Anti-friction bearing malfunction detection and diagnostics using hybrid approach
Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guides moving parts of the machine and reduces friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bear...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2019
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27940/1/Anti-friction%20bearing%20malfunction%20detection%20and%20diagnostics%20using%20hybrid%20.pdf |
| _version_ | 1825813261792051200 |
|---|---|
| author | Lemma, Tamiru Alemu Noraimi, Omar Gebremariam, M.A. Shazaib, Ahsan |
| author_facet | Lemma, Tamiru Alemu Noraimi, Omar Gebremariam, M.A. Shazaib, Ahsan |
| author_sort | Lemma, Tamiru Alemu |
| collection | UMP |
| description | Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guides moving parts of the machine and reduces friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This paper presents detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott–Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor, and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. |
| first_indexed | 2024-03-06T12:41:23Z |
| format | Conference or Workshop Item |
| id | UMPir27940 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:41:23Z |
| publishDate | 2019 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir279402020-10-08T03:52:21Z http://umpir.ump.edu.my/id/eprint/27940/ Anti-friction bearing malfunction detection and diagnostics using hybrid approach Lemma, Tamiru Alemu Noraimi, Omar Gebremariam, M.A. Shazaib, Ahsan TJ Mechanical engineering and machinery TS Manufactures Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guides moving parts of the machine and reduces friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This paper presents detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott–Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor, and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. Springer 2019-09 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27940/1/Anti-friction%20bearing%20malfunction%20detection%20and%20diagnostics%20using%20hybrid%20.pdf Lemma, Tamiru Alemu and Noraimi, Omar and Gebremariam, M.A. and Shazaib, Ahsan (2019) Anti-friction bearing malfunction detection and diagnostics using hybrid approach. In: Lecture Notes in Mechanical Engineering; 4th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2018 , 14 - 15 November 2018 , Melaka. pp. 117-131.. ISSN 2195-4356 ISBN 978-981138296-3 (Published) https://doi.org/10.1007/978-981-13-8297-0_15 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Lemma, Tamiru Alemu Noraimi, Omar Gebremariam, M.A. Shazaib, Ahsan Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title | Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title_full | Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title_fullStr | Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title_full_unstemmed | Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title_short | Anti-friction bearing malfunction detection and diagnostics using hybrid approach |
| title_sort | anti friction bearing malfunction detection and diagnostics using hybrid approach |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/27940/1/Anti-friction%20bearing%20malfunction%20detection%20and%20diagnostics%20using%20hybrid%20.pdf |
| work_keys_str_mv | AT lemmatamirualemu antifrictionbearingmalfunctiondetectionanddiagnosticsusinghybridapproach AT noraimiomar antifrictionbearingmalfunctiondetectionanddiagnosticsusinghybridapproach AT gebremariamma antifrictionbearingmalfunctiondetectionanddiagnosticsusinghybridapproach AT shazaibahsan antifrictionbearingmalfunctiondetectionanddiagnosticsusinghybridapproach |