Total integrated tube analysis
View Patent ↗The invention relates to improved systems and methods for inspecting the tubes of a steam generator of a nuclear reactor that involves modeling the steam generator, comparing signals of a tube from an eddy current sensor with aspects of the model to determine whether further analysis is required, employing primary and secondary analysis processes, and producing a combined report of the primary and secondary analysis results.
1. A method of non-destructively assessing a current condition of a number of tubes of a steam generator of a nuclear power plant, the method comprising:
(a) establishing a model of the steam generator that comprises a set of baseline parameters for each of a plurality of exemplary regions of interest (ROIs) of a number of the tubes of the steam generator;
(b) extracting a signal from each of a number of physical ROIs of a number the tubes;
(c) comparing the signal from a given physical ROI of a tube with the set of baseline parameters of the corresponding exemplary ROI of the model;
(d) triggering additional processing when at least a portion of the signal from the given physical ROI exceeds at least a portion of the set of baseline parameters of the corresponding exemplary ROI;
(e) employing a primary automated analysis code for conducting steps (a) through (d);
(f) generating a first set of results based on the primary automated analysis code;
(g) employing a different secondary automated analysis code for conducting steps (a) through (d);
(h) generating a second set of results based on the different secondary automated analysis code;
(i) combining the first set of results and the second set of results; and
(j) producing a single report including output from each of the first set of results and the second set of results generated by the primary automated analysis code and the different secondary automated analysis code, respectively, absent of duplicate entries.
2. The method of claim 1 , wherein the combining of the first and second sets of results in step (i) comprises, automatically comparing the first and second sets of results, identifying a common and duplicate entry generated by the primary automated analysis code and the different secondary automated analysis code, and selecting a single entry as output in the single report.
3. The method of claim 1 , wherein the combining of the first and second sets of results in step (i) comprises automatically comparing the first and second sets of results, identifying any entry that is reported by only one of the primary automated analysis code and the different secondary automated analysis code, and providing the entry as output in the single report.
4. The method of claim 1 , wherein prior to step (e), the method further comprises:
generating pattern recognition data;
generating adaptive threshold data; and
validating the pattern recognition and threshold data against each other.
5. The method of claim 1 , further comprising comparing the output in the single report with previous inspection results.
6. The method of claim 1 , further comprising verifying any gap in measurement or analysis.
7. The method of claim 1 , further comprising performing a check ensuring that all unusual noise signals are evaluated and entered into the single report.
8. The method of claim 1 , wherein one of the secondary and primary automated analysis codes is EADS and the other is RTAA.
9. The method of claim 1 , further comprising refraining from the triggering of additional processing with respect to a particular physical ROI when no portion of a signal from the particular physical ROI exceeds a set of baseline parameters of a corresponding exemplary ROI.
10. The method of claim 1 , further comprising triggering an additional notification if the at least portion of the signal from the given physical ROI exceeds the at least portion of the set of baseline parameters of the corresponding exemplary ROI by a predetermined amount.
11. The method of claim 1 , wherein the model further comprises an exception data set for each of one or more physical ROIs of each of one or more tubes of the number of tubes, each exception data set being representative of a preexisting signal of the physical ROI that exceeds the set of baseline parameters of the corresponding exemplary ROI, and wherein the triggering of additional processing comprises seeking in the model an exception data set for the given physical ROI.