Training feedforward neural networks for fault diagnosis of ball bearing
Vibration-based condition monitoring plays important roles for early fault detection and diagnosis of expensive rotating machinery. This paper presents the application of a novel metaheuristic approach named chaos-enhanced stochastic fractal search (CFS) to train feedforward neural networks (FNNs) f...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/65091/ http://psasir.upm.edu.my/id/eprint/65091/1/68-73-1.pdf |
| _version_ | 1848855189213151232 |
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| author | Tuan Abdul Rahman, Tuan Ahmad Zahidi As'arry, Azizan Abdul Jalil, Nawal Aswan Raja Ahmad, Raja Mohd Kamil |
| author_facet | Tuan Abdul Rahman, Tuan Ahmad Zahidi As'arry, Azizan Abdul Jalil, Nawal Aswan Raja Ahmad, Raja Mohd Kamil |
| author_sort | Tuan Abdul Rahman, Tuan Ahmad Zahidi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Vibration-based condition monitoring plays important roles for early fault detection and diagnosis of expensive rotating machinery. This paper presents the application of a novel metaheuristic approach named chaos-enhanced stochastic fractal search (CFS) to train feedforward neural networks (FNNs) for monitoring a ball bearings system. The vibration response data are analyzed using statistical methods to characterize several defects of ball bearings and generate vibration signature features. Then, a novel CFS-based FNNs approach is applied to classify these ball bearings conditions. The results show that the proposed approach produces comparable classification accuracy as parameters of the FNNs were optimized systematically using CFS algorithm. |
| first_indexed | 2025-11-15T11:21:48Z |
| format | Conference or Workshop Item |
| id | upm-65091 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:21:48Z |
| publishDate | 2018 |
| publisher | Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-650912018-09-03T04:54:32Z http://psasir.upm.edu.my/id/eprint/65091/ Training feedforward neural networks for fault diagnosis of ball bearing Tuan Abdul Rahman, Tuan Ahmad Zahidi As'arry, Azizan Abdul Jalil, Nawal Aswan Raja Ahmad, Raja Mohd Kamil Vibration-based condition monitoring plays important roles for early fault detection and diagnosis of expensive rotating machinery. This paper presents the application of a novel metaheuristic approach named chaos-enhanced stochastic fractal search (CFS) to train feedforward neural networks (FNNs) for monitoring a ball bearings system. The vibration response data are analyzed using statistical methods to characterize several defects of ball bearings and generate vibration signature features. Then, a novel CFS-based FNNs approach is applied to classify these ball bearings conditions. The results show that the proposed approach produces comparable classification accuracy as parameters of the FNNs were optimized systematically using CFS algorithm. Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/65091/1/68-73-1.pdf Tuan Abdul Rahman, Tuan Ahmad Zahidi and As'arry, Azizan and Abdul Jalil, Nawal Aswan and Raja Ahmad, Raja Mohd Kamil (2018) Training feedforward neural networks for fault diagnosis of ball bearing. In: 5th Mechanical Engineering Research Day (MERD'18), 3 May 2018, Kampus Teknologi UTeM, Melaka. (pp. 290-292). |
| spellingShingle | Tuan Abdul Rahman, Tuan Ahmad Zahidi As'arry, Azizan Abdul Jalil, Nawal Aswan Raja Ahmad, Raja Mohd Kamil Training feedforward neural networks for fault diagnosis of ball bearing |
| title | Training feedforward neural networks for fault diagnosis of ball bearing |
| title_full | Training feedforward neural networks for fault diagnosis of ball bearing |
| title_fullStr | Training feedforward neural networks for fault diagnosis of ball bearing |
| title_full_unstemmed | Training feedforward neural networks for fault diagnosis of ball bearing |
| title_short | Training feedforward neural networks for fault diagnosis of ball bearing |
| title_sort | training feedforward neural networks for fault diagnosis of ball bearing |
| url | http://psasir.upm.edu.my/id/eprint/65091/ http://psasir.upm.edu.my/id/eprint/65091/1/68-73-1.pdf |