PIs: Sherif Noah, Alexander G. Parlos, Suhada Jayasuriya, Texas A&M University, Department of Mechanical Engineering, College Station, Texas 77843
Proposal Number: 0100238
Proposal Title: Smart Rotating Machinery
Project Abstract:
This exploratory research project addresses the development of smart rotating machinery. Unplanned machinery downtime and poor machinery performance impact negatively both industrial productivity and safety at an annual level of $1 trillion. It is therefore timely to develop smart rotating machinery that are highly adaptive to uncertain dynamic environments while maintaining high level of performance. Such machinery must incorporate new and innovative breakthroughs rooted in new information technologies that enable them to exhibit memory, learn from experience and use this learning ability to improve their adaptability while performing in an optimal manner.
The technical approach of the proposed research relies on health monitoring, condition assessment and early fault diagnosis through a combination of physics-based nonlinear rotordynamics models and empirical models developed through real-time sensor data. Closed-loop early incipient fault diagnosis is achieved through the use of computational intelligence tools, e.g. neural networks, fuzzy logic, and genetic algorithms, and other advanced signal processing methods, such as wavelet analysis. Towards making smart rotating machinery a reality, an initial framework will be explored for a methodology that will enable embedding certain elements of intelligent behavior into rotating machinery. The proposed limited effort is considered high-risk because it constitutes a pioneering study in smart systems with the perceived difficulties in developing an effective methodology. Furthermore, this research will lead to the experimental demonstrations of early diagnosis algorithms for controlled rotating machinery, a subject that has yet to be addressed in the literature. Such algorithms will control and mitigate impending failures of critical rotating machinery, reducing the probability of unplanned downtime, emergency shutdowns and catastrophic accidents.
