The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

基于人工智能相关技术的智能故障诊断研究成果显着。在工程场景中，机器通常工作在正常状态，这意味着可以收集到有限的故障数据。小数据不平衡智能故障诊断(S&I-IFD)是指利用有限的机器故障样本建立智能诊断模型以实现准确的故障识别，一直受到研究人员的关注。目前，S&I-IFD的研究已经取得了丰硕的成果，但对最新成果的梳理还比较欠缺，未来的研究方向还不够明确。为了解决这个问题，我们回顾了 S&I-IFD 的研究结果，并在本文中提供了一些未来的观点。现有的研究成果分为三类：基于数据增强的、基于特征学习的和基于分类器设计的。基于数据增强的策略通过增强训练数据来提高诊断模型的性能。基于特征学习的策略通过从小数据和不平衡数据中提取特征来准确识别故障。基于分类器设计的策略通过构建适合小数据和不平衡数据的分类器来实现高诊断准确性。最后，本文指出了 S&I-IFD 面临的研究挑战，并提供了一些可能带来突破的方向，包括元学习和零样本学习。基于数据增强的策略通过增强训练数据来提高诊断模型的性能。基于特征学习的策略通过从小数据和不平衡数据中提取特征来准确识别故障。基于分类器设计的策略通过构建适合小数据和不平衡数据的分类器来实现高诊断准确性。最后，本文指出了 S&I-IFD 面临的研究挑战，并提供了一些可能带来突破的方向，包括元学习和零样本学习。基于数据增强的策略通过增强训练数据来提高诊断模型的性能。基于特征学习的策略通过从小数据和不平衡数据中提取特征来准确识别故障。基于分类器设计的策略通过构建适合小数据和不平衡数据的分类器来实现高诊断准确性。最后，本文指出了 S&I-IFD 面临的研究挑战，并提供了一些可能带来突破的方向，包括元学习和零样本学习。