Causal dilated convolutional neural networks for automatic inspection of ultrasonic signals in non-destructive evaluation and structural health monitoring. (August 2021)

• Record Type: Journal Article
• Authors: Mariani, Stefano; Rendu, Quentin; Urbani, Matteo; Sbarufatti, Claudio
• Is Part Of: Mechanical systems and signal processing. Volume 157(2021)
• Publication Date: 2021-08
• Subjects: Ultrasound -- Guided waves -- Defect detection -- Deep learning -- WaveNet -- Causal dilated CNN; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107748
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: This paper presents a deep learning network that inspects ultrasonic signals for defect detection. The network is an adaptation of WaveNet, hence is based on causal dilated convolutional neural networks and residual blocks. The network is shown to outperform both a widely used conventional analysis method and some competing deep learning algorithms. The validation was performed on two datasets, one generated via finite element simulations on a steel plate, the other by actual experiments on a composite plate. Significant improvements over the conventional method are obtained especially when testing occurs at temperatures well outside the range in the training set of signals. Abstract: This paper presents a deep learning network that performs automatic detection of defects by inspecting full ultrasonic guided wave signals excited in plate structures. The findings show that the algorithm, which is an adaptation of WaveNet, and hence is based on causal dilated convolutional neural networks, is effectively able to learn features and/or patterns related to the presence of waves scattered from damage, thus eliminating the need for any feature engineering to be performed by human operators. The network outperformed the widely used conventional approach that combines the optimal baseline selection and baseline signal stretch compensation methods when tested on two different datasets. The first dataset consisted of finite element simulated Lamb wave signals acquired in a pitch-catch configuration on a steel plate across a 50 °C range of temperature variations, and the second was a publicly available experimental dataset of Lamb wave signals also acquired in pitch-catch mode on a composite plate with a 40 °C range of variations. The improvements over the conventional approach are particularly encouraging when analyzing signals at temperatures well outside the temperature range available in the set of baseline signals, hence suggesting that this class of algorithms can complement or substitute existing methods, especially when testing occurs at unseen environmental and operational conditions, or when the effects of sensor drift make conventional methods less effective.