Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strat...Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.展开更多
Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and m...Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51575102)Jiangsu Postgraduate Research Innovation Program(Grant No.KYCX18_0075).
文摘Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same time.In this paper,a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix,aiming to o er a fast multi-tasking solution.The short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration signal.Then,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity indexes.Subsequently,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks.Finally,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic results.The Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.
基金Supported by National Natural Science Foundation of China(Grant No.51835009).
文摘Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.
文摘针对滚动轴承在变工况环境中网络特征提取能力不足的问题,提出了一种域对抗图卷积注意力迁移学习的故障诊断方法(DAGRESL)。首先,通过残差神经网络(residual network, Resnet)提取输入的轴承故障信息特征并通过Simam注意力模块增强Resnet的特征表达能力;其次,利用图生成层学习Resnet的特征数据并挖掘样本结构特征之间的关系来构造实例图;然后,利用图卷积网络(graph convolutional network, GCN)对实例图进行建模;最后,利用域判别器和局部最大平均差异(local maximum mean discrepancy, LMMD)对齐子域和全局域之间的分布并通过标签分类网络完成故障分类。通过在SQI-MFS轴承数据集的实验结果证明了所提出的DAGRESL模型能够精准地区分变工况轴承故障类型,有效解决了滚动轴承在变工况环境中网络特征提取能力不足的问题。
