A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors

Hadi Salehi; Saptarshi das; Shantanu Chakrabartty; Subir Biswas

doi:10.1117/12.2295990

A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors

Hadi Salehi
, Saptarshi das
, Shantanu Chakrabartty
, Subir Biswas

Michigan State University
Washington University in St. Louis

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
DOIs	https://doi.org/10.1117/12.2295990
State	Published - Mar 2018
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Disciplines

Civil Engineering
Structural Engineering

Access to Document

10.1117/12.2295990License: Unspecified

Cite this

A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors. / Salehi, Hadi; das, Saptarshi; Chakrabartty, Shantanu et al.
In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 03.2018.

Research output: Contribution to journal › Article › peer-review

@article{ec8c6a6793ea443baf5d8f83a70a836d,

title = "A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors",

author = "Hadi Salehi and Saptarshi das and Shantanu Chakrabartty and Subir Biswas",

note = "This study presents a novel methodology for structural health monitoring (SHM), using a self-powered sensing concept, within the context of machine learning (ML) and pattern recognition (PR). The proposed method is based on the interpretation of data provided by a self-powered discrete analog wireless sensor used to measure the structural response along with an energy-efficient pulse switching technology employed for data communication.",

year = "2018",

month = mar,

doi = "10.1117/12.2295990",

language = "American English",

journal = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018",

}

TY - JOUR

T1 - A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors

AU - Salehi, Hadi

AU - das, Saptarshi

AU - Chakrabartty, Shantanu

AU - Biswas, Subir

N1 - This study presents a novel methodology for structural health monitoring (SHM), using a self-powered sensing concept, within the context of machine learning (ML) and pattern recognition (PR). The proposed method is based on the interpretation of data provided by a self-powered discrete analog wireless sensor used to measure the structural response along with an energy-efficient pulse switching technology employed for data communication.

PY - 2018/3

Y1 - 2018/3

UR - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10598/105980X/A-methodology-for-structural-health-diagnosis-and-assessment-using-machine/10.1117/12.2295990.short?SSO=1

U2 - 10.1117/12.2295990

DO - 10.1117/12.2295990

M3 - Article

JO - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018

JF - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018

ER -

A methodology for structural health diagnosis and assessment using machine learning with noisy and incomplete data from self-powered wireless sensors

UN SDGs

Disciplines

Access to Document

Other files and links

Fingerprint

Cite this