Dr. Bob Brennan

To remain competitive in today’s global market, manufacturers require systems that are capable of quickly responding to change while maintaining stable and efficient operation. For example, in the context of discrete part manufacturing, the manufacturing system must be capable of responding to upstream disturbances such as material delivery delays and material quality problems, internal disturbances such as control/communication failures and machine breakdowns, and downstream disturbances such as rush orders and change orders. Increasingly, industrial automation and control technology is viewed as central to achieving this goal.

The aim of this research program is to develop innovative technologies and practical solutions to realize industrial cyber-physical systems that are both resilient (capable of recovering from disturbances) and adaptive (capable of reconfiguration). This work builds on the conceptual work on cyber-physical systems (systems consisting of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators) with a focus on design methods and tools for device-level sensing and control in harsh, industrial environments (e.g., discrete-part manufacturing, chemical and process industries).

Our scientific approach focuses on the ambitious goal of emulating human processes rather than simply automating them: i.e., enabling autonomy by delegating responsibility to the system to meet its prescribed goals. This is achieved by matching the control model more closely with the physical system, resulting in a low-cost industrial digitalization approach where control is achieved by the emergent behaviour of many simple, autonomous and cooperative modules. This is crucial for modern automation systems, which need to monitor and control widely distributed devices in an environment that is prone to disruptions and subject to noise and interference generated from heavy machinery.

Achieving resilient and adaptive systems requires a specialized form of autonomy: the ability of the system to manage itself (to heal, protect, configure, optimize). The long-term vision is to develop next-generation industrial cyber-physical systems with these self-management abilities at the device level. Short-term objectives include creating design methods and tools for self-healing (recovering from faults to maximize system availability) and self-configuration (adapting functions, structures, and processes to changes).

The anticipated outcome is a modular and scalable distributed intelligent control solution suitable for small to medium enterprises, offering low-cost, systematic, and repeatable deployment of digital technologies. This innovative approach will significantly enhance the flexibility and resilience of industrial systems, enabling Canadian manufacturers to respond quickly to change while maintaining stable system operation and efficient use of available resources.

All Canadian undergraduate engineering programs are required to follow an accreditation process to ensure that their graduates meet the high standards necessary to become professional engineers. In recent years, this process has shifted from an input-based approach of analyzing curriculum content and contact hours, to an outcomes-based approach with an increased focus on student learning outcomes and continual improvement. Engineers Canada is now recommending a full shift to outcomes-focused accreditation (FEA, 2024). 

To support this transition to outcomes-focused accreditation, we are developing a set of surveys that will assess student self-efficacy across the Canadian Engineering Accreditation Board’s 12 graduate attributes (CEAB, 2024). This data will be used in combination with existing graduate attribute targeted classroom assessments to provide a critical additional view – the students personal perspective - on the attributes that students obtain during their studies. However, before this form of assessment can be effectively used in the outcomes-based assessment process, it is important to first understand the relationship between self-efficacy and the teaching and learning environment. This project focuses on exploring how students’ self-efficacy is impacted over time as they progress through their programs, and whether students’ self-efficacy is impacted by changes in the curriculum.

The global shift towards sustainable energy solutions has heightened interest in hydrogen as a clean fuel alternative, mainly because of its potential to reduce greenhouse gas emissions. However, safely and efficiently transporting hydrogen from production sites to consumers is a major challenge. For example, hydrogen’s distinctive physical and chemical properties, such as its broad flammability range and low activation energy, raise the risk of leaks and potential hazards. Early detection and a swift response to hydrogen leaks are crucial to minimizing the chances of dangerous incidents and the associated risks to the environment, infrastructure, and communities.

Pipeline incidents result from a combination of pipeline integrity issues such as corrosion and fatigue, and environmental hazards such as flooding, slope instability, and frost heaving. In a parallel Alberta Innovates Hydrogen Center of Excellence project, we are utilizing the Advanced Pipeline Research and Innovation Laboratory (APRIL) flow loop facility to focus on pipeline integrity issues. For this project, our focus is on environmental hazards that can occur over large geographic areas such as those spanned by the Alberta Products Pipe Line (APPL).

Our aim is to create a pipeline risk analysis model that can accurately identify specific areas where failures are likely to occur based on environmental conditions like weather forecasts and GIS (Geographic Information System) data. This system will enable rapid deployment of emergency services and repair teams to the correct pipeline locations in the event of a failure, and help operations personnel identify high-risk areas for preventive maintenance. Additionally, the model will serve as a training tool for pipeline risk analysis.