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Transfer Learning for Prognostics and Health Management:Advances,Challenges,and Opportunities

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Journal of Dynamics, Monitoring and Diagnostics 2024年第2期3卷 60-82页

作者： ruqiang yan Weihua Li Siliang Lu Min Xia Zhuyun Chen Zheng Zhou Yasong Li Jingfeng Lu School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhouChina College of Electrical Engineering and Automation Anhui UniversityHefeiChina Department of Mechanical&Materials Engineering Western UniversityOntarioCanada State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of TechnologyGuangzhou 510006China

As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadriven *** this paper,we briefly discuss general idea and advances of various transfer learning techniques in PHM domain,including domain adaptation,domain generalization,federated learning,and knowledge-driven transfer *** on the observations from state of the art,we provide extensive discussions on possible challenges and opportunities of transfer learning in PHM domain to direct future development.

关键词： domain adaptation domain generalization federated learning knowledge-driven PHM transfer learning

基于CBAM-CNN和压电悬臂梁的温度解耦质量感知方法

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仪器仪表学报 2024年第4期45卷 113-126页

作者：闫宇楠刘智康徐佳文严如强东南大学仪器科学与工程学院南京210096 机器人感知与控制技术重点实验室南京210096 西安交通大学高端装备研究院西安710049

悬臂梁结构广泛用于微小质量测量,而温度变化会引起测量结果漂移。传统测量方法需要在温度稳定的环境中进行,但实际应用中通常难以满足此要求,且温度漂移对测量的影响难以直接解耦。本文提出了一种基于数据驱动,CBAM-CNN和压电悬臂梁的... 详细信息

悬臂梁结构广泛用于微小质量测量,而温度变化会引起测量结果漂移。传统测量方法需要在温度稳定的环境中进行,但实际应用中通常难以满足此要求,且温度漂移对测量的影响难以直接解耦。本文提出了一种基于数据驱动,CBAM-CNN和压电悬臂梁的自适应温度解耦质量感知方法。首先,搭建谐振式压电悬臂梁温控测量平台采集不同质量负载下的阻抗响应信号,设计自适应加权预处理方法以增强结构特征并突出有限样本中的关键信息;其次,设计基于混合领域注意力机制的CBAM-CNN网络来评估信号中多个谐振峰的相对关系,实现温度解耦和质量感知。实验结果表明,该方法在25℃至55℃的温度范围内的对0.1~1 g的质量感知准确率高达99.70%,无需进行温度补偿即可实现大跨度温度下的精确质量感知。

关键词：压电悬臂梁深度学习 CNN CBAM 质量感知温度解耦

Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis

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Chinese Journal of Mechanical Engineering 2023年第1期36卷 1-18页

作者： Zuogang Shang Zhibin Zhao ruqiang yan The State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an710049China

Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.

关键词： Signal processing Deep learning Explainable Denoising Fault diagnosis

Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis

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Chinese Journal of Electronics 2021年第1期30卷 18-25页

作者： yan ruqiang SHEN Fei ZHOU Mengjie School of Mechanical Engineering Xi'an Jiaotong University School of Instrument Science and Engineering Southeast University

This paper presents a transfer learning-based approach for induction motor fault diagnosis,where the Transfer principal component analysis(TPCA)is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution difference between training and testing data by mapping cross-domain data into a shared latent space in which domain difference can be reduced. The trained model can achieve a good performance in testing data by using the learned features consisting of common latent principal components. Experimental results show that the proposed approach outperforms traditional machine learning techniques and can diagnose induction motor fault under various working conditions effectively.

关键词： Fault diagnosis Transfer principal component analysis Transfer learning Various working conditions Induction motors

降噪混合注意力变分自编码器及其在轴向柱塞泵故障诊断中的应用

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机械工程学报 2024年第4期60卷 167-177页

作者：王志颖李天福许文纲孙闯张军辉徐兵严如强西安交通大学机械工程学院西安710049 洛桑联邦理工大学智能运维与运营系统实验室瑞士洛桑1015 浙江大学流体动力与机电系统国家重点实验室杭州310027

轴向柱塞泵作为液压系统的供能元件,其故障诊断研究具有重要意义。现有故障诊断方法大多依赖人工经验,提取故障特征需要大量专家知识,且对数据和噪声的鲁棒性较差。针对轴向柱塞泵服役环境复杂、监测信号易受噪声干扰的问题,提出端到端... 详细信息

轴向柱塞泵作为液压系统的供能元件,其故障诊断研究具有重要意义。现有故障诊断方法大多依赖人工经验,提取故障特征需要大量专家知识,且对数据和噪声的鲁棒性较差。针对轴向柱塞泵服役环境复杂、监测信号易受噪声干扰的问题,提出端到端的降噪混合注意力变分自编码器方法,直接提取淹没在噪声中的故障特征,从而实现噪声环境下轴向柱塞泵故障诊断。该方法利用卷积变分自编码器从柱塞泵压力信号和振动信号中提取故障特征,通过引入混合注意力机制,实现编码器隐层特征的加权融合,以增强故障特征并削弱噪声影响;进一步使用自适应软阈值降噪方法,降低噪声干扰,完成强噪声环境下轴向柱塞泵故障诊断。通过轴向柱塞泵故障模拟试验和噪声鲁棒性试验对所提出方法的有效性进行验证,结果表明该方法在5dB噪声下的诊断准确率高达99.32%,在-5d B的强噪声影响下仍能保持69.72%的准确率,优于常用的故障诊断方法。

关键词：轴向柱塞泵故障诊断变分自编码器注意力机制软阈值降噪

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

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Chinese Journal of Mechanical Engineering 2021年第1期34卷 168-180页

作者： Chao Chen Fei Shen Jiawen Xu ruqiang yan School of Instrument Science and Engineering Southeast UniversityNanjing 210096China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an 710049China

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering ***,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more *** this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working *** 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 *** 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 *** experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working *** proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

关键词： Gear fault diagnosis Model parameter transfer Varying working conditions Least square support vector machine

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

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Chinese Journal of Mechanical Engineering 2020年第1期33卷 182-196页

作者： Fei Shen Chao Chen Jiawen Xu ruqiang yan School of Instrument Science and Engineering Southeast UniversityNanjing 210096China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Most gear fault diagnosis(GFD)approaches su er from ine ciency when facing with multiple varying working conditions at the same *** 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 *** short-time Fourier transform(STFT)is first used to obtain the time-frequency features from the gear vibration ***,the optimal clustering numbers are estimated using the Bayesian information criterion(BIC)theory,which possesses the simultaneous assessment capability,compared with traditional validity ***,the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column ***,the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA)strategy in order to achieve reliable diagnostic *** Spectra Quest’s Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.

关键词： Gear fault diagnosis Non-negative matrix factorization Co-clustering Varying working conditions

可解释人工智能在工业智能诊断中的挑战和机遇:先验赋能

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机械工程学报 2024年第12期60卷 1-20页

作者：严如强商佐港王志颖许文纲赵志斌王诗彬陈雪峰西安交通大学机械工程学院西安710049

进入“大数据”时代,人工智能技术因其强大的数据挖掘与学习能力,成为工业智能诊断领域的重要方法,在机械装备的异常检测、故障诊断和寿命预测等方面发挥重要作用。随着机械装备日益向大型化、高速化、集成化和自动化发展,诊断方法的可... 详细信息

进入“大数据”时代,人工智能技术因其强大的数据挖掘与学习能力,成为工业智能诊断领域的重要方法,在机械装备的异常检测、故障诊断和寿命预测等方面发挥重要作用。随着机械装备日益向大型化、高速化、集成化和自动化发展,诊断方法的可信度变得至关重要。因此弱可解释性正成为人工智能技术在诊断领域实际应用的巨大障碍。为了推动人工智能技术在工业智能诊断领域的发展,对可解释人工智能方法进行综述。首先介绍可解释性技术的概念与作用原理,并对目前可解释性技术的主要观点与分类进行总结。接着,从工业诊断中常用的信号处理先验和物理知识先验角度,概述内在可解释的先验赋能可解释技术的研究现状。最后指出先验赋能可解释技术存在的挑战与机遇。

关键词：智能诊断可解释性先验赋能信号处理物理知识

学科交叉背景下高校实验室安全管理探索与实践

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实验室研究与探索 2024年第9期43卷 224-228,240页

作者：侯雨杉张留洋严如强丁立库谷文媛杨凡凡西安交通大学机械工程学院西安710049 西安交通大学实验室管理处西安710049

实验室为现代大学重要的教学与科研场所,其安全稳定的运行对科研工作、人才培养以及学术交流都至关重要。随着不同学科间的交叉与融合成为新常态,实验室安全管理工作也面临着前所未有的新挑战。通过剖析多学科交叉实验室的特点与管理难... 详细信息

实验室为现代大学重要的教学与科研场所,其安全稳定的运行对科研工作、人才培养以及学术交流都至关重要。随着不同学科间的交叉与融合成为新常态,实验室安全管理工作也面临着前所未有的新挑战。通过剖析多学科交叉实验室的特点与管理难点,提出了适用于此类实验室的管理举措:完善管理制度,建立3级协同管理责任体系;制定全周期安全教育方案,强化安全培训;依托安全网格员队伍,实现闭环管理;分类分级管理实验室设备,精准防控风险;全面营造安全文化,激发成员责任意识。这些措施提升了实验室运行效率,且有效地促进了科研的发展与创新,可为其他多学科交叉实验室的安全管理提供参考依据。

关键词：实验室安全学科交叉实验室实验室管理