Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids
The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
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Department of Electrical Engineering, Faculty of Engineering, Tanjungpura University
2020-10-01
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| Series: | Elkha: Jurnal Teknik Elektro |
| Subjects: | |
| Online Access: | https://jurnal.untan.ac.id/index.php/Elkha/article/view/41511 |
| _version_ | 1818115476891369472 |
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| author | Eska Rizqi Naufal Gigih Priyandoko Fachrudin Hunaini |
| author_facet | Eska Rizqi Naufal Gigih Priyandoko Fachrudin Hunaini |
| author_sort | Eska Rizqi Naufal |
| collection | DOAJ |
| description | The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%. |
| first_indexed | 2024-12-11T04:07:14Z |
| format | Article |
| id | doaj.art-37f854a0ae114cb89d06a76f77e0f554 |
| institution | Directory Open Access Journal |
| issn | 1858-1463 2580-6807 |
| language | Indonesian |
| last_indexed | 2024-12-11T04:07:14Z |
| publishDate | 2020-10-01 |
| publisher | Department of Electrical Engineering, Faculty of Engineering, Tanjungpura University |
| record_format | Article |
| series | Elkha: Jurnal Teknik Elektro |
| spelling | doaj.art-37f854a0ae114cb89d06a76f77e0f5542022-12-22T01:21:29ZindDepartment of Electrical Engineering, Faculty of Engineering, Tanjungpura UniversityElkha: Jurnal Teknik Elektro1858-14632580-68072020-10-01122546110.26418/elkha.v12i2.4151130318Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-MedoidsEska Rizqi Naufal0Gigih Priyandoko1Fachrudin Hunaini2Universitas Widyagama MalangUniversitas Widyagama MalangUniversitas Widyagama MalangThe 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%.https://jurnal.untan.ac.id/index.php/Elkha/article/view/41511induction motorbearing faultdiscrete wavelet transformationk-medoids |
| spellingShingle | Eska Rizqi Naufal Gigih Priyandoko Fachrudin Hunaini Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids Elkha: Jurnal Teknik Elektro induction motor bearing fault discrete wavelet transformation k-medoids |
| title | Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids |
| title_full | Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids |
| title_fullStr | Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids |
| title_full_unstemmed | Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids |
| title_short | Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids |
| title_sort | klasterisasi kerusakan bearing motor induksi 3 fasa menggunakan metode transformasi wavelet diskrit dan k medoids |
| topic | induction motor bearing fault discrete wavelet transformation k-medoids |
| url | https://jurnal.untan.ac.id/index.php/Elkha/article/view/41511 |
| work_keys_str_mv | AT eskarizqinaufal klasterisasikerusakanbearingmotorinduksi3fasamenggunakanmetodetransformasiwaveletdiskritdankmedoids AT gigihpriyandoko klasterisasikerusakanbearingmotorinduksi3fasamenggunakanmetodetransformasiwaveletdiskritdankmedoids AT fachrudinhunaini klasterisasikerusakanbearingmotorinduksi3fasamenggunakanmetodetransformasiwaveletdiskritdankmedoids |