Automated Diagnostics for Energy and Environmental Systems

Our research team develops remote automated methods to diagnose faults in energy systems and related industries to reduce the risk of failures, as well as their environmental and economic impacts. We do this by developing and deploying novel instruments, embedded sensors, & robotic delivery systems for predictive maintenance based on reliability models. These automaed systems, deployable remotely, improve reliability of a range of energy systems, from pipelines and rail cars to wind turbines.

Model-based industrial equipment diagnostic methods include measurements from conventional sensors (vibration sensors, thermographic imagers, acoustic sensors, strain gauges, and material acoustic emissions sensors) and analysis of  samples (lubricants, material samples), which for some parameters can be done in-situ, but more often requires a sample to be collected, prepared, and analyzed elsewhere. Environmental monitoring of areas that may be affected by industrial activities adds another dimension to system reliability. Water sampling can be done by automated amphibious robots with sampling payloads. Geotechnical properties can be done in-situ using instrumentation such as a cone penetrometer, soil characteristics can be measured while driving, and samples can also be collected.

Our goal is to automate the collection of this information so that people can stay out of potentially hazardous situations, avoid the drudgery of remote data collection, and put their talent to interpretation and judgment for assessing the integrity of industrial and environmental systems.

Laboratory physical systems enable time-varying system diagnostic methods to be tested before field deployment in ruggedized packages. Our research apparatus includes a programmable electric-drive mechanical fault diagnostic system, 100 kg 200 Hz shaker table, a flow loop for pipeline leak detection studies, robotic rovers for tailings diagnostics & , embedded wireless sensors, 32 channel accelerometer bank and toughbook data acquisition, UAVs for remote asset integrity assessment and environmental monitoring (vibration monitoring, ultrasonic structural inspection, lubricant sampling, remote thermal monitoring such as tire condition, water sampling, and soil sampling).

Applications include rotating equipment, bitumen production systems (shovels, trucks, pumps, slurry lines, extraction components, tailings structures and systems), pipelines, rail air brakes, high-voltage electrical cables, and wind turbines.

Oilsands mining and bitumen production

Existing oilsands production methods are energy intensive and have ongoing operability and reliability challenges. Current plants use economy of scale and integrated operations to improve unit cost of production, which is economical only for large, high-quality orebodies. This strategy introduces issues of net land disturbance, material handling costs, and process robustness.

Although the fundamentals of bitumen separation processes are being extensively investigated, other aspects of bitumen production have received less attention, particularly how oilsand mining methods affect downstream extraction separation performance and equipment reliability. Many techniques exist for monitoring and controlling the performance of mechanical systems and chemical processes. There are also many techniques for monitoring and refurbishing the condition of such systems to restore system reliability. There is, however, little understanding of the relationships between them and a framework for controlling reliability within the constraints of production to improve the utilization of equipment and processes. New, more integrated processes will rely increasingly on process control for robustness and on-line assessments of reliability to achieve production targets and run lengths while reducing energy intensity, environmental footprint, and costs.

This research program in reliable integrated oilsands systems considers issues of design and reliable operation of complex industrial systems, with a focus on oilsands bitumen production equipment for surface deposits and near-surface marginal orebodies. The objectives of this research program are:

• to develop a framework for controlling the reliability of a system with process constraints or interdependencies;
• to develop model-based understanding of complex systems such as oilsands bitumen production, and
• to apply this understanding to new production methods that more sustainable (reduced energy intensity, emissions, and cost of capital and operation, including maintenance and reclamation).

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Short-term objectives address process performance and reliability of components and systems in discrete-event mining systems and continuous extraction and material handling systems, and the potential for automation.

This research area combines systems analysis, modeling and identification, reliability engineering, and classification techniques to develop:

• methods to detect anomalous equipment behaviour;
• direct measures of equipment condition – ideally on-line;
• methods to evaluate machine condition by indirect methods, when direct methods are unavailable and incipient failure is detectable by a change in process performance;
• mechanistic understanding of damage processes and their relationship to process dynamics;
• characterization of maintenance and refurbishment processes to determine to what extent reliability is restored.

Examples of bitumen production systems reliability relationships include:

• shovel tooth loss leading to crusher damage leading to screen blinding leading to reduced throughput
• screen cloth opening increase to improve throughput leading to bigger lumps leading to downstream vessel wall damage and high pump wear
• solids loading leading to centrifuge fouling, sloughing, imbalance, and failure
• high slurry particle size leading to higher deposition velocity requirement leading to higher flow rates leading to increased pump and pipe wear (also damage to internals of flotation cells)
• remote vibration monitoring and lubricant sampling for inaccessible gearboxes and other equipment using robotic UAV platforms.

Longer-term objectives are more sustainable production and reclamation methods, industrial environmental monitoring of pipelines and other systems (using embedded systems and robotics), and novel methods for extracting bitumen from new orebodies.