ORALS
SESSION:
AdvancedMaterialsWedPM2-R5
| 7th Intl Symposium on New & Advanced Materials and Technologies for Energy, Environment, Health and Sustainable Development |
| Wed. 29 Nov. 2023 / Room: Heliconia | |
| Session Chairs: Sanjeev Khanna; Malgorzata Gradzka-Dahlke; Session Monitor: TBA |
16:00: [AdvancedMaterialsWedPM209] OL
BEARING REMAINING USEFUL LIFE PREDICTION UNDER STARVED LUBRICATING CONDITION USING TIME DOMAIN ACOUSTIC EMISSION SIGNAL PROCESSING Mohsen Motahari-Nezhad1 ;
1Technical and Vocational University, Zabol, Iran;
Paper Id: 254
[Abstract] Condition monitoring means troubleshooting and maintenance of machines without interruption in their operation and is performed based on accurate information obtained from the equipment status [1]. The basis for condition monitoring is troubleshooting and the prediction of the fault occurring without causing the machine to stop working [2]. There are four general strategies for fault prediction, namely experience-based methods, statistical modeling, artificial intelligence methods, and physical modeling [3].In this paper, the estimation of the remaining useful life (RUL) of angular contact ball bearing using time-domain signal processing method is discussed. An experimental setup based on acoustic emission (AE) signal is used to extract and collect the desired data. The residual life test is performed on the SKF 7202 BEP angular contact ball bearing. Sixty-time domain features have been introduced and used for fault detection. Improved Distance Evaluation (IDE) method has been used for feature dimensionality reduction and the best 10 features have been selected. K-Nearest Neighbors (KNN) algorithm has been used to investigate the classification accuracy of IDE based on selected features for classifying healthy and faulty bearings. The results show that the IDE method enables natural fault detection in bearings with high precision. To validate the performance of the KNN classifier, performance indices such as accuracy, precision, and specificity are applied. The results show that kurtosis, FM4, k factor, energy, and peak are the best features and kurtosis has the highest KNN rank with accuracy, precision, and specificity of 97%, 93%, and 94%, respectively.
References:
[1] Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
[2] Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836.
[3] Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. New Jersey: John Wiley & Sons.
