Rotating Equipment Defect Detection Using the Algorithm of Mode Isolation
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Findings from a project involving rotating equipment defect detection using the Algorithm of Mode Isolation (AMI) are presented. The prototypical system evaluated is a rotating shaft, supported by hydrodynamic bearings at both ends, with one disk mounted to the shaft. Shaft cracks and bearing wear are the two equipment defects considered. An existing model of the prototypical system from the literature, termed the simplified model. is modified to simulate the presence of a transverse shaft crack at mid-span. This modified model is termed the standard model. Ritz series analysis, in conjunction with a previously published description of the compliance related to the presence of a transverse shaft crack, is used to describe the decrease in shaft stiffness associated with the crack. The directional frequency response function (dFRF) is shown in the literature to provide benefits over the standard frequency response function (FRF) in both system identification and shaft crack detection for rotating equipment. The existing version of AMI is modified to process dFRFs and termed Two-Sided AMI. The performance of Two-Sided AMI is verified through system identification work using both the simplified model and a rigid rotor model from the literature. The results confirm the benefits of using the dFRF for system identification of isotropic systems. AMI and Two-Sided AMI are experimental modal analysis (EMA) routines, which estimate modal properties based on a frequency domain expression of system response. Eigenvalues and associated modal residues are the modal properties considered in the present work. Three defect detection studies are fully described. In the first, the simplified model is used to investigate bearing wear detection. Various damage metrics related to the eigenvalue and the residue are evaluated. The results show that residue-based metrics are sensitive to bearing wear. Next, the standard model is used in an in-depth investigation of shaft crack detection. When a shaft crack is present, the standard model is time-varying in both the fixed and moving coordinate systems. Therefore, this analysis is also used to evaluate performing EMA on non-modal data. In addition to continuing the evaluation of various xiv damage metrics, the shaft crack study also investigates the effects of noise and coordinate system choice (fixed or moving) on shaft crack detection. Crack detection through EMA processing of noisy, non-modal data is found to be feasible. The eigenvalue-based damage metrics show promise. Finally, the standard model is used in a dual-defect study. The system is configured with both a shaft crack and a worn bearing. One defect is held constant while the magnitude of the other is increased. The results suggest that AMI is usable for defect detection of rotating machinery in the presence of multiple system defects, even though the response data is not that of a time-invariant system. The relative merits of both input data types, the FRF and the dFRF, are evaluated in each study.