A Web-Based Data Analysis Methodology for Estimating Reliability of Weld Flaw Detection, Location, and Sizing

Abstract

Recent advances in computer technology, internet communication networks, and finite element modeling and analysis capability have made it feasible for engineers to accelerate the feedback loop between the field inspectors of a structure or component for critical flaws by nondestructive evaluation (NDE) and the office engineers who do the damage assessment and recommendations for field action to prevent failure. For example, field inspection data of critical flaws can be transmitted to the office instantly via the internet, and the office engineer with a computer database of equipment geometry, material properties, past loading/deformation histories, and potential future loadings, can process the NDE data as input to a damage assessment model to simulate the equipment performance under a variety of loading conditions until its failure. Results of such simulations can be combined with engineering judgment to produce a specific recommendation for field action, which can also be transmitted to the field by the internet. In this paper, we describe a web-based NDE data analysis methodology to estimate the reliability of weld flaw detection, location, and sizing by using a public-domain statistical data analysis software named DATAPLOT and a ten-step sensitivity analysis of NDE data from a two-level fractional factorial orthogonal experimental design. A numerical example using the 1968 ultrasonic examination data of weld seam in PVRC test block 251J, and the 1984 sectioning data of 251J containing 15 implanted flaws, is presented and discussed.

A Risk-Uncertainty Formula Accounting for Uncertainties of Failure Probability and Consequence in a Nuclear Powerplant

“…Finally, we note that the variance formula given by Ku [79] and used in this paper to derive the risk uncertainty formula has also been applied by Fong and his colleagues [82][83][84][85] in estimating uncertainty in fatigue life prediction or failure probability due to a number of source uncertainties such as:…”

Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data

“…In a 2008 PVP conference paper by Fong, Hedden, Filliben, and Heckert [32], a prototype web-based NDE data analysis methodology to estimate the reliability of weld flaw detection, location, and sizing was described with an example application to the 1968 ultrasonic testing (UT) data of weld seam in PVRC test block 251J and the 1984 sectioning data of the same block implanted with 15 flaws of 4 types.…”

“…In that 2008 example [32], five factors were identified as potential contributors of uncertainty to the UT process of Team A in finding an implanted 2-inch crack. Figures 10 and 11, the resulting analysis using judgment-based fictitious data concluded that the UT operator's experience (X1) and the transducer probe angle (X4) are the two dominant factors contributing to the uncertainty of the Team A's UT results.…”

“…not to include the initial crack depth as a variable to be simulated by the probabilistic model." Many NDE studies including ours [10,11,20,21,24,28,32,33] showed the uncertainty in the size of a detected crack to be somewhat large, so we decided to run the same PC-PRAISE code with a simple, straight 20% increase in N1, the initial crack depth parameter that were held constant in all previous calculations.…”

“…ISI AND NDE DATABASE DB-2 (CONT'D)Step 7 of a 10-step analysis showing the main and interaction effects of a 5-factor, 8-run, 2-level fractional factorial design-of-experiments-based exercise (k = 5, n = 8). Note X1 and X4 are dominant (after Fong, et al[32]).…”