AIMC Topic: Vibration

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Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra.

The journal of physical chemistry. A
Ultrafast spectroscopy often involves measuring weak signals and long data acquisition times. Spectra are typically collected as a "pump-probe" spectrum by measuring differences in intensity across laser shots. Shot-to-shot intensity fluctuations are...

Comparative Study of Structural Anomaly Diagnosis Based on ANN Model Using Random Displacement and Acceleration Responses with Incomplete Measurements.

Sensors (Basel, Switzerland)
Structural anomaly diagnosis, such as damage identification, is a continuously interesting issue. Artificial neural networks have an excellent ability to model complex structure dynamics. In this paper, an artificial neural network model is used to d...

A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

Journal of speech, language, and hearing research : JSLHR
PURPOSE: Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details....

Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network.

Sensors (Basel, Switzerland)
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance ...

Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment.

Sensors (Basel, Switzerland)
CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal featu...

An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network.

Computational intelligence and neuroscience
In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the...

Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method.

Sensors (Basel, Switzerland)
With the rapid development of underground infrastructure and the uncertainty of its location, the possibility of damage due to nearby construction has increased. Thus, for the early warning of dangerous construction behaviors around underground facil...

Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling Method.

Sensors (Basel, Switzerland)
An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there ...

Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items.

Sensors (Basel, Switzerland)
In recent years, rotating machinery fault diagnosis methods based on convolutional neural network have achieved much success. However, in real industrial environments, interfering signals are unavoidable, which may reduce the accuracy of fault diagno...

Prediction of Automobile Wiper Motor Noise Based on Support Vector Machine with Vibration Sensors.

Computational intelligence and neuroscience
Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehi...