Dual-loss nonlinear independent component estimation for augmenting explainable vibration samples of rotating machinery faults

Obtaining fault samples for diagnosing faults in rotating machinery for engineering applications can be costly. To address this challenge, fault sample augmentation methods are used to train fault diagnosis models. However, existing techniques mainly focus on comparing augmented signal loss with real ones, overlooking the underlying vibration mechanism of rotating machinery faults. Addressing this limitation, a novel approach, called dual-loss nonlinear independent component estimation (DLNICE), is proposed to enhance understanding of fault features in vibration signals. This integrates augmentation losses in both time and frequency domains to enrich fault vibration samples. DLNICE effectively utilizes limited fault samples for augmentation by estimating nonlinear independent components, capturing key fault characteristics like impulsiveness and cyclo-stationarity. Therefore, augmented fault samples become more explainable for analyzing rotating machinery faults. Experimental evaluations on bearing and gearbox vibration samples confirm the effectiveness of DLNICE. Utilizing the augmented samples leads to an average accuracy of 86.27 % in bearing fault diagnosis, and that of 81.60 % in gearbox fault diagnosis. The results demonstrate that DLNICE excels in augmenting high-quality vibration samples of rotating machinery faults.