ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis
Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2023-09-01
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| Series: | Archives of Control Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/128385/PDF/art06_int.pdf |
| _version_ | 1797682299728297984 |
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| author | Muhammad Ahsan Dariusz Bismor Muhammad Arslan Manzoor |
| author_facet | Muhammad Ahsan Dariusz Bismor Muhammad Arslan Manzoor |
| author_sort | Muhammad Ahsan |
| collection | DOAJ |
| description | Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARLwavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. Spectral kurtosis (SK) of the envelope spectrum is employed as a fitness function for the PSO algorithm which helps to track the periodic spikes generated by the fault frequency in the vibration signal. To validate the performance of the ARL-wavelet-BPF, different vibration signals with low signal-to-noise ratio are used and faults are diagnosed. |
| first_indexed | 2024-03-11T23:57:44Z |
| format | Article |
| id | doaj.art-fd5a8ee253ae47119b4028896ef9ddb1 |
| institution | Directory Open Access Journal |
| issn | 1230-2384 |
| language | English |
| last_indexed | 2024-03-11T23:57:44Z |
| publishDate | 2023-09-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Control Sciences |
| spelling | doaj.art-fd5a8ee253ae47119b4028896ef9ddb12023-09-18T10:54:20ZengPolish Academy of SciencesArchives of Control Sciences1230-23842023-09-01vol. 33No 3https://doi.org/10.24425/acs.2023.146961ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosisMuhammad Ahsan0https://orcid.org/0000-0003-2362-3297Dariusz Bismor1https://orcid.org/0000-0003-4758-3592Muhammad Arslan Manzoor2Department of Measurements and Control Systems, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Measurements and Control Systems, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAERotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARLwavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. Spectral kurtosis (SK) of the envelope spectrum is employed as a fitness function for the PSO algorithm which helps to track the periodic spikes generated by the fault frequency in the vibration signal. To validate the performance of the ARL-wavelet-BPF, different vibration signals with low signal-to-noise ratio are used and faults are diagnosed.https://journals.pan.pl/Content/128385/PDF/art06_int.pdfsignal-to-noise ratioasymmetric real laplace waveletbandpass filterparticleswarm optimizationspectral kurtosisfault frequency |
| spellingShingle | Muhammad Ahsan Dariusz Bismor Muhammad Arslan Manzoor ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis Archives of Control Sciences signal-to-noise ratio asymmetric real laplace wavelet bandpass filter particleswarm optimization spectral kurtosis fault frequency |
| title | ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis |
| title_full | ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis |
| title_fullStr | ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis |
| title_full_unstemmed | ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis |
| title_short | ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis |
| title_sort | arl wavelet bpf optimization using pso algorithm for bearing fault diagnosis |
| topic | signal-to-noise ratio asymmetric real laplace wavelet bandpass filter particleswarm optimization spectral kurtosis fault frequency |
| url | https://journals.pan.pl/Content/128385/PDF/art06_int.pdf |
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