小样本下基于元学习和网络结构搜索的新模态故障诊断

实际多模态化工过程通常由于产品需求等调整而产生新模态,现有基于深度学习的故障诊断方法存在未充分利用现有模态设计经验、小样本下难以训练模型等局限.针对上述问题,提出一种基于元学习(meta learning, ML)和网络结构搜索(neural architecture search, NAS)的新模态故障诊断方法MetaNAS.首先,利用NAS自动获取现有模态性能最优的网络模型;然后,利用ML从现有模态的NAS过程中学习故障诊断模型的设计经验;最后,当新模态产生时,在已学习设计经验基础上进行梯度更新,即在小样本条件下快速得到新模态故障诊断模型.通过数值系统和田纳西伊斯曼(Tennessee Eastman, TE)化工过程的仿真实验充分验证所提出方法的有效性和可行性.

Fault diagnosis based on meta learning and neural architecture search for new modes with small samples

In the actual multimode chemical processes, new modes are usually generated due to the adjustments of product requirements. The existing fault diagnosis methods based on deep learning have limitations, such as the insufficient use of the design experience from the existing modes and the difficulty of training models with small samples. Aiming at the above problems, a fault diagnosis method named MetaNAS based on meta learning (ML) and neural architecture search (NAS) is proposed for new modes. The proposed method first uses NAS to automatically obtain the network models with the best performances for the existing modes. Then, ML is used to learn the design experience of the fault diagnosis models from the NAS processes of the existing modes. Finally, when new modes are generated, gradient update is carried out on the basis of the learned design experience, that is, the fault diagnosis models of new modes can be quickly obtained under the condition of small samples. The effectiveness and feasibility of the proposed method are fully verified by the simulation experiments of a numerical system and the Tennessee Eastman (TE) chemical process.