Intelligent Reliability Assurance Using Dynamics Modeling and Machine Learning

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Today's society depends on safe and reliable operation of colossal engineering systems such as power plants and air, ground, and water transportation systems. Despite technological advancements in reliability assurance methodologies over the past 80 years, accidents and service disruptions affecting millions of people still occur frequently. Because degradation occurs as a device delivers service and inherent uncertainty exists in material properties, degradation behaviors, and environmental conditions, reliability assurance is a challenging problem to solve. Growing functionalities, faster operating speeds, and harsher operating environments of today's engineering systems present new challenges to their reliability assurance. New approaches including physics-based computer simulations and machine learning algorithms for online reliability assurance are needed to address these challenges. The new research frontier within the reliability community is to develop unique solutions for separate classes of engineering systems such as rotating equipment and pipelines. My research group has been making significant contributions over the past 30 years to the long-term goal of advancing reliability assurance methodologies for engineering systems. The proposed research over the next five years aims to build upon the expertise of my research group and to utilize computing power and artificial intelligence to improve the existing reliability assurance methodologies for the asset class of rotating equipment. Specifically, we will (1) develop physics-based dynamics models for gear systems which generate vibration responses reflecting the degrading health conditions of their critical components and the varying operating conditions including load and speed and (2) develop data-driven machine learning algorithms for accurate assessment of the hidden health state of gear systems, prediction of their remaining useful lives, and effective maintenance decision making to ensure their reliable and cost-effective operation. We will conduct extensive laboratory experiments on gear systems to provide the needed data to validate the developed dynamics models and the generated machine learning algorithms for various aspects of reliability assurance. The proposed research will generate practical models and intelligent algorithms which can be directly applied to systems such as machine tools, wind farms, driverless vehicles, and power plants. These cutting-edge research results together with the trained highly qualified personnel will put Canadian research community and relevant industry at the forefront in the world stage.