零小样本旋转机械故障诊断综述

Review on Zero or Few Sample Rotating Machinery Fault Diagnosis

随着数据时代的到来,基于数据驱动的故障诊断方法表现出了优秀的性能。深度学习应用于故障诊断以来,监督学习取得了巨大的发展,但当样本稀少或者缺失时,监督学习将缺乏训练的必要条件。提出了零小样本问题并分析了其在旋转机械故障诊断领域的现状;回顾了零小样本旋转机械故障诊断的发展历程、主流模型和当前研究热点;从零样本问题和小样本问题两个方面总结了现有研究成果并分析现有方法在零小样本问题中的应用。最后,展望了旋转机械故障诊断的零小样本方法的发展趋势。

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.