Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis

• Published in: Applied acoustics Vol. 225; p. 110200
• Main Authors: Chauhan, Sumika, Vashishtha, Govind, Zimroz, Radoslaw, Kumar, Rajesh, Kumar Gupta, Munish
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.11.2024
• Subjects: Denoising filter; Kurtosis; Mountain gazelle algorithm; Rotary components defects; Sparsity index; Sparsity index; Kurtosis; Mountain gazelle algorithm; Denoising filter; Rotary components defects
• ISSN: 0003-682X
• DOI: 10.1016/j.apacoust.2024.110200

•The filter coefficient representing system impulse is optimized by MGO algorithm.•Novel sparsity index NEKI is developed to characterizes the periodic impulses.•The efficacy of denoising filter is validated through the case study from industries. The informative frequency band (IFB) plays a vital role in detecting defects in complex machinery through visible informative features. In the present work, a denoising filter has been designed to enhance the small non-stationarities present in the signal. Initially, the system impulse is computed to estimate the filter coefficients which are further optimized by the mountain gazelle optimization (MGO) based on the maximum value fitness function. The novel sparsity index based on kurtosis and negentropy (NE) is put forward as the fitness function. Then, optimized coefficients are convolved with the system impulse to design the denoising filter. The efficacy of the designed filter is verified through vibration and acoustic signals from the defective components of the belt conveyor system. The designed filter is better able to extract the impulsiveness from the signal, give improved values of kurtosis and signal-to-noise ratio (SNR), and reduce interferences from other machinery components and the environment simultaneously.