Source-free domain adaptation for transferable remaining useful life prediction of machine considering source data absence

Data-driven method developed based on deep learning theory has satisfactorily solved the problems of fault classification and health prognosis for industrial equipment. Meanwhile, domain adaptation (DA) further endows the models with the ability to operate effectively across operating scenarios. Unfortunately, current DA methods require the overall participation of source data, which in real-world industrial scenarios is unavailable due to its privacy. In response to this, this paper proposes source-free domain adaptation (SFDA) to realize the transferable remaining useful life (RUL) prognosis of rotating machinery considering source data absence. Specifically, SFDA transforms measurement of inter-domain feature discrepancy into measurement of discrepancy between estimated parameters of corresponding statistical models through implicit statistical distribution generalization. In addition, a design criterion for improving the reliability of pseudo labels is proposed, which generates more robust pseudo labels for source-free domain self-training by minimizing angle offset and distance offset. The proposed framework has good generalization ability for target data and shared feature representations between different multiple domains. Run-to-failure degradation experiments were conducted on the core components of rotating equipment, and the experimental results verified the effectiveness and superiority of the proposed prediction framework.