少样本下基于元学习的柱塞泵故障诊断方法  

Fault Diagnosis Method of Piston Pump Based on Meta⁃Learning with Few Samples

作　　者：胡宏俊 杨喜旺[1] 黄晋英[2] HU Hongjun;YANG Xiwang;HUANG Jinying(School of Computer Science andTechnology,North University of China,Taiyuan 030051,China;School of Mechanical Engineering,North University of China,Taiyuan 030051 China)

机构地区：[1]中北大学计算机科学与技术学院,山西太原030051 [2]中北大学机械工程学院,山西太原030051

出　　处：《中北大学学报（自然科学版）》2024年第5期592-600,共9页Journal of North University of China(Natural Science Edition)

基　　金：山西省回国留学人员科研教研资助项目(2022-141);山西省基础研究计划资助项目(202203021211096);山西省创新项目(2023KY600)。

摘　　要：针对柱塞泵故障样本少、在噪声干扰下故障信号微弱及传统深度学习依赖大量训练样本的问题,提出了一种基于模型不可知元学习(MAML)的少样本柱塞泵故障诊断方法。首先,利用改进的带自适应噪声的完全集成经验模态分解(ICEEMDAN)方法来分解采集到的一维振动信号,得到本征模态函数的IMF分量,并筛选故障信息丰富的敏感分量以增强振动信号中的特征信息。其次,建立了多通道一维卷积模型,该模型构建了一个具有高效通道注意力机制的通道交互特征编码器,旨在关注不同通道间的交互故障信息,进而有效地提取多个诊断元任务的通用诊断知识。最后,将一维卷积模型作为基模型,并通过MAML方法训练获得了最优的模型初始化参数;最优的初始化模型能够快速适应新工况下的少量柱塞泵故障样本,从而实现了少样本下的柱塞泵故障诊断。利用柱塞泵实验数据验证了模型的性能,结果表明,所提方法在少样本条件下对于各种诊断任务的诊断准确率都达到90%以上。Addressing the issues of limited fault samples for piston pumps,weak fault signals under noise interference,and traditional deep learning's heavy reliance on vast amounts of training data,we proposed a novel few-shot fault diagnosis approach for piston pumps based on model-agnostic meta-learning(MAML)with few samples.Firstly,the collected one-dimensional vibration signal was decomposed using improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),resulting in intrinsic mode function(IMF)components.Subsequently,sensitive components rich in fault information were selected to enhance the feature information within the vibration signal.Secondly,a multi-channel one-dimensional convolutional model was established,incorporating a channel interaction feature encoder equipped with an efficient channel attention mechanism.This design aimed to focus on the mutual fault information among different channels,thereby effectively extracting general diagnostic knowledge applicable to multiple diagnostic meta-tasks.Finally,the one-dimensional convolutional model served as the base model,which was trained through the MAML method to obtain optimal initial model parameters.Following this,the optimally initialized model could quickly adapt to new operating condi‐tions with limited piston pump fault samples,thereby realizing few-shot fault diagnosis for piston pumps.The performance of the proposed model was validated using experimental data from piston pump tests.Experimental results demonstrate that the proposed method achieves an accuracy rate of over 90%across various diagnostic tasks under few-sample conditions.

关 键 词：模型不可知元学习 少样本 注意力机制 柱塞泵 

分 类 号：TH165[机械工程—机械制造及自动化]