|
Abstract:
|
In this dissertation , we investigate the theory and application of higher -order spectral analysis techniques to condition monitoring in shipboard electrical power systems . Monitoring and early detection of faults in rotating machines , such as induction motors , are essential for both preventive maintenance and to avoid potentially severe damage . As machines degrade , they often tend to become more nonlinear . This increased nonlinearity results in the introduction of new frequencies which satisfy particular frequency selection rules ; the exact selection rule depends on the order of the nonlinearity . In addition , the phases of the newly generated frequencies satisfy a similar phase selection rule . This results in a phase coherence , or phase coupling , between the “original” interacting frequencies and the “new” frequencies . This phase coupling is a true signature of nonlinearity . Since the classical auto -power spectrum contains no phase information , the phase coupling signature associated with nonlinear interactions is not available . However , various higher -order spectra (HOS ) are capable of detecting such nonlinear -induced phase coupling . The efficacy of the various proposed HOS -based methodologies is investigated using real -world vibration time -series data from a faulted induction motor driving a dc generator . The fault is controlled by varying a resistor placed in one phase of the three -phase line to the induction motor . First , we propose a novel method using a bispectral change detection (BCD ) for condition monitoring . Even though the bicoherence is dominant and powerful in the detection of phase coupling of nonlinearly interacting frequencies , it has some difficulties in its application to machine condition monitoring . Basically , the bicoherence may not be able to distinguish between intrinsic nonlinearities associated with healthy machines and fault -induced nonlinearities . Therefore , the ability to discriminate the fault -only nonlinearities from the intrinsic nonlinearities is very important . The proposed BCD method can suppress the intrinsic nonlinearities of a healthy machine by nulling them out and thereby identify the fault -only nonlinearities . In addition , most machines contain rotating components , and the vibration fields they generate are periodic . These periodic impulse train signals may produce artificially high bicoherence values and can lead to ambiguous indications of faults in machine condition monitoring . The proposed BCD method can remove the artificially high bicoherence values caused by periodic impulse -train signals . With these advantages , the proposed BCD method is a new and sensitive indicator for condition monitoring . Second , we propose a novel method to estimate , from a measured single time -series data record , complex coupling coefficients in order to quantify the “strength” of nonlinear frequency interactions associated with rotating machine degradation . The estimation of the coupling coefficients is based on key concepts from higher -order spectral analysis and least mean -square -error analysis . The estimated coupling coefficients embody the physics of the nonlinear interactions associated with machine degradation and provide a quantitative measure of the “strength” of the nonlinear interactions . In addition , as an auto -quantity method utilizing a single time -series data record , the proposed method adds supplemental fault signature information to conventional tools . Such knowledge has the potential to advance the state -of -the -art of machine condition monitoring . Third , we propose a bispectral power transfer analysis methodology to quantify power transfer between nonlinearly interacting frequency modes associated with machine degradation . Our proposed method enables us to identify the relative amounts of power transferred by various nonlinear interactions , and thereby identify the predominant interactions . Such knowledge provides important new signature , or feature , information for machine condition monitoring diagnostics . |