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Comparison of Vibration and Ultrasonic Signal Performance in Predicting the Remaining Useful Life of Rolling Element Bearings
Yousefizadeh, Emadaddin | 2024
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- Type of Document: M.Sc. Thesis
- Language: English
- Document No: 57701 (58)
- University: Sharif University of Technology, International Campus, Kish Island
- Department: Science and Engineering
- Advisor(s): Behzad, Mehdi; Mohammadi, Somayeh
- Abstract:
- The prediction of Remaining Useful Life (RUL) has become a critical tool in enhancing the availability of rotating machinery. By knowing the remaining time before a bearing failure occurs during operation, significant reductions in unplanned production downtimes and associated costs can be achieved. In this study, by analyzing the time signals of vibration and ultrasonic data related to the bearing of a 4.1 MW electromotor from installation to replacement, and by investigating the behavior trends and correlation between extracted features, RMS has been ultimately identified as the health indicator in both vibration and ultrasonic techniques for monitoring the bearing’s condition. First, using Linear Regression in three different scenarios, the RMSE has been calculated, and the scenario with the lowest error has been selected as the optimal one. Next, using a support vector regressor (SVR) model, two scenarios have been considered: in the first scenario, the vibration RMS values have been used as input, and the output has also been the predicted vibration RMS; in the second scenario, the simultaneous vibration and ultrasonic RMS values have been used as input, and the output has remained the predicted vibration RMS. Finally, by comparing the optimal case between Linear Regression and SVR, it was determined that SVR, with an RMSE of 0.147, has the lowest error, achieved by using both vibration and ultrasound signals as inputs
- Keywords:
- Rotating Machinery ; Rolling Element Bearings (REBs) ; Vibrational Analysis ; Remaining Useful Life ; Support Vector Machine (SVM) ; Bootstrapping ; Ultrasonic-Vibration
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