Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings
This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8541 |
| _version_ | 1797496418314747904 |
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| author | Xiaoqing Yuan Naqash Azeem Azka Khalid Jahanzeb Jabbar |
| author_facet | Xiaoqing Yuan Naqash Azeem Azka Khalid Jahanzeb Jabbar |
| author_sort | Xiaoqing Yuan |
| collection | DOAJ |
| description | This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically. |
| first_indexed | 2024-03-10T03:03:20Z |
| format | Article |
| id | doaj.art-0a66621501944a76b9fced769bea77e0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:03:20Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0a66621501944a76b9fced769bea77e02023-11-23T12:41:21ZengMDPI AGApplied Sciences2076-34172022-08-011217854110.3390/app12178541Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller BearingsXiaoqing Yuan0Naqash Azeem1Azka Khalid2Jahanzeb Jabbar3School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaMultan College of Arts, Bahauddin Zakariya University, Multan 60000, PakistanSchool of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710072, ChinaThis study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically.https://www.mdpi.com/2076-3417/12/17/8541fault detectionIndustry 4.0roller bearingsstatistical analysisvibration energy at damage |
| spellingShingle | Xiaoqing Yuan Naqash Azeem Azka Khalid Jahanzeb Jabbar Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings Applied Sciences fault detection Industry 4.0 roller bearings statistical analysis vibration energy at damage |
| title | Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings |
| title_full | Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings |
| title_fullStr | Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings |
| title_full_unstemmed | Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings |
| title_short | Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings |
| title_sort | vibration energy at damage based statistical approach to detect multiple damages in roller bearings |
| topic | fault detection Industry 4.0 roller bearings statistical analysis vibration energy at damage |
| url | https://www.mdpi.com/2076-3417/12/17/8541 |
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