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Extra info for Ann Rpt - Forewarn of Failure - Critical Equip at Next-Gen Nuclear Powerplants
A fourth factor involves refinement of the research-class FORTRAN software implementation of the methodology by adding new high-level routines for partial automation of the PSDM analysis, rewriting modules for clear algorithmic flow, and combining related modules. A final reason for this accomplishment involves development of MatLabTM m-files to automate the search through PSDM results for many different parameter choices, and also to provide publication-quality plots of the results. These improvements led to a much more efficient use of the analyst’s time.
The middle plot shows the correlation dimension (D). The bottom plot shows the Kolmogorov entropy (K). Error bars in the middle and bottom plots correspond to the 95% confidence interval. See text for discussion. 32 Fig. 11. PSDM for the broken-rotor seeded-fault. Dataset #1 is for the nominal (no fault) state. Dataset #2 is for the 50% cut in one rotor bar. Dataset #3 is for the 100% cut in one rotor bar. Dataset #4 is for two cut rotor bars. Dataset #5 is for four cut rotor bars. The exponential rise in the severity of the seeded faults is shown as an almost linear rise (solid line) in the logarithm of all four dissimilarity measures (*) for the chosen set of phase-space parameters.
Second plot down shows skewness (solid) and kurtosis (- -) in the instantaneous torque. Third plot down shows the number of time steps per cycle in the torque. Bottom plot shows the lag in time steps, corresponding to the first zero in the autocorrelation function, which varies from zero to 539. See text for discussion. 43 Fig. 21. Conventional nonlinear measures of load torque data. Top plot shows the location (in time steps) of the first minimum in the mutual information function. The middle plot shows the correlation dimension (D).
Ann Rpt - Forewarn of Failure - Critical Equip at Next-Gen Nuclear Powerplants