Preprint Article Version 1 This version is not peer-reviewed

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Version 1 : Received: 6 August 2018 / Approved: 6 August 2018 / Online: 6 August 2018 (21:51:53 CEST)

A peer-reviewed article of this Preprint also exists.

de Oliveira, M.A.; Monteiro, A.V.; Vieira Filho, J. A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network. Sensors 2018, 18, 2955. de Oliveira, M.A.; Monteiro, A.V.; Vieira Filho, J. A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network. Sensors 2018, 18, 2955.

Journal reference: Sensors 2018, 18, 2955
DOI: 10.3390/s18092955

Abstract

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Subject Areas

SHM; Electromechanical Impedance; Piezoelectricity; Intelligent Fault Diagnosis; Machine Learning; CNN; Deep Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.