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Found 10 papers, 2 papers with code
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations.
Zero-Shot Motor Health Monitoring by Blind Domain Transition
To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics.
Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping
The DA module transfers the accumulated knowledge in contrasting no-damage and damage cases in the source domain to the target domain, given only the target's no-damage case.
CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection
Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) methods for vibration-based damage diagnostics of civil structures has been utilized extensively due to the observed high performances in learning from data.
Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection
The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.
Generative Adversarial Networks for Data Generation in Structural Health Monitoring
The presented work in this paper shows that for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on.
Generative Adversarial Networks for Labelled Vibration Data Generation
As Structural Health Monitoring (SHM) being implemented more over the years, the use of operational modal analysis of civil structures has become more significant for the assessment and evaluation of engineering structures.
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs.
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures.
1D Convolutional Neural Networks and Applications: A Survey
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations.
