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A housekeeping prognostic health management framework for microfluidic systems

Research output: Contribution to journalJournal articlepeer-review

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A housekeeping prognostic health management framework for microfluidic systems. / Khan, Haroon; Al-Gayem, Qais; Richardson, Andrew Mark David.

In: IEEE Transactions on Device and Materials Reliability, Vol. 17, No. 2, 01.06.2017, p. 438-449.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Khan, H, Al-Gayem, Q & Richardson, AMD 2017, 'A housekeeping prognostic health management framework for microfluidic systems', IEEE Transactions on Device and Materials Reliability, vol. 17, no. 2, pp. 438-449. https://doi.org/10.1109/TDMR.2017.2694227

APA

Vancouver

Khan H, Al-Gayem Q, Richardson AMD. A housekeeping prognostic health management framework for microfluidic systems. IEEE Transactions on Device and Materials Reliability. 2017 Jun 1;17(2):438-449. https://doi.org/10.1109/TDMR.2017.2694227

Author

Khan, Haroon ; Al-Gayem, Qais ; Richardson, Andrew Mark David. / A housekeeping prognostic health management framework for microfluidic systems. In: IEEE Transactions on Device and Materials Reliability. 2017 ; Vol. 17, No. 2. pp. 438-449.

Bibtex

@article{e5bb2d1fddb547dfae6dcfc75f112dd8,
title = "A housekeeping prognostic health management framework for microfluidic systems",
abstract = "Micro-Electro-Mechanical Systems (MEMS) and Microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.",
keywords = "Electrodes, Oscillators, reliability, Microfluidics, Prognostics, health management, Sensors ",
author = "Haroon Khan and Qais Al-Gayem and Richardson, {Andrew Mark David}",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2017",
month = jun,
day = "1",
doi = "10.1109/TDMR.2017.2694227",
language = "English",
volume = "17",
pages = "438--449",
journal = "IEEE Transactions on Device and Materials Reliability",
issn = "1530-4388",
publisher = "IEEE",
number = "2",

}

RIS

TY - JOUR

T1 - A housekeeping prognostic health management framework for microfluidic systems

AU - Khan, Haroon

AU - Al-Gayem, Qais

AU - Richardson, Andrew Mark David

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2017/6/1

Y1 - 2017/6/1

N2 - Micro-Electro-Mechanical Systems (MEMS) and Microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.

AB - Micro-Electro-Mechanical Systems (MEMS) and Microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.

KW - Electrodes

KW - Oscillators

KW - reliability

KW - Microfluidics

KW - Prognostics

KW - health management

KW - Sensors

U2 - 10.1109/TDMR.2017.2694227

DO - 10.1109/TDMR.2017.2694227

M3 - Journal article

VL - 17

SP - 438

EP - 449

JO - IEEE Transactions on Device and Materials Reliability

JF - IEEE Transactions on Device and Materials Reliability

SN - 1530-4388

IS - 2

ER -