A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samples

Effective fault diagnosis is a prerequisite for ensuring the safe, stable and long-term operation of many rotating machinery. With the rapid development of measurement, sensor and computing technologies, measurement data presents a high-dimensional and massive distribution. This makes the valuable fault information in samples sparse. Moreover, industrial data can only present the distribution state of few-shot unlabeled information. In addition, the vibration signal of bearing faults contains noise interference, leading to poor stability and low efficiency of most models. In this study, we propose an approach for rolling bearing faults diagnosis under few-shot samples. It consists of a multi-order graph embedding stacked denoising auto encoder optimized by an improved sine–cosine​ algorithm (MGE-ISCA-SDAE) and a collaborative central domain adaptation (CCDA). First, a multi-order graph embedding model and an ISCA-based strategy are designed to improve the SDAE, thereby improving the feature extraction effect. To overcome the sparseness of valuable information, we design a CCDA model that learns the fault features using the labeled samples. Subsequently, it is transferred to the target domain of few-shot labeled samples for adaptation. Finally, the intelligent diagnosis is achieved under few-shot samples. We conduct experiments with four datasets. The results show that the MGE-ISCA-SDAE can extract the time–frequency high-level fault features. The CCDA model can transfer the fault samples well. When there are fewer fault samples, our approach has outstanding advantages.