A novel probabilistic approach for damage localization and prognosis including temperature compensation

Rajesh Kumar Neerukatti, Kevin Hensberry, Narayan Kovvali, Aditi Chattopadhyay

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.

Original languageEnglish (US)
Pages (from-to)592-607
Number of pages16
JournalJournal of Intelligent Material Systems and Structures
Volume27
Issue number5
DOIs
StatePublished - Mar 1 2016

Fingerprint

Cracks
Temperature
Fatigue of materials
Compensation and Redress
Aluminum
Crack propagation
Physics

Keywords

  • localization
  • optimization
  • particle filter
  • piezoelectric
  • prognosis
  • Structural health monitoring

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

Cite this

A novel probabilistic approach for damage localization and prognosis including temperature compensation. / Neerukatti, Rajesh Kumar; Hensberry, Kevin; Kovvali, Narayan; Chattopadhyay, Aditi.

In: Journal of Intelligent Material Systems and Structures, Vol. 27, No. 5, 01.03.2016, p. 592-607.

Research output: Contribution to journalArticle

@article{8bc726b072494a56acd3e2b42def9eb1,
title = "A novel probabilistic approach for damage localization and prognosis including temperature compensation",
abstract = "The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.",
keywords = "localization, optimization, particle filter, piezoelectric, prognosis, Structural health monitoring",
author = "Neerukatti, {Rajesh Kumar} and Kevin Hensberry and Narayan Kovvali and Aditi Chattopadhyay",
year = "2016",
month = "3",
day = "1",
doi = "10.1177/1045389X15575084",
language = "English (US)",
volume = "27",
pages = "592--607",
journal = "Journal of Intelligent Material Systems and Structures",
issn = "1045-389X",
publisher = "SAGE Publications Ltd",
number = "5",

}

TY - JOUR

T1 - A novel probabilistic approach for damage localization and prognosis including temperature compensation

AU - Neerukatti, Rajesh Kumar

AU - Hensberry, Kevin

AU - Kovvali, Narayan

AU - Chattopadhyay, Aditi

PY - 2016/3/1

Y1 - 2016/3/1

N2 - The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.

AB - The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.

KW - localization

KW - optimization

KW - particle filter

KW - piezoelectric

KW - prognosis

KW - Structural health monitoring

UR - http://www.scopus.com/inward/record.url?scp=84958172967&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958172967&partnerID=8YFLogxK

U2 - 10.1177/1045389X15575084

DO - 10.1177/1045389X15575084

M3 - Article

AN - SCOPUS:84958172967

VL - 27

SP - 592

EP - 607

JO - Journal of Intelligent Material Systems and Structures

JF - Journal of Intelligent Material Systems and Structures

SN - 1045-389X

IS - 5

ER -