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Condition monitoring in a hydraulic system of an industrial machine using unscented Kalman filter

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Title: Condition monitoring in a hydraulic system of an industrial machine using unscented Kalman filter
Author: Razavi, Behnam
Degree Master of Applied Science - MASc
Program Mechanical Engineering
Copyright Date: 2009
Publicly Available in cIRcle 2009-09-30
Abstract: The detection and isolation of faults in engineering systems is of great practical significance. The early detection of fault occurrence in a machine is critical in avoiding machine-performance degradation, and major damage to the machine itself. In the present thesis, the focus is to selects and implements an appropriate modeling approach to detect and diagnose the possible faults in a complex hydraulic system of an industrial machine with on-line monitoring. This work develops a model-based system for on-line condition monitoring of the hydraulic system of an industrial automated fish processing machine, using Unscented Kalman Filter (UKF). A requirement in implementing this technique is to develop an accurate mathematical model of the monitored system. First, a state-space model is developed and validated against simulated results. The state variables of the model are displacement and velocity of the spool valve, pressures of the two chambers of the hydraulic cylinder, and displacement and velocity of the hydraulic actuator. The unknown parameters of the state-space model are identified through direct measurement and experimentation. Results show that under normal operating conditions, the response of the machine satisfactorily matches that of the state-space model. The developed UKF is implemented in the machine and four common hydraulic faults are artificially introduced. These faults are external leakage in the two chambers of the cylinder; internal leakage; and dry friction build up on the surface of the two moving plates (cutter carriage). Low, medium and high levels of leakage are introduced to the system. The criteria that are considered in fault diagnosing are residual moving average of the errors, chamber pressures, and actuator characteristics. Experimental studies indicate that the developed scheme can correctly estimate the current state of the system in real time, with an acceptable residual of moving average error (MAE), thereby validating it.
URI: http://hdl.handle.net/2429/13395
Scholarly Level: Graduate

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