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Sensor Selection, State Estimation and Fault Diagnosis of Simple Rotor-Bearing Systems Open Access


Other title
optimal sensor selection
finite element discretization
rotor-bearing system
optimal two-stage Kalman filter
adaptive two-stage Kalman filter
Type of item
Degree grantor
University of Alberta
Author or creator
Pan, Rui
Supervisor and department
Zhao, Qing (Electrical and Computer Engineering)
Examining committee member and department
Chen, Tongwen (Electrical and Computer Engineering)
Zuo, Ming J. (Mechanical Engineering)
Department of Electrical and Computer Engineering
Control Systems
Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
In this research, state and imbalance fault estimation of a simple rotor-bearing system using Kalman filtering techniques has been investigated. The motion of a simple rotor- bearing system can be described by a set of coupled partial differential equations (PDEs); approximative equations of the motion PDEs are derived by variational formulation, where the PDE system is spatially discretized into a high dimensional ordinary differential equation (ODE) form. Optimization-based sensor selection algorithm for Kalman filtering is then applied to optimally choose among the large number of ODE model states to measure, such that specific requirements for state estimation performance are satisfied with a small number of sensors. For practical applications such as the rotor-bearing systems, fault estimation is usually one of the goals of system monitoring; augmented-state Kalman filter (ASKF) is preferred for its simple formulation, but at the cost of more intensive computation and greater numeric errors due to higher system order. Alternatively, optimal two-stage Kalman filter (OTSKF) provides an equivalent form of ASKF under certain algebraic constraint but with generally lower computation complexity and many practical advantages. Adaptive two-stage Kalman filter (ATSKF) is thus applied in this research for simultaneous state and fault estimation of the rotor-bearing system, and the optimal adaptive fading factor for OTSKF is designed using the innovation sequence which is equivalent to that of ASKF. Simulation results have demonstrated the effectiveness of ATSKF handling sudden imbalance fault occurrence during the operation of the rotor-bearing system using the optimally selected sensors.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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