermine the performance of other networks. One of the major objectives in an infrastructure network is to maintain the necessary service level through continuous maintenance while ensuring cost effectiveness The initial step is to choose certain maintenance tactics and then decide how often these tactics will be performed. The frequency of maintenance, its actions or tactics depends on the current condition of the equipment which is linked within the technical characteristics of failure and specific monitoring technique. “Regardless of the system you select, you must start from where you are and then develop an orderly series of steps to get where you want to end up, so start by assessing your current situation. The most essential ingredient in your maintenance management system is the people. An excellent system run by poorly trained or unmotivated people will be adequate at best but well trained people with positive attitudes can make an excellent system world class.”-Thomas Westerkamp Dissertations -- Business management Theses -- Business management
45	An adaptive modeling and simulation environment for combined-cycle data reconciliation and degradation estimation. Lin, TsungPo 26 June 2008 (has links) Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principle component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation. Robust estimation Hypothesis testing Process decomposition System decomposition Combined cycle Model calibration Degradation estimation Data reconciliation Gross error detection Levenberg-Marquardt algorithm Principal component analysis Electric power systems Mathematical models Calibration Combined cycle power plants Plant maintenance
46	Guidelines for successful implementation of total productive maintenance in a chemical plant / Jethro Padya Mahlangu Mahlangu, Jethro Padya January 2014 (has links) With the world economy becoming unpredictable, it has become a necessity for businesses to relook at the way they do business. The world has become competitive and companies that aim to become profitable have seen the need to find ways to improve efficiencies and increase productivity to stay relevant. There has been an adoption of strategies that are aimed at improving the efficiencies in companies such as Total Productive Maintenance (TPM). The strategy is aimed at improving equipment efficiencies and increase productivity through the transfer of certain skills from maintenance personnel to operators. The aim is that the operators perform some of the activities that the maintenance people used to do and they do the more complex tasks. By transferring these skills to operators there is constant cleaning, inspections and lubricating of equipment. This frees up time for maintenance people to do planning and other jobs that require time and higher skills levels. The implementation of these activities allows companies to tap into unused capacity that was always hidden by breakdowns and unplanned stops. The process however requires commitment from management and all stakeholders involved in the organisation. There are prescribed implementation processes that can be followed or companies can follow their own processes but the fundamentals of involving people from the onset must be followed. The involvement of stakeholders creates commitment at all levels and in order to sustain this initiative people must be committed to it. The inclusion of the activities transferred from maintenance people to operators, will reinforce the knowledge and habits required from operators and perhaps sustain the initiative. / MBA, North-West University, Potchefstroom Campus, 2015 Total Productive maintenance Overall equipment efficiency TPM Deployment Autonomous maintenance Plant maintenance Preventative maintenance Planned maintenance Quality maintenance Focused improvement Early equipment management Training and education Safety Health and Environment TPM in administration Zero defects Zero waste Zero downtime 5S Breakdown maintenance Preventative maintenance Corrective maintenance Maintenance prevention Downtime losses Speed losses Defect losses Quality defect losses Normal production loss Abnormal production losses Maintenance schedules Prescribed TPM deployment Equipment failure Function failure Function reduction Reprocessing losses Factors of successful TPM deployment
47	Guidelines for successful implementation of total productive maintenance in a chemical plant / Jethro Padya Mahlangu Mahlangu, Jethro Padya January 2014 (has links) With the world economy becoming unpredictable, it has become a necessity for businesses to relook at the way they do business. The world has become competitive and companies that aim to become profitable have seen the need to find ways to improve efficiencies and increase productivity to stay relevant. There has been an adoption of strategies that are aimed at improving the efficiencies in companies such as Total Productive Maintenance (TPM). The strategy is aimed at improving equipment efficiencies and increase productivity through the transfer of certain skills from maintenance personnel to operators. The aim is that the operators perform some of the activities that the maintenance people used to do and they do the more complex tasks. By transferring these skills to operators there is constant cleaning, inspections and lubricating of equipment. This frees up time for maintenance people to do planning and other jobs that require time and higher skills levels. The implementation of these activities allows companies to tap into unused capacity that was always hidden by breakdowns and unplanned stops. The process however requires commitment from management and all stakeholders involved in the organisation. There are prescribed implementation processes that can be followed or companies can follow their own processes but the fundamentals of involving people from the onset must be followed. The involvement of stakeholders creates commitment at all levels and in order to sustain this initiative people must be committed to it. The inclusion of the activities transferred from maintenance people to operators, will reinforce the knowledge and habits required from operators and perhaps sustain the initiative. / MBA, North-West University, Potchefstroom Campus, 2015 Total Productive maintenance Overall equipment efficiency TPM Deployment Autonomous maintenance Plant maintenance Preventative maintenance Planned maintenance Quality maintenance Focused improvement Early equipment management Training and education Safety Health and Environment TPM in administration Zero defects Zero waste Zero downtime 5S Breakdown maintenance Preventative maintenance Corrective maintenance Maintenance prevention Downtime losses Speed losses Defect losses Quality defect losses Normal production loss Abnormal production losses Maintenance schedules Prescribed TPM deployment Equipment failure Function failure Function reduction Reprocessing losses Factors of successful TPM deployment

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