A deep learning approach for anomaly identification in PZT sensors using point contact method

Abstract

The implementation of piezoelectric sensors is degraded due to surface defects, delamination, and extreme weathering conditions, to mention a few. The sensor needs to be diagnosed before the efficacious implementation in the structural health monitoring (SHM) framework. A novel experimental method based on Coulomb coupling is utilised to visualise the evolution of elastic waves and interaction with the surface anomaly in the Lead Zirconate Titanate (PZT) substrate. Machine learning is expeditiously becoming an essential technology for scientific computing, with several possibilities to advance the field of structural health monitoring (SHM). This study employs a deep learning-based autoencoder neural network in conjunction with image registration and PSNR to diagnose the surface anomaly in the PZT substrate. The autoencoder extracts the significant damage-sensitive features from the complex waveform big data. The autoencoder provides a non-linear input-output model that is well suited for the non-linear interaction of the wave with the surface anomaly and boundary of the substrate. The mean absolute error (MAE) in the reconstruction of the sequential signal from the autoencoder network is evaluated. The MAEs are sensitive to the anomaly that lies in the PZT substrate. Image registration is leveraged to overcome the challenge due to the offset in the data. Further, the localisation and quantification of the anomaly are performed by computing PSNR values. This work proposes an advanced, efficient damage detection algorithm in the scenario of big data that is ubiquitous in SHM.