Rolling element bearing condition monitoring using acoustic emission technique
| dc.contributor.author | Hemmati, F. | |
| dc.contributor.author | Orfali, W. | |
| dc.date.accessioned | 2021-12-23T07:06:31Z | |
| dc.date.accessioned | 2023-08-23T05:12:20Z | |
| dc.date.available | 2021-12-23T07:06:31Z | |
| dc.date.available | 2023-08-23T05:12:20Z | |
| dc.date.issued | 2012 | |
| dc.description | Condition monitoring of heavy rotating machinery and equipment such as turbines, compressors and generators, is gaining importance in various industries since it keeps the plant at healthy condition for maximum production; helps in detecting faults at early stages; avoid serious accidents and damage; and reduces downtime. Bearings are the common elements used in heavy rotating machinery and equipment because of their high reliability. Bearings start to malfunction due to machine overload, shaft misalignment, rotor unbalance, overheating, etc. Many different techniques based on vibration methods have been developed to extract bearing fault features [1]. However, vibration signals are not sensitive to incipient faults and they usually masked by background noise caused by mechanical vibration signals from rotating machinery. Hence, it is normally difficult for the vibration techniques to detect bearing faults at an early stage. | en_US |
| dc.description.abstract | Acoustic emission (AE) signals generated from defects in rolling element bearings are investigated analytically and experimentally in this paper. Roller element bearings are crucial parts of many machines and there has been an increasing demand for effective and reliable health monitoring of these data elements and for finding optimum procedures for the signal processing of the measured data to detect and diagnose the size and location of incipient defects in rolling element bearings. This paper describes a novel signal processing algorithm designed to diagnose localized defects on rolling element bearings components which has recently been tested for different operating speeds and loadings. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transforms and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals under different operating conditions. Also, the effects of defect size, operating speed, and loading conditions on AE burst duration have also been investigated to estimate the fault size on the outer race | en_US |
| dc.identifier.citation | Hemmati, F., Orfali, W., & Gadala, M. S. (2012). Rolling element bearing condition monitoring using acoustic emission technique. In ISMA Conference on Advanced Acoustics and Vibration Engineering (pp. 699-714). | en_US |
| dc.identifier.uri | https://dspace-uat.adu.ac.ae/handle/1/1922 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Acoustic emission | en_US |
| dc.subject | Element method | en_US |
| dc.title | Rolling element bearing condition monitoring using acoustic emission technique | en_US |
| dc.title.alternative | In ISMA Conference on Advanced Acoustics and Vibration Engineering | en_US |
| dc.type | Article | en_US |