Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach
Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS set...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017511 |
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| author | Gagandeep Sharma Tejbir Kaur Sanjay Kumar Mangal Amit Kohli |
| author_facet | Gagandeep Sharma Tejbir Kaur Sanjay Kumar Mangal Amit Kohli |
| author_sort | Gagandeep Sharma |
| collection | DOAJ |
| description | Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS setup is initially developed, comprising a Raspberry Pi 4B+ CPU module, a NucleoF401RET6 MCU, an OLED screen, and an Adxl1002z accelerometer for acquiring vibration signals at the desired sampling frequency stored in the CPU memory. Further, an RF model is also developed to classify different types of faults based on features extracted from the acquired vibration data. The model evaluates the precision and reliability of the MEMS setup in capturing and classifying vibration signals. A detailed signal analysis is also conducted to determine the performance of the developed MEMS setup and to investigate the effect of bearing vibration signature due to gear fault and vice versa. The results indicate that bearing faults cause irregularities in the shaft's rotational speed, leading to modulation of the gear mesh frequency (gmf) of gears mounted on the affected shaft. Conversely, gear faults disrupt the shaft's rotational motion, imposing excessive loads on shaft-supported bearings. These disruptions result in distinct vibration patterns characterised by increased harmonics and side bands within the bearing frequency range. The RF model effectively identifies and classifies faults with high accuracy by leveraging its ability to prioritise the most significant vibrational features, resulting in improved predictive performance and robustness. |
| format | Article |
| id | doaj-art-b4b3a87e6ef447abaef39809a93be453 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-b4b3a87e6ef447abaef39809a93be4532025-08-20T01:58:31ZengElsevierResults in Engineering2590-12302024-12-012410349910.1016/j.rineng.2024.103499Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approachGagandeep Sharma0Tejbir Kaur1Sanjay Kumar Mangal2Amit Kohli3Department of Mechanical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, IndiaDepartment of Mechanical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, IndiaDepartment of Mechanical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, IndiaMSE department, University Canada West, Canada; Corresponding author.Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS setup is initially developed, comprising a Raspberry Pi 4B+ CPU module, a NucleoF401RET6 MCU, an OLED screen, and an Adxl1002z accelerometer for acquiring vibration signals at the desired sampling frequency stored in the CPU memory. Further, an RF model is also developed to classify different types of faults based on features extracted from the acquired vibration data. The model evaluates the precision and reliability of the MEMS setup in capturing and classifying vibration signals. A detailed signal analysis is also conducted to determine the performance of the developed MEMS setup and to investigate the effect of bearing vibration signature due to gear fault and vice versa. The results indicate that bearing faults cause irregularities in the shaft's rotational speed, leading to modulation of the gear mesh frequency (gmf) of gears mounted on the affected shaft. Conversely, gear faults disrupt the shaft's rotational motion, imposing excessive loads on shaft-supported bearings. These disruptions result in distinct vibration patterns characterised by increased harmonics and side bands within the bearing frequency range. The RF model effectively identifies and classifies faults with high accuracy by leveraging its ability to prioritise the most significant vibrational features, resulting in improved predictive performance and robustness.http://www.sciencedirect.com/science/article/pii/S2590123024017511GearboxRaspberry PiVibration analysisFault detectionRandom forest |
| spellingShingle | Gagandeep Sharma Tejbir Kaur Sanjay Kumar Mangal Amit Kohli Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach Results in Engineering Gearbox Raspberry Pi Vibration analysis Fault detection Random forest |
| title | Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach |
| title_full | Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach |
| title_fullStr | Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach |
| title_full_unstemmed | Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach |
| title_short | Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach |
| title_sort | investigating bearing and gear vibrations with a micro electro mechanical systems mems and machine learning approach |
| topic | Gearbox Raspberry Pi Vibration analysis Fault detection Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024017511 |
| work_keys_str_mv | AT gagandeepsharma investigatingbearingandgearvibrationswithamicroelectromechanicalsystemsmemsandmachinelearningapproach AT tejbirkaur investigatingbearingandgearvibrationswithamicroelectromechanicalsystemsmemsandmachinelearningapproach AT sanjaykumarmangal investigatingbearingandgearvibrationswithamicroelectromechanicalsystemsmemsandmachinelearningapproach AT amitkohli investigatingbearingandgearvibrationswithamicroelectromechanicalsystemsmemsandmachinelearningapproach |