AIMC Topic: Vibration

Clear Filters Showing 71 to 80 of 154 articles

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...

A new intelligent bearing fault diagnosis model based on triplet network and SVM.

Scientific reports
Separating sensitive characteristic signals from original vibration data is an important challenge for rolling bearing fault diagnosis. Because it is difficult to obtain large number of damaged bearings, Rolling bearing fault datasets are often small...

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data.

Sensors (Basel, Switzerland)
Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve ...

Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps.

Sensors (Basel, Switzerland)
Vibration analysis is an established method for fault detection and diagnosis of rolling element bearings. However, it is an expert oriented exercise. To relieve the experts, the use of Artificial Intelligence (AI) techniques such as deep neural netw...

Neural Network Modeling and Dynamic Analysis of Different Types of Engine Mounts for Internal Combustion Engines.

Sensors (Basel, Switzerland)
This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimen...