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

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.