Detection and Classification of Artificial Defects on Stainless Steel Plate for a Liquefied Hydrogen Storage Vessel Using Short-Time Fourier Transform of Ultrasonic Guided Waves and Linear Discriminant Analysis

Abstract

Liquefied hydrogen storage vessels (LHSVs) are vulnerable to surface-crack initiation, propagation, and fracture on their surfaces because they are under high-pressure, low-temperature conditions. Defects can also occur in the coatings of the storage containers used to prevent hydrogen permeation, and these lead to surface defects such as pitting corrosions. Together, these increase the probability of liquid hydrogen leaks and can cause serious accidents. Therefore, it is important to detect surface defects during periodic surface inspections of LHSVs. Among the candidate non-destructive evaluation (NDE) techniques, testing using guided waves (GWs) is effective for detecting surface defects. Because of the ability of GWs to travel long distances without significant acoustic attenuation, GW testing has attracted much attention as a promising structural monitoring technique for LHSVs. In this study, an ultrasonic NDE method was designed for detecting surface defects of 304SS plate, which is the main material used for fabricating LHSVs. It involves the use of linear discriminant analysis (LDA) based on short-time Fourier transform (STFT) pixel information produced from GW data. To accomplish this, the differences in the number of STFT pixels between sound and defective specimens were used as a major factor in distinguishing the two groups. Consequently, surface defects could be detected and classified with 97% accuracy by the newly developed pixel-based mapping method. This indicates that the newly developed NDE method with LDA can be used to detect defects and classify LHSVs as either sound or defective.

Steel Surface Defect Recognition: A Survey

“…For example, Yong-hao et al [72] enables the detection of longitudinal cracks on the surface of the continuous casting plate in a complex background by calculating the Fourier magnitude spectrum of each sub-band to obtain features with translational invariance. In addition, Hwang et al [73] used linear discriminant analysis using short-time Fourier transform pixel information generated from ultrasound guided wave data to achieve defect detection on 304SS steel plates. The Hough transform methods use the global features of the image to connect the edge pixels to form a regionally closed boundary.…”

Nondestructive Testing of Metal Cracks: Contemporary Methods and Emerging Challenges

“…Generating and analyzing S-scan images and employing Mask R-CNN models for image training and classification, their research has not only corroborated the model's accuracy but also enhanced detection efficiency. Additionally, to augment the defect detection capabilities of ultrasonic guided wave techniques, Hwang et al [56] employed a short-time Fourier transform for signal timefrequency analysis, extracting distinguishing features of samples with and without defects. Coupled with linear discriminant analysis for sample classification, their methodology demonstrates high accuracy in defect detection in 304 stainless steel plates, providing Additionally, to augment the defect detection capabilities of ultrasonic guided wave techniques, Hwang et al [56] employed a short-time Fourier transform for signal time-frequency analysis, extracting distinguishing features of samples with and without defects.…”

“…Additionally, to augment the defect detection capabilities of ultrasonic guided wave techniques, Hwang et al [56] employed a short-time Fourier transform for signal timefrequency analysis, extracting distinguishing features of samples with and without defects. Coupled with linear discriminant analysis for sample classification, their methodology demonstrates high accuracy in defect detection in 304 stainless steel plates, providing Additionally, to augment the defect detection capabilities of ultrasonic guided wave techniques, Hwang et al [56] employed a short-time Fourier transform for signal time-frequency analysis, extracting distinguishing features of samples with and without defects. Coupled with linear discriminant analysis for sample classification, their methodology demonstrates high accuracy in defect detection in 304 stainless steel plates, providing an effective nondestructive evaluation method for the safety assessment of such plates.…”

A Novel Adaptive Time-Frequency Filtering Approach to Enhance the Ultrasonic Inspection of Stainless Steel Structures

“…Manufacturing flaws (inclusions, voids or lack of fusion) are another factor of performance deterioration that could also exist in the welded joints. To ensure the safety and integrity of stainless steel structures, ultrasonic nondestructive testing techniques would be very helpful to detect, locate and size potential in-service cracks and manufacturing flaws [ 6 , 7 ]. Especially, state-of-the-art additive manufacturing (AM) requires advanced ultrasonic NDT techniques to evaluate the integrity of AM components [ 8 ] and to design an inspection scheme based on the reliability and cost analysis of AM components [ 9 ].…”