
MILHDBK1823
This twoday short course is based on the new (2009) MILHDBK1823A "Nondestructive Evaluation System Reliability Assessment" and uses the mh1823 POD software. The course provides the latest methods for measuring your NDE system's effectiveness and the workshop will use these stateoftheart techniques to analyze your data. Course layout is reversechronological ? we discuss the analysis before we discuss how to design the experiment to produce the results we are analyzing. We will work through examples using real data, and time will be allocated for analyzing your enterprisespecific data.
Course Content Details ? Day One: 30 Years of Quantitative POD History (Understanding how we got here.) ► 1970s Have Cracks ? Will Travel ► Early 1980s ? Flight propulsion manufacturers? individual efforts to improve POD analysis ► Late 1980s ? USAF, UDRI, GEAE, P&W, and AlliedSignal (now Honeywell) working group produced MILHDBK1823, ?Nondestructive Evaluation System Reliability Assessment? draft. I was the editor and lead author. ► 1993 ? NATO AGARD sponsored 2day POD Short Course based on MILHDBK1823 that I presented in Ankara, Turkey, Lisbon, Portugal, Patras, Greece, and Ottawa, Canada. ► Late 1990s ? USAF officially publishes MILHDBK1823, 30 April, 1999 ► Early 2000s ? ModelAssisted POD gains a following ► February, 2007 ? Draft of revised and updated MILHDBK1823 released for comment, with allnew software incorporating the latest statistical best practices for NDE data. ► 7 April, 2009 ? The 2007 update was released by the USAF as MILHDBK1823A.
Probability and Confidence ► What is Probability? (Two incompatible definitions; both are correct) ► What is Probability of Detection? ► What is Confidence and how is that distinct from Probability? ► What is likelihood? How is it related to, but distinct from, probability? ► What does ?90/95? really mean? ● Are all methods for assessing ?sub>90/95 equally effective? (Answer: No.) ► 2 kinds of NDE data. (There are more, but this is a twoday course)
How to install the mh1823 POD software This shortcourse comes with a selfcontained CD with R installed along with the necessary ancillary R routines (RColorBrewer, rcom and RODBC), the installed mh1823 POD software, and the example datasets ? everything. You open the CD, drag the mh1823 POD icon to the desktop and you?re up and running. You only need to put the icon on the desktop once. Next time, just click the icon and begin. (We will, for completeness, spend some class time to demonstrate how to install R from the internet, and then how to install the mh1823 POD package.) How to analyze ?vs a data ► Background ● The ?ideal? POD(a) a curve ● Why ?vs a data is different from Hit/Miss data ● When ?/i> is less informative than simple Hit/Miss ► ?vs a Data Analysis ● Read ?vs a data ○ Preliminary Data Assessment: Plot the data and choose the best ?vs a model. ● Build the ?vs a linear model ○ Four ?vs a Requirements (Warning: If any of these assumptions is false, or, if the model is a line and the data describe a curve, then the subsequent POD analysis will be wrong even though the computational steps are correct.) ● How to go from ?vs a to POD vs a ? The Delta Method ○ Compute the transition matrix from ?vs a to POD vs a ○ The POD(a) Curve ● Wald method to compute ?vs a confidence bounds ○ Plot POD(a); compute POD confidence bounds ► Classwork ? ● Analyze a simple ?vs a example. ● Effects of analysis decisions on a_{90/95}
How to Analyze ?vs a data with Repeated Measures (Multiple inspections of the same Target Set) ► Why repeated measures are not simply ?more data? ● Red apples and green apples
► Special Situations ● How to recognize pathological ?vs a data (which is unfortunately common) ● Special difficulties with FieldFinds ? When mh1823 methods are not enough
How to Analyze Noise ► Understanding Noise ► Definition of Noise ► Choosing a probability density to describe the noise ► False Positive Analysis (with ?vs a data) ● Noise analysis and the Combined ?vs a Plot ● The POD(a) Curve ● Miscellaneous mh1823 POD algorithms ► Analyze the noise; compute the falsepositive rate
Analysis of enterprisespecific ?vs a Data ► Handson individual POD problemsolving
Day Two: How to analyze Binary (Hit/Miss) Data ► Understanding binary data ? why ordinary regression methods fail ► Read Hit/Miss data ► Build the GLM (Generalized Linear Model) ● Understanding Generalized Linear Models ● Choosing Link Functions ► Hit/Miss Confidence Bounds ● Not all statistical confidence methods are equally accurate ● How the LogLikelihood Ratio Criterion Works ● How to compute likelihood ratio confidence bounds ● Constructing Hit/Miss Confidence Bounds ► Classwork ? ● Analyze a simple Hit/Miss example. ● Effects of Hit/Miss analysis decisions on a_{90/95} ► Special Situations ● Choosing an Asymmetric Link Function ● How to analyze Repeated Measures ● How to analyze Disparate Data correctly ● How to analyze Hit/Miss Noise ● How to recognize Hit/Miss pathological data
Analysis of enterprisespecific Hit/Miss Data Statistical Design Of eXperiments (DOX) ► What is Statistical Experimental Design? ► Variable types ► Nuisance variables ► Objective of Experimental Design ► Factorial experiments ► Categorical variables ► Noise ? Probability of False Positive (PFP) ► How to Design an NDE Experiment ● Philosophy of NDE demonstrations ● How many specimens are enough? ● Specimen Design, Fabrication, Documentation, and Maintenance ○ Examples of NDE Specimens
Other Important Topics: ► False Positives, Sensitivity and Specificity ► Receiver Operating Characteristic (ROC) Curve ► ModelAssisted POD (MAPOD) ► Data that do not meet MILHDBK1823 requirements ● min(POD) > 0 or max(POD) < 1 ● Floor, Ceiling POD(a) models
Training Review & Course Wrapup

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