Industrial Bearing Fault Detection Using Time-Frequency Analysis
Journal Title: Engineering, Technology & Applied Science Research - Year 2018, Vol 8, Issue 4
Abstract
Time-frequency fault detection techniques were applied in this study, for monitoring real life industrial bearing. For this aim, an experimental test bench was developed to emulate the bearing rotating motion and to measure the induced vibration signals. Dedicated software was used to analyze the acquired measurements in the time-frequency domain using several distributions with varying resolution. Results showed that each fault type exhibits a specific behavior in the time-frequency domain, which is exploited in the localization of the faulty component.
Authors and Affiliations
Y. Bella, A. Oulmane, M. Mostefai
Keywords
Related Articles
- EP ID EP230810
- DOI -
- Views 253
- Downloads 0
Uterine EMG Signals Spectral Analysis for Pre-Term Birth Prediction
A methodology for prediction of pre-term births is presented in this paper. The methodology is based on the analysis of EHG signals and data mining techniques. Initially, spectral and non-linear characteristics of the EH...
Accuracy of Bit Error Probability for W-CDMA System Using Code Tree
W-CDMA is radio access utilized for 3G cell frameworks. A code tree allocation scheme is one of the most explored channelization techniques, used to improve system performance and capacity through adjustable data rates....
Prediction of Springback in the Air Bending Process Using a Kriging Metamodel
This paper addresses the use of the kriging‏ approach to predict the springback in the air bending process. The materials and the geometrical parameters, which significantly affect the springback, were considered as inpu...
A Novel Structure for Vector Control of Symmetrical Six-Phase Induction Machines with Three Current Sensors
Vector control is one of the most popular multiphase induction machine drive methods due to its dynamic performance. Since all phase currents are needed in vector control drive, by increasing phase numbers, the number of...
Evaluation of window size in classification of epileptic short-term EEG signals using a Brain Computer Interface software
The complexity of epilepsy created a fertile ground for further research in automated methods, attempting to help the epileptologists’ task. Over the past years, great breakthroughs have emerged in computer-aided analysi...