Multiple Changepoint Analysis of Noisy Nonlinear Data with an Application to Modeling Crack Growth in Additively Manufactured Titanium

Published

December 5, 2019

Author(s)

Lucas N. Koepke, Jolene D. Splett, Timothy P. Quinn, Nikolas W. Hrabe, Jake T. Benzing, Michael R. Frey

Abstract

Noisy measurement data pose a challenge for changepoint analysis, especially in the presence of multiple changepoints and when the model is nonlinear. We explore various approaches to estimating changepoints and their standard errors under these conditions. We consider whether adding a monotonicity constraint improves the changepoint estimates and reduces their standard errors. We finish with a novel application to material science using crack growth data from additively manufactured titanium. As cyclic loading is applied to a test specimen, crack growth can be classified into three regimes: slow-growth, mid-growth, and high-growth. We improve estimates of the transition points between these regimes versus those made by experts in the field by adding confidence bounds to the changepoint locations, allowing for designed experiments to study treatment effects on changepoint location.

Proceedings Title

JSM Proceedings

Conference Dates

July 28-August 1, 2019

Conference Location

Denver, CO

Conference Title

2019 Joint Statistical Meetings

Pub Type

Conferences

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Keywords

changepoint, isotonic regression, nonlinear least squares, pool-adjacent violators algorithm

Citation

Koepke, L. , Splett, J. , Quinn, T. , Hrabe, N. , Benzing, J. and Frey, M. (2019), Multiple Changepoint Analysis of Noisy Nonlinear Data with an Application to Modeling Crack Growth in Additively Manufactured Titanium, JSM Proceedings, Denver, CO, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928835 (Accessed December 15, 2024)