A simulation approach to modelling quality and reliability features of plant processes

The relationship between component and system reliability is a key factor in the improvement of plant processes and a wide variety of models have been studied, under the general headings of “Probabilistic Methods”, “Graph Theoretical Methods” and “Simulation”. An outline review of these reliability models is given as a background to the work of the thesis and the ideas were used to steer the design of the software tool, which we have developed. The tool is generic in the sense that it can be used for any production system consisting of any number of parallel production lines, although we have considered its application in detail for one system only. In particular, we describe an application of reliability theory in the modelling of a plant process, which incorporates examples of Load-Sharing, parallel and series stages and we demonstrate how the production plannmg control is related to reliability considerations. The tool has been tested in reference to a real production system, for which Quality and Reliability features have been analysed though data collection and simulation. The production system is located in Intel’s ESSM (European Site for System Manufacturmg) plant m Ireland. The plant's products are the basic components of a Pentium II processor, based on a new technology, (known as MMX or Secc), which enables enhancements for multimedia and communication applications. We have also applied our software tool to the old production line (pre-datmg Secc Technology), both for calibration purposes and to compare the two lines Software features mclude the ability to, mvestigate line reaction to changes in quality and reliability, to pmpomt problem areas, to cost failures in reliability, to explore degraded operation, stages with poor quality/reliability can be identified and Estimate the real UPH (Units Per Hour). We present an analysis of system performance and provide recommendations for possible improvements to the system

Component Reliability Estimation From Partially Masked and Censored System Life Data Under Competing Risks.

This research presents new approaches to the estimation of component reliability distribution parameters from partially masked and/or censored system life data. Such data are common in continuous production environments. The methods were tested on Monte Carlo simulated data and compared to the only alternative suggested in literature. This alternative did not converge on many masked datasets. The new methods produce accurate parameter estimates, particularly at low masking levels. They show little bias. One method ignores masked data and treats them as censored observations. It works well if at least 2 known-cause failures of each component type have been observed and is particularly useful for analysis of any size datasets with a small fraction of masked observations. It provides quick and accurate estimates. A second method performs well when the number of masked observations is small but forms a significant portion of the dataset and/or when the assumption of independent masking does not hold. The third method provides accurate estimates when the dataset is small but contains a large fraction of masked observations and when independent masking is assumed. The latter two methods provide an indication which component most likely caused each masked system failure, albeit at the price of much computation time. The methods were implemented in user-friendly software that can be used to apply the method on simulated or real-life data. An application of the methods to real-life industrial data is presented. This research shows that masked system life data can be used effectively to estimate component life distribution parameters in a situation where such data form a large portion of the dataset and few known failures exist. It also demonstrates that a small fraction of masked data in a dataset can safely be treated as censored observations without much effect on the accuracy of the resulting estimates. These results are important as masked system life data are becoming more prevalent in industrial production environments. The research results are gauged to be useful in continuous manufacturing environments, e.g. in the petrochemical industry. They will also likely interest the electronics and automotive industry where masked observations are common