Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/17/5827 |
| _version_ | 1797556177011212288 |
|---|---|
| author | Farzin Piltan Jong-Myon Kim |
| author_facet | Farzin Piltan Jong-Myon Kim |
| author_sort | Farzin Piltan |
| collection | DOAJ |
| description | In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively. |
| first_indexed | 2024-03-10T16:58:13Z |
| format | Article |
| id | doaj.art-a5cc01c470d24612857707e3f11ac3e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:58:13Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a5cc01c470d24612857707e3f11ac3e82023-11-20T11:03:32ZengMDPI AGApplied Sciences2076-34172020-08-011017582710.3390/app10175827Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault ObserverFarzin Piltan0Jong-Myon Kim1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaDepartment of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaIn this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively.https://www.mdpi.com/2076-3417/10/17/5827rotating machinebearingadaptive cascade observersupport vector machineproportional-integral (PI) observerstructure fault observer |
| spellingShingle | Farzin Piltan Jong-Myon Kim Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer Applied Sciences rotating machine bearing adaptive cascade observer support vector machine proportional-integral (PI) observer structure fault observer |
| title | Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer |
| title_full | Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer |
| title_fullStr | Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer |
| title_full_unstemmed | Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer |
| title_short | Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer |
| title_sort | bearing fault identification using machine learning and adaptive cascade fault observer |
| topic | rotating machine bearing adaptive cascade observer support vector machine proportional-integral (PI) observer structure fault observer |
| url | https://www.mdpi.com/2076-3417/10/17/5827 |
| work_keys_str_mv | AT farzinpiltan bearingfaultidentificationusingmachinelearningandadaptivecascadefaultobserver AT jongmyonkim bearingfaultidentificationusingmachinelearningandadaptivecascadefaultobserver |