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Detection of encroachment on pipeline right-of-way is important for pipeline safety. An effective system can provide on-time warning while reducing the probability of false alarms. There are a number of industry and academic developments to tackle this problem. This thesis is the first to study the use of a wireless sensor network for pipeline right-of-way encroachment detection. In the proposed method, each sensor node in the network is responsible for detecting and transmitting vibration signals caused by encroachment activities to a base station (computer center). The base station monitors and analyzes the signals. If an encroachment activity is detected, the base station will send a warning signal. We describe such a platform with hardware configuration and software controls, and the results demonstrate that the platform is able to report our preliminary experiments in detecting digging activities by a tiller in the natural and automotive noise.

This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models. / Thesis (Master, Mining Engineering) -- Queen's University, 2007-11-28 15:22:17.454 / I would like to thank the financial support provided by the George C. Bateman and J. J. Denny Graduate Fellowship, as well as funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) provided via NSERC grant support to Dr. Daneshmend.