An experimental study of machine-learning-driven temperature monitoring for Printed Circuit Boards (PCBs) using ultrasonic guided waves
An experimental study of machine-learning-driven temperature monitoring for Printed Circuit Boards (PCBs) using ultrasonic guided waves
Temperature has a significant impact on the operational lifetime of electronic components, as excessive heat can lead to accelerated degradation and ultimately failure. In safety-critical applications, it is important that real-time monitoring is employed to reduce the risk of system failures and maintain the safety, reliability, and integrity of the connected systems. In the case of printed circuit boards (PCBs), it is often not feasible to install enough sensors to adequately cover all of the temperature sensitive components. In this study, we present a novel method for the temperature monitoring of PCBs using ultrasonic guided waves and machine learning techniques. Our approach utilizes a small number of low-cost, unobtrusive piezoelectric wafer active sensors (PWAS) sensors for propagating ultrasonic guided waves across a PCB. Through interaction with board features, the temperature of components can be predicted using multi-output regression algorithms. Our technique has been applied to three different PCBs, each with five hotspot positions, achieving an RMSE of <3.5 °C and
Yule, Lawrence
87c4d44f-a50a-4ae4-8084-50de55b9a24c
Harris, Nicholas
237cfdbd-86e4-4025-869c-c85136f14dfd
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
1 January 2025
Yule, Lawrence
87c4d44f-a50a-4ae4-8084-50de55b9a24c
Harris, Nicholas
237cfdbd-86e4-4025-869c-c85136f14dfd
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
Yule, Lawrence, Harris, Nicholas, Hill, Martyn and Zaghari, Bahareh
(2025)
An experimental study of machine-learning-driven temperature monitoring for Printed Circuit Boards (PCBs) using ultrasonic guided waves.
NDT, 3 (1).
(doi:10.3390/ndt3010001).
Abstract
Temperature has a significant impact on the operational lifetime of electronic components, as excessive heat can lead to accelerated degradation and ultimately failure. In safety-critical applications, it is important that real-time monitoring is employed to reduce the risk of system failures and maintain the safety, reliability, and integrity of the connected systems. In the case of printed circuit boards (PCBs), it is often not feasible to install enough sensors to adequately cover all of the temperature sensitive components. In this study, we present a novel method for the temperature monitoring of PCBs using ultrasonic guided waves and machine learning techniques. Our approach utilizes a small number of low-cost, unobtrusive piezoelectric wafer active sensors (PWAS) sensors for propagating ultrasonic guided waves across a PCB. Through interaction with board features, the temperature of components can be predicted using multi-output regression algorithms. Our technique has been applied to three different PCBs, each with five hotspot positions, achieving an RMSE of <3.5 °C and
Text
ndt-03-00001
- Version of Record
More information
Accepted/In Press date: 16 December 2024
Published date: 1 January 2025
Identifiers
Local EPrints ID: 498494
URI: http://eprints.soton.ac.uk/id/eprint/498494
PURE UUID: 059bcd2c-9c67-4e3b-9c46-74d35eb38a15
Catalogue record
Date deposited: 20 Feb 2025 17:37
Last modified: 21 Feb 2025 02:54
Export record
Altmetrics
Contributors
Author:
Lawrence Yule
Author:
Nicholas Harris
Author:
Bahareh Zaghari
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics