Abstract:
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 correlationbased
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.
