CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 16; p. 7059
• Main Authors: Shang, Li, Zhang, Zi, Tang, Fujian, Cao, Qi, Pan, Hong, Lin, Zhibin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023; MDPI
• Subjects: Accuracy; Classification; CNN-LSTM hybrid model; Corrosion; Corrosion and anti-corrosives; damage identification; Data processing; data-driven approach; Datasets; Deep learning; Lamb wave; Machine learning; Neural networks; Pattern recognition systems; Pipe lines; Signal processing; structural health monitoring; Time series; Taiwan
• DOI: 10.3390/s23167059
• ISSN: 1424-8220

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise.