Assessing the legacy of the pine point mine: post-industrial land cover and land use

While mining is a major component of the northern Canadian economy, including the contemporary mixed economy of Aboriginal communities, it often leaves legacies of environmental and economic transformation that persist after closure. The legacies of historical mines in northern Canada challenge industry claims of sustainability. This thesis addresses how industrial mineral development and closure continue to affect local environments and economies after abandonment. The abandoned Pine Point mine in the Northwest Territories provides a case study for explaining the ongoing relationships among land cover, land use, and the post-industrial landscape. Drawing from landscape ecology and micropolitical ecology, I adopt an interdisciplinary approach to examine environmental and socioeconomic changes in the wake of industrial development and closure at Pine Point. The results show that passive reclamation is not sufficient for restoring ecological function in a subarctic environment. Land use, however, persists as land users adapt to the post-industrial landscape despite grave concern about its environmental condition. If mining is to be considered sustainable, decommissioning and reclamation must explicitly account for long-term environmental transformation as well as ongoing post-industrial land use, particularly in Aboriginal contexts.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.