Study of an underdetermined blind source separation method applied to acoustic inspection of rollers

In the acoustic monitoring of belt conveyor rollers, multiple sound sources and strong noise often impede remote fault diagnosis based on acoustic signals. To address this issue, this study proposes an underdetermined blind source separation method that combines singular spectrum decomposition (SSD) and improved sparse component analysis (SCA), which is suitable for tackling noise interference in roller acoustic inspection systems. Field tests have validated the effectiveness of this method, which reduces reliance on extensive a prior knowledge and the need for numerous sensors, demonstrating outstanding performance in separating sound sources of faulty rollers from multiple noise sources.