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Improved Multi-Bandwidth Mode Manifold for Enhanced Bearing Fault Diagnosis

Chinese Journal of Mechanical Engineering 2022年第1期35卷 179-191页

作者： Guifu Du Tao Jiang Jun Wang Xingxing Jiang zhongkui zhu School of Rail Transportation Soochow UniversitySuzhou 215131China

Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment(LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method.Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD(RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor(Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capab

关键词： Variational mode decomposition Manifold learning Natural nearest neighbor algorithm Rolling bearing Fault diagnosis Time-frequency signal decomposition

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Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

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Chinese Journal of Aeronautics 2023年第6期36卷 361-377页

作者： Bojian CHEN Changqing SHEN Juanjuan SHI Lin KONG Luyang TAN Dong WANG zhongkui zhu School of Rail Transportation Soochow UniversitySuzhou 215131China China Chang Guang Satellite Technology CO.LTD Changchun 130102China Department of Industrial Engineering and Management and in the State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200030China

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.

关键词： Catastrophic forgetting Continual learning Fault diagnosis Knowledge distillation Machine increments Stability-plasticity dilemma

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Synchronous chirp mode extraction: A promising tool for fault diagnosis of rolling element bearings under varying speed conditions

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Chinese Journal of Aeronautics 2022年第1期35卷 348-364页

作者： Xingxing JIANG Qiang HUANG Changqing SHEN Qian WANG Kun XU Jie LIU Juanjuan SHI zhongkui zhu School of Rail Transportation Soochow UniversitySuzhou 215131China Wenzheng College of Soochow University Suzhou 215104China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Sclool of Hydropower and Infonnation Engineering Huazhong University of Science and TechnologyWuhan 430074China

As critical components in modern aerospace productions,rolling element bearings(REBs)generally work under varying speed conditions,which brings great challenges to their operating health monitoring.Some novel time–frequency decomposition(TFD)algorithms are established recently to extract nonlinear features from the non-stationary signals effectively,which are promising for realizing fault diagnosis of REBs under varying speed conditions.However,numerous personal experiences must be incorporated and the anti-noise performance of these methods needs to be further enhanced.Given these issues,a synchronous chirp mode extraction(SCME)-based REB fault diagnosis method is proposed for the health monitoring of REBs under varying speed conditions in this study.It mainly consists of following two parts.(a)The shaft rotational frequency(SRF)is initially estimated from the low-frequency band of the vibration signal.Simultaneously,an adaptive refining strategy is incorporated to obtain a suitable bandwidth parameter.(b)A cycle-one-step estimation frame is constructed to extract synchronous modes from the envelope waveform of the vibration signal.Meanwhile,a synchronous mode spectrum(SMS)is generated using the information of the extracted synchronous modes,which is a novel REBs fault diagnosis technique with tacholess and resampling-free.In contrast to the current TFD algorithms,the proposed method needs fewer input parameters and owns a well anti-noise performance because there is no iterative optimization in the procedure of construction of SMS.As a result,the health conditions of REBs are evaluated by detecting the exhibited features in the SMS.Simulations and experiments are conducted to validate the effectiveness of the proposed method in terms of REB fault diagnosis.Analysis results demonstrate that the proposed method outperforms the current TFD algorithm and the conventional order tracking technique for fault diagnosis of REB under varying speed conditions.

关键词： Envelope analysis Fault diagnosis Order tracking Rolling element bearing Varying speed

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Iterative HOEO fusion strategy:a promising tool for enhancing bearing fault feature

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Frontiers of Mechanical Engineering 2023年第1期18卷 253-273页

作者： Xingxing JIANG Demin PENG Jianfeng GUO Jie LIU Changqing SHEN zhongkui zhu School of Rail Transportation Soochow UniversitySuzhou 215131China China Academy of Railway Sciences Co.Ltd. Beijing 100094China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China

As parameter independent yet simple techniques,the energy operator(EO)and its variants have received considerable attention in the field of bearing fault feature detection.However,the performances of these improved EO techniques are subjected to the limited number of EOs,and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction.As a result,the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises.To address these issues,this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively.Specifically,the proposed strategy is conducted through the following three steps.First,a multi-dimensional information matrix(MDIM)is constructed by performing the higher order energy operator(HOEO)on the analysis signal iteratively.MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region.Second,an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses.Third,the intrinsic manifolds are weighted to recover the fault-related transients.Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods,including HOEOs,the weighting HOEO fusion,the fast Kurtogram,and the empirical mode decomposition.

关键词： higher order energy operator fault diagnosis manifold learning rolling element bearing information fusion

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面向变工况下工业流数据故障诊断的持续迁移学习系统

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仪器仪表学报 2024年第4期45卷 10-16页

作者：石明宽丁传仓王锐黄伟国朱忠奎苏州大学轨道交通学院苏州215131

机器学习模型在智能故障诊断中取得了显著成功,但主要应用于静态场景。在实际场景中,新的故障类别数据以流形式不断产生,且数据分布随机械设备运行条件变化而发生变化,导致连续流数据具有非独立同分布的特征,这种面向非独立同分布连续... 详细信息

机器学习模型在智能故障诊断中取得了显著成功,但主要应用于静态场景。在实际场景中,新的故障类别数据以流形式不断产生,且数据分布随机械设备运行条件变化而发生变化,导致连续流数据具有非独立同分布的特征,这种面向非独立同分布连续流数据的诊断问题被称为持续迁移诊断问题。针对此问题,本文提出了一种基于持续迁移学习系统(CTLS)的故障诊断方法。该方法设计了域适应学习损失函数和持续迁移学习机制,能有效处理变工况下的工业流数据,无需重放旧类别数据便能够能学习新类别知识。此外,利用机械故障诊断案例评估该方法的性能,分析结果证明CTLS能够高效处理变工况条件下的工业流数据,是一种极具潜力的解决实际工业问题的可靠工具。

关键词：持续迁移学习工业流数据故障诊断旋转机械

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频域特征驱动的车辆旋转部件灰色故障诊断

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振动．测试与诊断 2024年第3期44卷 514-522,619,620页

作者：苏舟石娟娟关云辉张晶黄伟国沈长青朱忠奎苏州大学轨道交通学院苏州215131 中车戚墅堰机车车辆工艺研究所有限公司常州213011

根据不同故障引起的振动信号频域特征各异的特点,首先,运用调Q小波变换(tunable Q-factor wavelet transform,简称TQWT)的频率响应对车辆旋转部件振动信号或其包络信号进行分析,构建以信号在不同频段各子带能量占比为元素的特征向量;其... 详细信息

根据不同故障引起的振动信号频域特征各异的特点,首先,运用调Q小波变换(tunable Q-factor wavelet transform,简称TQWT)的频率响应对车辆旋转部件振动信号或其包络信号进行分析,构建以信号在不同频段各子带能量占比为元素的特征向量;其次,针对灰色接近关联度在处理2组相交的序列时存在的两序列变化趋势不同、原始累差小而导致关联度过大的问题,提出了灰色绝对接近关联度模型;最后,在所构建的频域特征向量驱动下,计算其与标准模式的灰色绝对接近关联度,对车辆关键旋转部件故障状态进行识别。利用所提方法对列车轮对轴承和汽车变速器齿轮箱不同运行状态的振动信号进行分析,结果表明,所提方法能够准确识别车辆旋转部件的运行状态和故障类型,通过对比分析验证了该方法的优越性。

关键词：轴承齿轮箱故障诊断故障辨识能量分布

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基于融合特征与数模联动的轴承寿命预测方法

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振动．测试与诊断 2023年第4期43卷 705-711,828,829页

作者：王前黄强江星星徐坤朱忠奎苏州城市学院光学与电子信息学院苏州215104 苏州大学轨道交通学院苏州215131 南京航空航天大学能源与动力学院南京210016

针对现有数模联动剩余使用寿命(remaining useful life,简称RUL)预测方法易受随机噪声干扰、没有考虑轴承的退化特性等问题,提出了一种基于融合指标与数模联动的轴承RUL预测方法,以提高原始数模联动RUL预测方法的准确性。首先,利用主成... 详细信息

针对现有数模联动剩余使用寿命(remaining useful life,简称RUL)预测方法易受随机噪声干扰、没有考虑轴承的退化特性等问题,提出了一种基于融合指标与数模联动的轴承RUL预测方法,以提高原始数模联动RUL预测方法的准确性。首先,利用主成分分析及指数加权移动平均算法融合表征轴承退化状态的多种特征,构建单调趋势良好的性能退化融合指标;其次,基于3σ准则建立一种首次预测时间确定方案,以触发RUL预测模型的启动,避免RUL预测的无效性;最后,考虑轴承的退化特性,嵌入Rauch-Tung-Striebel平滑滤波算法,以减小退化模型的随机波动,实现轴承RUL的可靠预测。仿真数据和试验信号分析结果证明了所提方法的有效性,相较于现有数模联动方法,显著提高了轴承RUL的预测精度。

关键词：剩余寿命预测数模联动滚动轴承融合特征平滑滤波

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Leaching kinetics of fluorine during the aluminum removal from spent Li-ion battery cathode materials

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Journal of Environmental Sciences 2024年第4期138卷 312-325页

作者： Shengjie Li Jianxin zhu Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China

The high content of aluminum(Al)impurity in the recycled cathode powder seriously affects the extraction efficiency of Nickel,Cobalt,Manganese,and Lithium resources and the actual commercial value of recycled materials,so Al removal is crucially important to conform to the industrial standard of spent Li-ion battery cathode materials.In this work,we systematically investigated the leaching process and optimum conditions associated with Al removal from the cathode powder materials collected in a wet cathode-powder peeling and recycling production line of spent Li-ion batteries(LIBs).Moreover,we specifically studied the leaching of fluorine(F)synergistically happened along with the removal process of Al,which was not concerned about in other studies,but one of the key factors affecting pollution prevention in the recovery process.The mechanism of the whole process including the leaching of Al and F from the cathode powder was indicated by using NMR,FTIR,and XPS,and a defluoridation process was preliminarily investigated in this study.The leaching kinetics of Al could be successfully described by the shrinking core model,controlled by the diffusion process and the activation energy was 11.14 kJ/mol.While,the leaching of F was attributed to the dissolution of LiPF6and decomposition of PVDF,and the kinetics associated was described by Avrami model.The interaction of Al and F is advantageous to realize the defluoridation to some degree.It is expected that our investigation will provide theoretical support for the large-scale recycling of spent LIBs.

关键词： Al removal F leaching Cathode powder Heating alkali leaching Leaching kinetics

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Electron beam lithography on nonplanar and irregular surfaces

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Microsystems & Nanoengineering 2024年第2期10卷 1-23页

作者： Chenxu zhu Huseyin Ekinci Aixi Pan Bo Cui Xiaoli zhu Department of Electrical and Computer Engineering and Waterloo Institute for Nanotechnology(WIN) University of WaterlooWaterlooONCanada

E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in size.However,alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular surfaces.In this review,we summarize the state of the art in nanofabrication on irregular substrates using e-beam lithography.To overcome these challenges,unconventional methods have been developed.For instance,polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular substrates.Moreover,chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these surfaces.In addition,thermal oxides can serve as resists,as the etching rate in solution changes after e-beam exposure.Furthermore,e-beam lithography tools can be combined with cryostages,evaporation systems,and metal deposition chambers for sample development and lift-off while maintaining low temperatures.Metallic nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive resistor.Additionally,Ti/Au caps can be patterned around a carbon nanotube.Moreover,3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused e-beam.These advances in e-beam lithography on irregular substrates,including uniform film coating,instrumentation improvement,and new pattern transferring method development,substantially extend its capabilities in the fabrication and application of nanoscale structures.

关键词： lithography beam coating

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Wood of trees:Cellular structure,molecular formation,and genetic engineering

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Journal of Integrative Plant Biology 2024年第3期66卷 443-467页

作者： Yingying zhu Laigeng Li State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems and College of Ecology Lanzhou UniversityLanzhou 730000China National Key Laboratory of Plant Molecular Genetics CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai 200032China

Wood is an invaluable asset to human society due to its renewable nature,making it suitable for both sustainable energy production and material manufacturing.Additionally,wood derived from forest trees plays a crucial role in sequestering a significant portion of the carbon dioxide fixed during photosynthesis by terrestrial plants.Nevertheless,with the expansion of the global population and ongoing industrialization,forest coverage has been substantially decreased,resulting in significant challenges for wood production and supply.Wood production practices have changed away from natural forests toward plantation forests.Thus,understanding the underlying genetic mechanisms of wood formation is the foundation for developing high-quality,fast-growing plantation trees.Breeding ideal forest trees for wood production using genetic technologies has attracted the interest of many.Tremendous studies have been carried out in recent years on the molecular,genetic,and cell-biological mechanisms of wood formation,and considerable progress and findings have been achieved.These studies and findings indicate enormous possibilities and prospects for tree improvement.This review will outline and assess the cellular and molecular mechanisms of wood formation,as well as studies on genetically improving forest trees,and address future development prospects.

关键词： cambium cell wall Populus wood formation xylem

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