@ARTICLE{Ahsan_Muhammad_ARL-Wavelet-BPF_2023,
 author={Ahsan, Muhammad and Bismor, Dariusz and Manzoor, Muhammad Arslan},
 volume={vol. 33},
 number={No 3},
 journal={Archives of Control Sciences},
 pages={589–606},
 howpublished={online},
 year={2023},
 publisher={Committee of Automatic Control and Robotics PAS},
 abstract={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.},
 type={Article},
 title={ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis},
 URL={http://journals.pan.pl/Content/128385/PDF-MASTER/art06_int.pdf},
 doi={10.24425/acs.2023.146961},
 keywords={signal-to-noise ratio, asymmetric real Laplace wavelet, bandpass filter, particleswarm optimization, spectral kurtosis, fault frequency},
}