Probabilistic modeling and sizing of embedded flaws in nondestructive inspections for fatigue damage prognostics and structural integrity assessment

一种疲劳寿命预测、探测概率的技术,应用在概率网络、处理探测的响应信号、测量装置等方向,能够解决安全性因素依赖、检测人员不是轻松任务等问题

Inactive Publication Date: 2015-11-11
SIEMENS AG +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the determination of security factors basically relies on experience and expert judgment, which is not an easy task for inspectors such as engineers without strong domain knowledge

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  • Probabilistic modeling and sizing of embedded flaws in nondestructive inspections for fatigue damage prognostics and structural integrity assessment
  • Probabilistic modeling and sizing of embedded flaws in nondestructive inspections for fatigue damage prognostics and structural integrity assessment
  • Probabilistic modeling and sizing of embedded flaws in nondestructive inspections for fatigue damage prognostics and structural integrity assessment

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Embodiment Construction

[0022] Exemplary embodiments of the invention described herein generally include a systematic approach to probabilistic life prediction from non-destructive examination (hereinafter "NDE") detection and fatigue life parameters that takes uncertainty into account. While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example only in the drawings and will be described in detail herein. It should be understood, however, that there is no intent to limit the invention to the particular forms disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Additionally, the disclosures of US Patent Publication No. 2013 / 0268214, Application No. 13 / 855,130, titled "PROBABILISTIC FATIGUELIFE PREDICTION USING ULTRASONIC INSPECTION DATA CONSIDERINGE IFSUNCERTAINTY" are hereby incorporated by reference in their entiret...

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Abstract

A method for probabilistic fatigue life prediction using nondestructive testing data considering uncertainties from nondestructive examination (NDE) data and fatigue model parameters. The method utilizes uncertainty quantification models for detection, sizing, fatigue model parameters and inputs. A probability of detection model is developed based on a log-linear model coupling an actual flaw size with a nondestructive examination (NDE) reported size. A distribution of the actual flaw size is derived for both NDE data without flaw indications and NDE data with flaw indications by using probabilistic modeling and Bayes theorem. A turbine rotor example with real world NDE inspection data is presented to demonstrate the overall methodology.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 61 / 748,846, filed January 4, 2013, entitled "PROBABILISTIC MODELINGAND QUANTIFICATIONOFEMBEDDEDFLAWSINULTRASONICNON-DESTRUCTIVEINSPECTIONANDITSAPPLICATION," which is incorporated herein by reference in its entirety, and This application claims the benefit of its priority. technical field [0003] This patent application relates to a method for probabilistic fatigue life prediction based on non-destructive examination (NDE) data and fatigue model parameters considering uncertainty. Background technique [0004] Steel and alloy structures are fundamental parts of civil, aerospace, marine and power generation systems. Since the early 1970s, non-destructive examination (hereinafter, "NDE") and non-destructive (hereinafter, "NDT") techniques have been effective measures to assess the manufacturing quality and operational integr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N29/44G01N3/32G06F17/18
CPCG06N7/005G01N29/4472G01N2203/0066G01N2203/0073G01N2203/0214G01N2291/0258G06F17/5009G06F30/20G06N7/01
Inventor 关雪飞张竞丹周少华K.卡多Y.郭E.M.拉塞尔科德W.A.阿巴西C-S.李A.L.刘易斯S.H.拉德凯
Owner SIEMENS AG
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