Journal Bearing Fault Detection Based on Daubechies Wavelet
Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institute of Fundamental Technological Research Polish Academy of Sciences
2017-07-01
|
| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1918 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849413177676136448 |
|---|---|
| author | Narendiranath Babu THAMBA Himamshu H S Prabin Kumar NAYAK Rama Prabha D Nishant CHILUAR |
| author_facet | Narendiranath Babu THAMBA Himamshu H S Prabin Kumar NAYAK Rama Prabha D Nishant CHILUAR |
| author_sort | Narendiranath Babu THAMBA |
| collection | DOAJ |
| description | Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for utomated bearing fault detection. |
| format | Article |
| id | doaj-art-103e70317192409eab8d46d541c8f2af |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2017-07-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-103e70317192409eab8d46d541c8f2af2025-08-20T03:34:12ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2017-07-0142310.1515/aoa-2017-0042Journal Bearing Fault Detection Based on Daubechies WaveletNarendiranath Babu THAMBA0Himamshu H S1Prabin Kumar NAYAK2Rama Prabha D3Nishant CHILUAR4School of Mechanical Engineering, VIT University, vellore, IndiaSchool of Mechanical Engineering VIT University Vellore IndiaSchool of Mechanical Engineering VIT University Vellore IndiaSchool of Electrical Engineering VIT University Vellore IndiaSchool of Mechanical Engineering VIT University Vellore IndiaJournal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for utomated bearing fault detection.https://acoustics.ippt.pan.pl/index.php/aa/article/view/1918journal bearingfault diagnosisDebauchies waveletartificial neural network |
| spellingShingle | Narendiranath Babu THAMBA Himamshu H S Prabin Kumar NAYAK Rama Prabha D Nishant CHILUAR Journal Bearing Fault Detection Based on Daubechies Wavelet Archives of Acoustics journal bearing fault diagnosis Debauchies wavelet artificial neural network |
| title | Journal Bearing Fault Detection Based on Daubechies Wavelet |
| title_full | Journal Bearing Fault Detection Based on Daubechies Wavelet |
| title_fullStr | Journal Bearing Fault Detection Based on Daubechies Wavelet |
| title_full_unstemmed | Journal Bearing Fault Detection Based on Daubechies Wavelet |
| title_short | Journal Bearing Fault Detection Based on Daubechies Wavelet |
| title_sort | journal bearing fault detection based on daubechies wavelet |
| topic | journal bearing fault diagnosis Debauchies wavelet artificial neural network |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1918 |
| work_keys_str_mv | AT narendiranathbabuthamba journalbearingfaultdetectionbasedondaubechieswavelet AT himamshuhs journalbearingfaultdetectionbasedondaubechieswavelet AT prabinkumarnayak journalbearingfaultdetectionbasedondaubechieswavelet AT ramaprabhad journalbearingfaultdetectionbasedondaubechieswavelet AT nishantchiluar journalbearingfaultdetectionbasedondaubechieswavelet |