动力学仿真数据驱动的域自适应智能诊断方法

Dynamics Simulation Data Driven Domain Adaptive Intelligent Fault Diagnosis

高质量标记数据是基于深度学习的故障诊断方法有效性的重要保障,然而在实际中难以获取大量工业标记故障案例,导致模型的泛化诊断能力弱。针对该问题,提出了动力学仿真数据驱动的域自适应智能诊断方法,该方法考虑仿真数据与实际数据的本质差异,引入了一种特征分离网络域自适应诊断模型,在传统的域自适应模型基础上增加了目标域独有特征提取器以显式分离实际数据中的环境噪声等特征,增强域不变故障特征表示和聚类能力。提出了将域共享特征提取器诊断结果用于域独有特征提取器模型参数的训练策略,进一步提高模型的训练稳定性。采用凯斯西储大学轴承数据集测试了所提方法的诊断性能,结果表明诊断准确率和特征提取及聚类能力均优于其他对比迁移方法,并经验性地分析了模型超参数敏感度。

High-quality labeled data was a crucial prerequisite for the effectiveness of deep learning-based fault diagnosis methods.However,obtaining a substantial number of industrial labeled fault cases was challenging,which led the model s generalization ability weak.A novel domain adaptive intelligent diagnosis method driven by dynamics simulation data was proposed to address the above issue.This method considered the fundamental disparity between simulation data and actual data,and introduced a feature separation network for domain adaptation in diagnostic modeling.Based upon traditional domain adaptation models,a unique feature extractor that was specific to the target domain was incorporated to explicitly separate environmental noises present in actual data.This enhancement improved fault feature representation and clustering capabilities through other features that remain invariant across domains.Furthermore,a novel training strategy was proposed that leveraged diagnostic results obtained from the shared feature extractor to iteratively update the model parameters of the unique feature extractor,thereby enhancing training stability even further.The proposed method was evaluated using the bearing dataset from Case Western Reserve University,demonstrating improved feature extraction and clustering capabilities compared to other transfer methods for comparison,as evidenced by enhanced performance and diagnostic accuracy.Additionally,the hyper-parameter sensitivity was analyzed empirically.