Volume 24, Issue 11 (November 2024)
Modares Mechanical Engineering 2024, 24(11): 103-107 |
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Detection of Rolling Bearing Faults Using Vibration Signals and Radial Basis Function Neural Network. Modares Mechanical Engineering 2024; 24 (11) :103-107
URL: http://mme.modares.ac.ir/article-15-79282-en.html
URL: http://mme.modares.ac.ir/article-15-79282-en.html
Detection of Rolling Bearing Faults Using Vibration Signals and Radial Basis Function Neural Network
Abstract: (435 Views)
Fualts in rolling bearings are one of the main reasons for the failure of rotating machinery. faults detection rolling bearing has played an essential role in the reliable performance of production units. In addition, condition monitoring of machinery using vibration analysis is one of the most powerful tools in measuring the health of mechanical systems. This research proposes an intelligent system for detecting defects in rolling bearings based on vibration analysis. In the intelligent faults detection system, the extracted features of the vibration signals in the time domain and the radial basis function neural network are used. The train and test datasets are presented to the radial basis function neural network intelligent system. The results of neural network learning show the very successful performance of the intelligent fault diagnosis system in detecting the health state and triple fault states of rolling bearings
Keywords: Fault Diagnosis, Rolling Bearing, Vibration Analysis, Radial Basis Function Neural Network, Feature Extraction
Article Type: Original Research |
Subject:
Micro & Nano Systems
Received: 2025/02/1 | Accepted: 2024/10/22 | Published: 2024/10/22
Received: 2025/02/1 | Accepted: 2024/10/22 | Published: 2024/10/22
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