- Tytuł:
- ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis
- Autorzy:
-
Ahsan, Muhammad
Bismor, Dariusz
Manzoor, Muhammad Arslan - Powiązania:
- https://bibliotekanauki.pl/articles/27322619.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
signal-to-noise ratio
asymmetric real Laplace wavelet
bandpass filter
particle swarm optimization
spectral kurtosis
fault frequency - Opis:
- 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 ARL-wavelet 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.
- Źródło:
-
Archives of Control Sciences; 2023, 33, 3; 589--606
1230-2384 - Pojawia się w:
- Archives of Control Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł