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Journal Articles

Research on Advanced Prediction of Major Equipment Fatigue Load Based on Deep Learning

Accepted Manuscript
Bowen Yang, Chenxu Yang, Junzhou Huo, Xiangfeng Si
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation.
Paper No: NDE-23-1029
https://doi.org/10.1115/1.4065819
Published Online: June 24, 2024
View Article titled, Research on Advanced Prediction of Major Equipment Fatigue Load Based on Deep Learning
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Topics: Deep learning, Fatigue, Nondestructive evaluation, Stress, Convolutional neural networks, Errors, Fatigue life, Ablation (Vaporization technology), Aerospace engineering, Numerical analysis
Journal Articles

Deep Learning–Based Time-Series Classification for Robotic Inspection of Pipe Condition Using Non-Contact Ultrasonic Testing

Steven C. Hespeler, Hamidreza Nemati, Nihar Masurkar, Fernando Alvidrez, Hamidreza Marvi, Ehsan Dehghan-Niri
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. February 2024, 7(1): 011002.
Paper No: NDE-23-1023
https://doi.org/10.1115/1.4063694
Published Online: November 8, 2023
View Article titled, Deep Learning–Based Time-Series Classification for Robotic Inspection of Pipe Condition Using Non-Contact Ultrasonic Testing
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Topics: Inspection, Pipes, Robotics, Time series, Signals, Ultrasonic testing, Algorithms, Corrosion
Journal Articles

An Artificial Intelligence/Machine Learning Based Regression-Clustering-Sensor-Fusion Framework for Predicting Unmeasurable Time-Series Strain From Other Sensor Measurements

Subhasish Mohanty, Joseph Listwan
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. February 2022, 5(1): 011001.
Paper No: NDE-20-1073
https://doi.org/10.1115/1.4050592
Published Online: April 19, 2021
View Article titled, An Artificial Intelligence/Machine Learning Based Regression-Clustering-Sensor-Fusion Framework for Predicting Unmeasurable Time-Series Strain From Other Sensor Measurements
Open the PDF for An Artificial Intelligence/Machine Learning Based Regression-Clustering-Sensor-Fusion Framework for Predicting Unmeasurable Time-Series Strain From Other Sensor Measurements in another window
Topics: Artificial intelligence, Cycles, Fatigue testing, Machine learning, Sensors, Time series, Fatigue, Displacement