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Journal Articles

Composite Fault Identification in Multi-Part Coaxial Structure Equipment Based on Convolutional Neural Network-Transformer Neural Network

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Xiaotian Bai, Jiafeng Zhu, Huaitao Shi, Chengying Zhao, Rongzheng Zou
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. November 2025, 8(4): 041001.
Paper No: NDE-24-1058
https://doi.org/10.1115/1.4068126
Published Online: April 3, 2025
View Articletitled, Composite Fault Identification in Multi-Part Coaxial Structure Equipment Based on Convolutional Neural Network-Transformer Neural Network
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Topics: Artificial neural networks, Composite materials, Convolutional neural networks, Signals, Vibration, Fault diagnosis, Bearings, Sensors, Feature extraction
Journal Articles

Probabilistic Deep Learning Approach for Fatigue Crack Width Estimation and Prognosis in Lap Joint Using Acoustic Waves

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Shivam Ojha, Amit Shelke
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. February 2025, 8(1): 011003.
Paper No: NDE-24-1018
https://doi.org/10.1115/1.4065550
Published Online: July 30, 2024
View Articletitled, Probabilistic Deep Learning Approach for Fatigue Crack Width Estimation and Prognosis in Lap Joint Using Acoustic Waves
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Topics: Artificial neural networks, Deep learning, Fracture (Materials), Uncertainty, Waves, Aluminum, Fatigue cracks, Acoustics
Journal Articles

Computed Tomography Artifact Reduction Employing a Convolutional Neural Network Within the Context of Dimensional Metrology

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Mahdi Ghafarzadeh, Mohammad Tavakoli Kejani, Mehdi Karimi, Amirreza Asadi
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): 011001.
Paper No: NDE-23-1033
https://doi.org/10.1115/1.4063805
Published Online: October 31, 2023
View Articletitled, Computed Tomography Artifact Reduction Employing a Convolutional Neural Network Within the Context of Dimensional Metrology
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Topics: Artificial neural networks, Computerized tomography, Dimensional metrology, Electromagnetic scattering, Hardening, X-rays, Phantoms, Errors
Journal Articles

Fault Diagnostics and Faulty Pattern Analysis of High-Speed Roller Bearings Using Deep Convolutional Neural Network

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Maan Singh Rathore, S. P. Harsha
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. May 2023, 6(2): 021006.
Paper No: NDE-22-1020
https://doi.org/10.1115/1.4062252
Published Online: May 12, 2023
View Articletitled, Fault Diagnostics and Faulty Pattern Analysis of High-Speed Roller Bearings Using Deep Convolutional Neural Network
Open the PDF for Fault Diagnostics and Faulty Pattern Analysis of High-Speed Roller Bearings Using Deep Convolutional Neural Network in another window
Topics: Artificial neural networks, Bearings, Vibration, Roller bearings
Journal Articles

Full Waveform Inversion-Based Ultrasound Computed Tomography Acceleration Using Two-Dimensional Convolutional Neural Networks

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Christopher Kleman, Shoaib Anwar, Zhengchun Liu, Jiaqi Gong, Xishi Zhu, Austin Yunker, Rajkumar Kettimuthu, Jiaze He
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. November 2023, 6(4): 041004.
Paper No: NDE-22-1034
https://doi.org/10.1115/1.4062092
Published Online: March 31, 2023
View Articletitled, Full Waveform Inversion-Based Ultrasound Computed Tomography Acceleration Using Two-Dimensional Convolutional Neural Networks
Open the PDF for Full Waveform Inversion-Based Ultrasound Computed Tomography Acceleration Using Two-Dimensional Convolutional Neural Networks in another window
Topics: Artificial neural networks, Computerized tomography, Inverse problems, Machine learning, Ultrasound, Waves, Signals
Journal Articles

Spindle Condition Monitoring With a Smart Vibration Sensor and an Optimized Deep Neural Network

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Louis Oh, Emil Pitz, Kishore Pochiraju
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. May 2023, 6(2): 021003.
Paper No: NDE-22-1011
https://doi.org/10.1115/1.4056616
Published Online: January 31, 2023
View Articletitled, Spindle Condition Monitoring With a Smart Vibration Sensor and an Optimized Deep Neural Network
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Topics: Artificial neural networks, Condition monitoring, Failure, Failure mechanisms, Sensors, Signals, Vibration
Journal Articles

Localized Damage Identification in the Last Stage Low-Pressure Steam Turbine Blade Using Dynamic Parameter Measurements

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Keshav Ramesh Shetkar, J. Srinivas
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. February 2023, 6(1): 011006.
Paper No: NDE-22-1021
https://doi.org/10.1115/1.4056312
Published Online: December 12, 2022
View Articletitled, Localized Damage Identification in the Last Stage Low-Pressure Steam Turbine Blade Using Dynamic Parameter Measurements
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Topics: Blades, Fracture (Materials), Steam turbines, Artificial neural networks, Vibration, Damage
Journal Articles

Uncertainty Measurement of the Prediction of the Remaining Useful Life of Rolling Bearings

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Hongchun Sun, Chenchen Wu, Zunyang Lei
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. August 2022, 5(3): 031007.
Paper No: NDE-21-1030
https://doi.org/10.1115/1.4054392
Published Online: May 10, 2022
View Articletitled, Uncertainty Measurement of the Prediction of the Remaining Useful Life of Rolling Bearings
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Topics: Artificial neural networks, Bearings, Service life (Equipment), Uncertainty, Rolling bearings, Network models, Maintenance
Journal Articles

Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques

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Aditya Sharma
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. August 2022, 5(3): 031004.
Paper No: NDE-21-1027
https://doi.org/10.1115/1.4053773
Published Online: March 1, 2022
View Articletitled, Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques
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Topics: Artificial intelligence, Bearings, Fault diagnosis, Support vector machines, Rolling bearings, Artificial neural networks, Rotation
Journal Articles

Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation With Deep Neural Networks

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Shweta Dabetwar, Stephen Ekwaro-Osire, João Paulo Dias
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. May 2022, 5(2): 021004.
Paper No: NDE-21-1014
https://doi.org/10.1115/1.4051947
Published Online: August 23, 2021
View Articletitled, Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation With Deep Neural Networks
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Topics: Damage, Data fusion, Artificial neural networks, Composite materials
Journal Articles

Machine Learning and Anomaly Detection Algorithms for Damage Characterization From Compliance Data in Three-Point Bending Fatigue

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Subodh Kalia, Jakob Zeitler, Chilukuri K. Mohan, Volker Weiss
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. November 2021, 4(4): 041011.
Paper No: NDE-20-1085
https://doi.org/10.1115/1.4051903
Published Online: August 11, 2021
View Articletitled, Machine Learning and Anomaly Detection Algorithms for Damage Characterization From Compliance Data in Three-Point Bending Fatigue
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Topics: Algorithms, Cycles, Damage, Failure, Fatigue, Machine learning, Testing, Stress, Multilayer perceptron, Artificial neural networks
Journal Articles

Vibration Characteristics Diagnosis and Estimation of Fault Sizes in Rolling Contact Bearings: A Model-Based Approach

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I. M. Jamadar
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): 011003.
Paper No: NDE-20-1091
https://doi.org/10.1115/1.4051176
Published Online: June 7, 2021
View Articletitled, Vibration Characteristics Diagnosis and Estimation of Fault Sizes in Rolling Contact Bearings: A Model-Based Approach
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Topics: Bearings, Rolling contact, Vibration, Artificial neural networks, Rollers, Design, Errors
Journal Articles

Efficient Global Sensitivity Analysis of Model-Based Ultrasonic Nondestructive Testing Systems Using Machine Learning and Sobol’ Indices

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Jethro Nagawkar, Leifur Leifsson
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. November 2021, 4(4): 041008.
Paper No: NDE-20-1063
https://doi.org/10.1115/1.4051100
Published Online: June 7, 2021
View Articletitled, Efficient Global Sensitivity Analysis of Model-Based Ultrasonic Nondestructive Testing Systems Using Machine Learning and Sobol’ Indices
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Topics: Algorithms, Artificial neural networks, Machine learning, Nondestructive evaluation, Sensitivity analysis, Physics, Testing
Journal Articles

Magnetic Barkhausen Noise Technique for Early-Stage Fatigue Prediction in Martensitic Stainless-Steel Samples

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Zi Li, Bharath Basti Shenoy, Lalita Udpa, Satish Udpa, Yiming Deng
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research Papers
ASME J Nondestructive Evaluation. November 2021, 4(4): 041004.
Paper No: NDE-20-1082
https://doi.org/10.1115/1.4050842
Published Online: April 28, 2021
View Articletitled, Magnetic Barkhausen Noise Technique for Early-Stage Fatigue Prediction in Martensitic Stainless-Steel Samples
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Topics: Fatigue, Noise (Sound), Artificial neural networks, Principal component analysis, Uncertainty, Stainless steel, Signals, Fatigue life
Journal Articles

A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics

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Madhumitha Ramachandran, Zahed Siddique
Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Publisher: ASME
Article Type: Research-Article
ASME J Nondestructive Evaluation. May 2019, 2(2): 024501.
Paper No: NDE-18-1034
https://doi.org/10.1115/1.4043191
Published Online: April 4, 2019
View Articletitled, A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics
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Topics: Artificial neural networks, Multilayer perceptron, Signals, Torque, Feature selection, Failure, Friction