Structural Health Monitoring (SHM) is an emerging tool aimed to take a structure from passive to an active state in terms of intelligence and self diagnosis. However, many issues remain to be addressed before SHM becomes a readily usable and easily accessible tool. Techniques for extracting structural signatures, managing data, and detecting structural changes or damage remain core areas of research. This research on the Aurora II oil sand crusher of Syncrude Canada Limited in Fort McMurray, Alberta, extends the application of SHM beyond the conventional boundaries of bridges and buildings to industrial structures. A range of design parameters need to be tested, more detailed loading information needs to be obtained, and long term data on real field conditions are required for future design enhancements in the structure, which this research project strives to fulfill.

The Aurora II crusher has been instrumented with a state-of-the-art data acquisition system to regularly monitor and provide a health report on demand. This research establishes a knowledge-base on crusher behaviour and the loads acting on it under different operating conditions. Structural behaviour of the crusher including strain patterns and their inter-relationship with different dump types, critical strain magnitudes, and impact factors at various locations under severe loading have been established. Dynamic properties of the structure such as fundamental frequencies of vibration and their modal damping ratios have been determined. Fatigue analysis on the structural components revealed a reasonable fatigue life in general; however, factors like higher critical stresses due to a history of loss of bolts lead to a recommendation for annual inspection and tightening of bolts. Pontoon and hopper response are found to be quite stable whereas some of the columns are heavily stressed under extreme loading. Under similar conditions, most of the bracing members show signs of excessive bending as well as axial stresses indicating design vulnerability that could lead to issues with serviceability and fatigue. Hence, they are recommended for review in future designs of the crusher structure.

With about 1.5 GB of data produced each day, data processing and management assumed great importance. Using strain jump as the metric of dump events, a multi threshold based data processing and management algorithm has been developed to successfully reduce data by 10,000 times. The dump events identified are verified across a number of selected gauges before writing into a database of dump events. Statistical analyses were conducted to establish the parameters for monitoring the structure. A finite element model of the structure has been calibrated with field observations to propose a new field design load case. A numerical damage simulation exercise has led to decisions on suitable strategies for damage detection by monitoring the strain magnitudes and strain patterns in the vicinity of a damage. The data processing and management system, the structural behaviour and strain patterns, statistical parameters, and finite element model together constitute an SHM system that is able to provide on demand health report of the crusher, predict its remaining life and monitor its performance.