PLC implementation of online, PRBS-based tests for mechanical system parameter estimation.
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This thesis investigates the use of correlation techniques to perform system identification tests, with the objective of developing online test methods to perform mechanical parameter extraction as well as machine diagnostics. More specifically, these test methods must be implemented on a Programmable Logic Controller (PLC) in combination with Variable Speed Drives (VSD). Models for motor-based mechanical systems are derived and other documented methods for parameter identification of mechanical systems are discussed. An investigation is undertaken into the principle that the impulse response of a system may be obtained when a test signal with an impulsive autocorrelation is injected into the system. The theory of using correlation functions to determine the numerical impulse response of a system is presented. Suitable test signals, pseudorandom binary sequences (PRBS) are analysed, and their generation and properties are discussed. Simulations are presented as to how the various properties of the PRBS test signals influence the resulting impulse response curve. Further simulations are presented that demonstrate how PRBS-based tests in conjunction with a curve-fitting method, in this case the method of linear least squares, can provide a fair estimation of the parameters of a mechanical system. The implementation of a correlation based online testing routine on a PLC is presented. Results from these tests are reviewed and discussed. A SCADA system that has been designed is discussed and it is shown how this system allows the user to perform diagnostics on networked drives in a distributed automation system. Identification of other mechanical phenomena such as elasticity and the non-linearity introduced by the presence of backlash is also investigated.