Research on bearing fault detection using a filtering algorithm based on multi-scale morphology and an improved PSO algorithm

Mathematical morphology filtering, while being a commonly employed algorithm for bearing fault detection, demonstrates notable sensitivity to noise interference and inadequate capability in extracting transient vibration features. To overcome these limitations, this study proposes an enhanced design filtering architecture through mathematical morphology principles, and the characteristic frequency intensity coefficient serves as the operator selection criterion. This paper further develops a multi-scale mathematical morphology filtering operator while addressing the inherent challenge of determining weight coefficients in multi-scale systems. This is achieved through the particle swarm optimization algorithm with a tent chaotic sequence, which effectively captures weak periodic transient components in bearing vibration signals under noisy conditions. Ultimately, a multi-scale mathematical morphology filtering strategy is formulated and validated through simulated data and faulty bearing experiments. Comparative analyses demonstrate that the proposed algorithm achieves superior accuracy of faulty detection for inner ring faulty bearing, outer ring faulty bearing, and rolling element faulty bearing in the experiment compared to conventional morphology filtering methods, particularly in some complex noise environments.