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

de Oliveira, Mario A. and Monteiro, Andre and Vieira Filho, Jozue (2018) A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network. Sensors, 18 (9). p. 2955. ISSN 1424-8220

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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 four 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.

Item Type:	Article
Identification Number:	10.3390/s18092955
Dates:	Date Event 2 September 2018 Accepted 5 September 2018 Published Online
Uncontrolled Keywords:	SHM, electromechanical impedance, piezoelectricity, intelligent fault diagnosis, machine learning, CNN, deep learning
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User:	Gemma Tonks
Date Deposited:	29 Sep 2023 13:15
Last Modified:	20 Jun 2024 11:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14800

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