零小样本旋转机械故障诊断综述OA北大核心CSTPCD

With the advent of the data era,data-driven fault diagnosis methods have demonstrated excellent performance.Since the application of deep learning in fault diagnosis,supervised learning has made significant advancements.However,when samples are scarce or missing,supervised learning lacks the necessary training conditions.This paper proposes the zero-shot and small-sample problem,and analyzes its current status in the field of rotating machinery fault diagnosis.It reviews the development process,mainstream models,and current research hotspots of zero-shot rotating machinery fault diagnosis.Existing research achievements are summarized from two aspects:zero-shot problems and small-sample prob-lems,and their applications in zero-shot and small-sample problems are analyzed.Finally,the paper discusses the future trends in zero-shot methods for rotating machinery fault diagnosis.