A smart fault identification system for ball bearing using simulation-driven vibration analysis
Bearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s hea...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2023-06-01
|
| Series: | Archive of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/127641/PDF/AME_2023_145583_1.pdf |
| _version_ | 1797771912548450304 |
|---|---|
| author | Pallavi Khaire Vikas Phalle |
| author_facet | Pallavi Khaire Vikas Phalle |
| author_sort | Pallavi Khaire |
| collection | DOAJ |
| description | Bearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s health for avoiding the risk of failure. In this work, a simulation-based approach is adopted to identify surface defects at ball bearing raceways. The vibration data in time and frequency domain is captured by FFT analyzer from an experimental setup. The time frequency domain conversion of a raw time domain data was carried out by wavelet packet transform, as it takes into account the transients and spectral frequencies. The rotor bearing model is simulated in Ansys. Finally, most influencing statistical features were extracted by employing Principal Component Analysis (PCA), and fed to Multiclass Support Vector Machine (MSVM). To train the algorithm, the simulated data is used whereas the data acquired from FFT analyzer is used for testing. It can be concluded that the defects are characterized by Ball Pass Frequency (BPF) at inner race and outer raceway as indicated in the literature. The developed model is capable to monitor bearing’s health which gives an average accuracy of 99%. |
| first_indexed | 2024-03-12T21:44:30Z |
| format | Article |
| id | doaj.art-98e5a72a0e154bd39cac70ea4980d3cc |
| institution | Directory Open Access Journal |
| issn | 2300-1895 |
| language | English |
| last_indexed | 2024-03-12T21:44:30Z |
| publishDate | 2023-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archive of Mechanical Engineering |
| spelling | doaj.art-98e5a72a0e154bd39cac70ea4980d3cc2023-07-26T13:42:06ZengPolish Academy of SciencesArchive of Mechanical Engineering2300-18952023-06-01vol. 70No 2247270https://doi.org/10.24425/ame.2023.145583A smart fault identification system for ball bearing using simulation-driven vibration analysisPallavi Khaire0https://orcid.org/0000-0001-8485-7439Vikas Phalle1Veermata Jijabai Technological Institute, Mumbai, IndiaVeermata Jijabai Technological Institute, Mumbai, IndiaBearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s health for avoiding the risk of failure. In this work, a simulation-based approach is adopted to identify surface defects at ball bearing raceways. The vibration data in time and frequency domain is captured by FFT analyzer from an experimental setup. The time frequency domain conversion of a raw time domain data was carried out by wavelet packet transform, as it takes into account the transients and spectral frequencies. The rotor bearing model is simulated in Ansys. Finally, most influencing statistical features were extracted by employing Principal Component Analysis (PCA), and fed to Multiclass Support Vector Machine (MSVM). To train the algorithm, the simulated data is used whereas the data acquired from FFT analyzer is used for testing. It can be concluded that the defects are characterized by Ball Pass Frequency (BPF) at inner race and outer raceway as indicated in the literature. The developed model is capable to monitor bearing’s health which gives an average accuracy of 99%.https://journals.pan.pl/Content/127641/PDF/AME_2023_145583_1.pdfcondition monitoringbearing defectfft analyzerbpfibpfomulticlass support vector machine |
| spellingShingle | Pallavi Khaire Vikas Phalle A smart fault identification system for ball bearing using simulation-driven vibration analysis Archive of Mechanical Engineering condition monitoring bearing defect fft analyzer bpfi bpfo multiclass support vector machine |
| title | A smart fault identification system for ball bearing using simulation-driven vibration analysis |
| title_full | A smart fault identification system for ball bearing using simulation-driven vibration analysis |
| title_fullStr | A smart fault identification system for ball bearing using simulation-driven vibration analysis |
| title_full_unstemmed | A smart fault identification system for ball bearing using simulation-driven vibration analysis |
| title_short | A smart fault identification system for ball bearing using simulation-driven vibration analysis |
| title_sort | smart fault identification system for ball bearing using simulation driven vibration analysis |
| topic | condition monitoring bearing defect fft analyzer bpfi bpfo multiclass support vector machine |
| url | https://journals.pan.pl/Content/127641/PDF/AME_2023_145583_1.pdf |
| work_keys_str_mv | AT pallavikhaire asmartfaultidentificationsystemforballbearingusingsimulationdrivenvibrationanalysis AT vikasphalle asmartfaultidentificationsystemforballbearingusingsimulationdrivenvibrationanalysis AT pallavikhaire smartfaultidentificationsystemforballbearingusingsimulationdrivenvibrationanalysis AT vikasphalle smartfaultidentificationsystemforballbearingusingsimulationdrivenvibrationanalysis |