Unsupervised Learning for 3D Ultrasonic Data Compression

Ultrasonic testing has been widely used for nondestructive applications because it is inexpensive and has high inspection accuracy. However, massive ultrasonic data generated from the signal acquisition platform limits the performance of ultrasonic applications. Therefore, ultrasonic data compression with high accuracy is significant. In this study, we propose to use unsupervised learning (UL) to compress 3D ultrasonic data. The UL learns principal latent patterns and removes redundant information in ultrasonic data. The latent patterns are used to compress the 3D ultrasonic data. In this study, we propose to use several UL methods: Principal Component Analysis (PCA), Incremental-PCA, Independent Component Analysis (ICA), and Exploratory Factor Analysis (EFA) to learn latent patterns in acquired 3D ultrasonic data. An automatic ultrasonic testbed platform is assembled for ultrasonic data acquisition. By using UL, for high compression performance, we obtained the average 4.25% compression ratio; for high reconstruction performance, we obtained the average 9.67% compression ratio. The reconstruction accuracy is measured using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).