With the rapid development of smart manufacturing, data-driven machinery health management has been of growing attention. In situations where some classes are more difficult to be distinguished compared to others and where classes might be organised in a hierarchy of categories, current DL methods can not work well. In this study, a novel hierarchical cavitation intensity recognition framework using Sub-Main Transfer Network, termed SMTNet, is proposed to classify acoustic signals of valve cavitation. SMTNet model outputs multiple predictions ordered from coarse to fine along a network corresponding to a hierarchy of target cavitation states. Firstly, a data augmentation method based on Sliding Window with Fast Fourier Transform (Swin-FFT) is developed to solve few-shot problem. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is presented to capture sensitive features of the frequency domain valve acoustic signals. Thirdly, hierarchical multi-label tree is proposed to assist the embedding of the semantic structure of target cavitation states into SMTNet. Fourthly, experience filtering mechanism is proposed to fully learn a prior knowledge of cavitation detection model. Finally, SMTNet has been evaluated on two cavitation datasets without noise (Dataset 1 and Dataset 2), and one cavitation dataset with real noise (Dataset 3) provided by SAMSON AG (Frankfurt). The prediction accurcies of SMTNet for cavitation intensity recognition are as high as 95.32%, 97.16% and 100%, respectively. At the same time, the testing accuracies of SMTNet for cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, SMTNet has also been tested for different frequencies of samples and has achieved excellent results of the highest frequency of samples of mobile phones.

翻译：随着智能制造的快速发展，数据驱动的机械健康管理日益受到关注。在部分类别相较于其他类别更难区分、且类别可能按层级结构组织的情况下，当前深度学习方法无法有效工作。本研究提出一种新颖的分层空化强度识别框架——基于子主传递网络的SMTNet，用于对阀门空化声学信号进行分类。SMTNet模型沿对应目标空化状态层级结构的网络，按从粗到细的顺序输出多个预测结果。首先，提出基于滑动窗口快速傅里叶变换的数据增强方法Swin-FFT，以解决小样本问题；其次，设计一维双分层残差块，用于捕捉频域阀门声学信号的敏感特征；再次，提出分层多标签树，辅助将目标空化状态的语义结构嵌入SMTNet；然后，提出经验过滤机制，以充分学习空化检测模型的先验知识；最后，在由SAMSON AG（法兰克福）提供的两个无噪声空化数据集（数据集1和数据集2）以及一个含真实噪声的空化数据集（数据集3）上对SMTNet进行评估。SMTNet在空化强度识别上的预测准确率分别高达95.32%、97.16%和100%；同时，其在空化检测上的测试准确率分别高达97.02%、97.64%和100%。此外，SMTNet还针对不同频率的样本进行了测试，并在手机样本的最高频率上取得了优异结果。