ISWD: Improvement of swarm decomposition method based on new criterion for bearing early fault detection

A significant challenge in troubleshooting rotary machines is the use of signal processing methods that are often hindered by mode mixing, end effects, and predetermined parameters. This paper introduces an Improved Swarm Decomposition (ISWD) method to address these limitations. ISWD is an enhancement of the Swarm Decomposition (SWD) method, which employs a swarm-based intelligence technique to decompose a signal into oscillatory components (OCs). The performance of the original SWD method is highly dependent on two threshold parameters. A new objective function incorporating kurtosis, cross-correlation, and an orthogonal index, is defined and minimized by PSOSCALF algorithm, a hybrid of Particle Swarm Optimization, Lévy flight, and the sine–cosine algorithm. The ISWD technique is evaluated using noisy verification signals of early bearing fault data and the results are compared with other signal processing methods. Two case studies involving defective inner and outer rings are considered. The results indicate that the characteristic frequencies of bearings and their harmonics in the envelope spectra of components obtained by ISWD are clearly revealed, enabling easy identification of bearing fault types. Furthermore, the components extracted by ISWD are physically meaningful and contain higher-quality information than those obtained by SWD and other methods.