Sim-to-Real: Employing ultrasonic guided wave digital surrogates and transfer learning for damage visualization

Ultrasonics ◽  
2021 ◽  
Vol 111 ◽  
pp. 106338
Author(s):  
K. Supreet Alguri ◽  
Chen Ciang Chia ◽  
Joel B. Harley
Keyword(s):  
Transfer Learning ◽  
Guided Wave ◽  
Ultrasonic Guided Wave ◽  
Damage Visualization

Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves

2021 ◽  
pp. 147592172110107
Author(s):  
Bin Zhang ◽  
Xiaobin Hong ◽  
Yuan Liu
Keyword(s):  
Structural Health Monitoring ◽  
Transfer Learning ◽  
Health Monitoring ◽  
Component Analysis ◽  
Guided Wave ◽  
System Level ◽  
Structural Health ◽  
Ultrasonic Guided Wave ◽  
Generalization Problem ◽  
Cross Structure

Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system level, how to transfer a trained structural health monitoring model to another different structure remains a major challenge. In this article, a cross-structure ultrasonic guided wave structural health monitoring method based on distribution adaptation deep transfer learning is proposed to solve the feature generalization problem in different monitoring structures. First, the joint distribution adaptation method is employed to adapt both the marginal distribution and conditional distribution of the guided wave signals from different structures. Second, convolutional long short-term memory network is constructed to learn the mapping relationship from adapted training samples in source domain. Batch normalization layer is implemented to balance the input tensors of each sample to the same distribution. Finally, the multi-sensor damage indexes are utilized to visually present the damage by probability imaging. The experimental results show that proposed method can utilize the single-sensor monitoring data in one structure to implement the multi-sensor damage monitoring in another structure and achieve the damage imaging visualization. The imaging performance is significantly superior to the existing principal component analysis, transfer component analysis, and other state-of-art comparison methods.

Ultrasonic unilateral double-position excitation lamb wave defect detection and quantification method for ground electrode of transmission tower

2021 ◽  
pp. 1-15
Author(s):  
Kuan Ye ◽  
Kai Zhou ◽  
Ren Zhigang ◽  
Ruizhe Zhang ◽  
Chunsheng Li ◽  
...  
Keyword(s):  
Guided Waves ◽  
Power Transmission ◽  
Geometric Model ◽  
Guided Wave ◽  
Quantization Error ◽  
Dispersion Function ◽  
Stable Operation ◽  
Transmission Tower ◽  
Ultrasonic Guided Wave ◽  
Transmission Towers

The power transmission tower’s ground electrode defect will affect its normal current dispersion function and threaten the power system’s safe and stable operation and even personal safety. Aiming at the problem that the buried grounding grid is difficult to be detected, this paper proposes a method for identifying the ground electrode defects of transmission towers based on single-side multi-point excited ultrasonic guided waves. The geometric model, ultrasonic excitation model, and physical model are established, and the feasibility of ultrasonic guided wave detection is verified through the simulation and experiment. In actual inspection, it is equally important to determine the specific location of the defect. Therefore, a multi-point excitation method is proposed to determine the defect’s actual position by combining the ultrasonic guided wave signals at different excitation positions. Besides, the precise quantification of flat steel grounding electrode defects is achieved through the feature extraction-neural network method. Field test results show that, compared with the commercial double-sided excitation transducer, the single-sided excitation transducer proposed in this paper has a lower defect quantization error in defect quantification. The average quantization error is reduced by approximately 76%.

scholarly journals Phase-delayed laser diode array allows ultrasonic guided wave mode selection and tuning

2013 ◽  
Vol 113 (14) ◽  
pp. 144904 ◽  
Author(s):  
Pasi Karppinen ◽  
Ari Salmi ◽  
Petro Moilanen ◽  
Timo Karppinen ◽  
Zuomin Zhao ◽  
...  
Keyword(s):  
Laser Diode ◽  
Mode Selection ◽  
Wave Mode ◽  
Guided Wave ◽  
Diode Array ◽  
Laser Diode Array ◽  
Ultrasonic Guided Wave

scholarly journals Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview

2021 ◽  
Vol 11 (3) ◽  
pp. 1071
Author(s):  
Davide Bombarda ◽  
Giorgio Matteo Vitetta ◽  
Giovanni Ferrante
Keyword(s):  
Guided Waves ◽  
Structural Integrity ◽  
Diagnostic Procedures ◽  
Guided Wave ◽  
Rolling Stock ◽  
Technical Literature ◽  
Track Maintenance ◽  
Rail Network ◽  
Ultrasonic Guided Wave ◽  
Maintenance And Inspection

Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described.

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