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学者姓名：崔玲丽
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Abstract ：
Vibration signals collected from complex rotating machines often contain close-spaced or nonproportional instantaneous frequencies (IFs), including crossed IFs, and current time-frequency analysis (TFA) methods should be improved or are difficult to characterize the above IFs and detect mechanical faults with high time-frequency resolution. To tackle the above challenge, a TFA algorithm, termed local optimal scaling chirplet transform (CT) (LOSCT), is proposed. First, based on the scaling-basis CT (SBCT), the scaling chirplet basis is introduced to calculate various time-frequency representations (TFRs); then, Renyi entropy-based local optimal theory is constructed to capture local optimal TFRs, and finally, the local maximum extraction criterion is defined to calculate ideal time-frequency amplitudes on IF curves from the optimal TFR. The primary contribution is that the LOSCT can process nonstationary signals, whose IF are nonproportional or close-spaced, with high time-frequency concentration and detect mechanical faults. The LOSCT is verified by two simulated signals, whose IF curves are close-spaced or crossed, respectively. A comparative analysis with current TFA algorithms is used to evaluate the superiority of the developed technique. Finally, the engineering applications for processing mechanical vibration signals, i.e., fault bearing and planetary gearbox signals, are discussed.
Keyword ：
close-spaced frequencies close-spaced frequencies time-frequency analysis (TFA) time-frequency analysis (TFA) Chirplet transform (CT) Chirplet transform (CT) nonproportional frequencies nonproportional frequencies
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| GB/T 7714 | Zhao, Dezun , Wang, Honghao , Huang, Xiaofan et al. Local Optimal Scaling Chirplet Transform for Processing Nonstationary Mechanical Vibration Signals [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Zhao, Dezun et al. "Local Optimal Scaling Chirplet Transform for Processing Nonstationary Mechanical Vibration Signals" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Zhao, Dezun , Wang, Honghao , Huang, Xiaofan , Cui, Lingli . Local Optimal Scaling Chirplet Transform for Processing Nonstationary Mechanical Vibration Signals . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e. autoencoder, deep Boltzmann machine, convolutional neural network, transformer, generative adversarial network, and graph neural network, and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.
Keyword ：
vibration characteristic vibration characteristic deep learning deep learning planetary gearbox planetary gearbox fault diagnosis fault diagnosis
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| GB/T 7714 | Liu, Dongdong , Cui, Lingli , Cheng, Weidong . A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (1) . |
| MLA | Liu, Dongdong et al. "A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 1 (2024) . |
| APA | Liu, Dongdong , Cui, Lingli , Cheng, Weidong . A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (1) . |
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Abstract ：
Graph neural networks (GNNs) can capture interdependencies between data with the structured data modeling ability, and have received much attention from industry professionals in remaining useful life (RUL) prediction tasks. However, the existing methods assume that graph nodes and edges are of the same homogeneous attributes, which leads to information loss and cannot fully capture the complex degeneration pattern and topological relationship of the bearings. To solve this problem, a novel heterogeneous graph representation-driven multiplex aggregation graph neural network is proposed for bearing RUL prediction. Different from the conventional methods based on homogeneous graphs, we model the heterogeneous attributes of bearing data and parameterize the representation of node relationships in heterogeneous graphs. The node adjacency is represented as the heterogeneity belonging to the designed spatial meta-path and temporal meta-path, respectively. In addition, a multiplex aggregation heterogeneous graph neural network (MAHGNN) is proposed to extract heterogeneous features of the graph as well as temporal dependencies of each node and achieve the bearing RUL prediction. In particular, a novel hierarchical aggregation mechanism for graph heterogeneous attributes is designed, which includes node-level aggregation, path-level aggregation and time-level aggregation. This mechanism can capture the diverse relationships and significance of various types of nodes and edges in heterogeneous graphs, so as to aggregate the feature information of nodes within a meta-path and different meta-paths as well as extract the temporal dependencies. The experiments conducted on two datasets provide evidence for the superiority of the proposed method in comparison to other state-of-the-art RUL prediction methods based on homogeneous graphs.
Keyword ：
Rolling bearings Rolling bearings Heterogeneous graph Heterogeneous graph Meta -paths Meta -paths Remaining useful life Remaining useful life Graph neural network Graph neural network
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| GB/T 7714 | Xiao, Yongchang , Liu, Dongdong , Cui, Lingli et al. Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 220 . |
| MLA | Xiao, Yongchang et al. "Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 220 (2024) . |
| APA | Xiao, Yongchang , Liu, Dongdong , Cui, Lingli , Wang, Huaqing . Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 220 . |
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Abstract ：
Under strong noise, bearing fault-related instantaneous frequency (IF) is difficult to extract by time-frequency analysis (TFA)-based ridge extraction method; hence, the tacholess order tracking is unsuitable for characterizing bearing fault characteristic frequency (FCF). To address the above problem, an IF estimation-based order tracking is developed in this article. The fundamental principle of the developed technique is to obtain the IF through the defined instantaneous frequency estimation operator (IFEO) and recovery factor, and then the initial signal is resampled using the IF to achieve bearing fault diagnosis. Specifically, the IFEO is first defined based on the normalization theory, and then the pseudo signal is obtained by resampling the original signal through the IFEO that can match the frequency-modulated (FM) law of the original signal. Second, the spectra concentration index is constructed to calculate the optimal IFEO. Third, the recovery factor corresponding to the optimal IFEO is calculated by searching the highest peak from the envelope spectrogram of the pseudo signal, and then the IF of the maximum amplitude component is calculated. Finally, based on the IF, the bearing signal is resampled, and the fault characteristic order (FCO) spectrum is obtained to detect the bearing fault type. Analysis results of the simulated and measured bearing signals indicate that the developed technique can accurately predict the IF and detect the bearing fault and has better effectiveness in calculating IF and identifying bearing fault type than the traditional ridge extraction method under strong noise.
Keyword ：
Demodulation Demodulation Vibrations Vibrations Rolling bearings Rolling bearings rolling bearing rolling bearing Transforms Transforms Time-frequency analysis Time-frequency analysis Frequency estimation Frequency estimation order tracking order tracking time-varying rotational speed time-varying rotational speed Chirp Chirp Fault detection Fault detection instantaneous frequency estimation operator (IFEO) instantaneous frequency estimation operator (IFEO) recovery factor recovery factor
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| GB/T 7714 | Cui, Lingli , Yan, Long , Zhao, Dezun . Instantaneous Frequency Estimation-Based Order Tracking for Bearing Fault Diagnosis Under Strong Noise [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (24) : 30940-30949 . |
| MLA | Cui, Lingli et al. "Instantaneous Frequency Estimation-Based Order Tracking for Bearing Fault Diagnosis Under Strong Noise" . | IEEE SENSORS JOURNAL 23 . 24 (2023) : 30940-30949 . |
| APA | Cui, Lingli , Yan, Long , Zhao, Dezun . Instantaneous Frequency Estimation-Based Order Tracking for Bearing Fault Diagnosis Under Strong Noise . | IEEE SENSORS JOURNAL , 2023 , 23 (24) , 30940-30949 . |
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Abstract ：
针对传统Lemple?Ziv复杂度(Lempel?Ziv complexity,LZC)计算过程中,二值化处理时会改变原序列的动力学特征以及计算效率较低的问题,结合轴承故障冲击特征,提出复合字典匹配追踪算法(compound dictionary matching pursuit algorithm,CDMP)与变尺度Lempel?Ziv复杂度(variable scale Lempel?Ziv complexity,VLZC)分析相结合的滚动轴承内外圈损伤程度评估方法.采用CDMP对原信号进行重构,检测信号周期性冲击成分;根据冲击幅值将重构信号分为轴承故障冲击区和冲击衰减区,对信号冲击进行变尺度二值化处理后,将冲击作为迭代基本元素,采用遍历查找法计算其VLZC指标;根据3σ原则给出内外圈不同损伤程度的VLZC取值区间,引入BP神经网络对其损伤程度进行智能分类.结果表明,该方法能有效降噪,保留信号周期性冲击特征,抑制非冲击成分,提高迭代计算效率,实现滚动轴承内外圈损伤程度的评估.
Keyword ：
故障诊断 故障诊断 BP神经网络 BP神经网络 复合字典匹配追踪 复合字典匹配追踪 二值化 二值化 变尺度Lempel-Ziv算法 变尺度Lempel-Ziv算法
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| GB/T 7714 | 崔玲丽 , 安加林 , 王鑫 et al. 基于变尺度Lempel?Ziv的滚动轴承损伤程度评估方法 [J]. | 振动工程学报 , 2022 , 35 (5) : 1250-1258 . |
| MLA | 崔玲丽 et al. "基于变尺度Lempel?Ziv的滚动轴承损伤程度评估方法" . | 振动工程学报 35 . 5 (2022) : 1250-1258 . |
| APA | 崔玲丽 , 安加林 , 王鑫 , 张建宇 . 基于变尺度Lempel?Ziv的滚动轴承损伤程度评估方法 . | 振动工程学报 , 2022 , 35 (5) , 1250-1258 . |
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Abstract ：
针对裂纹引起齿轮时变啮合刚度(TVMS)减小这一现象，研究了裂纹故障对TVMS的影响规律。首先，构建了完整的轮齿齿廓曲线，基于传统势能法分析了相邻齿耦合效应对TVMS的影响，对TVMS计算公式进行修正。其次，采用有限元法确定了裂纹萌生点所在位置，提出了一种沿深度拓展的裂纹曲线，分析了裂纹深度对TVMS和负载分担比的影响，研究了裂纹同时沿深度与长度方向拓展的中早期故障模型。最后，构建了不同故障齿轮副模型，采用有限元法对裂纹沿深度结果进行验证，结果表明势能法与有限元法相吻合。
Keyword ：
时变啮合刚度 时变啮合刚度 势能法 势能法 裂纹 裂纹 故障模型 故障模型 有限元法 有限元法
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| GB/T 7714 | 孟宗 , 李佳松 , 潘作舟 et al. 裂纹故障对轮齿时变啮合刚度的影响分析 [J]. | 中国机械工程 , 2022 , 33 (16) : 1897-1905,1911 . |
| MLA | 孟宗 et al. "裂纹故障对轮齿时变啮合刚度的影响分析" . | 中国机械工程 33 . 16 (2022) : 1897-1905,1911 . |
| APA | 孟宗 , 李佳松 , 潘作舟 , 庞修身 , 崔玲丽 , 樊凤杰 . 裂纹故障对轮齿时变啮合刚度的影响分析 . | 中国机械工程 , 2022 , 33 (16) , 1897-1905,1911 . |
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Abstract ：
针对传统最大类间方差法（Maximum Between-Class Variance,MBCV）在分离轴承故障信号过程中存在的分割阈值适应性差、分离效果不佳的问题，提出一种基于MBCV动态阈值曲线的滚动轴承故障诊断方法。该方法通过MBCV法获得频谱均分子区间的各分割阈值，然后高阶拟合各部分阈值进而获得动态阈值曲线，再通过调整优化频谱分段数量并以分离信号与原信号之间的均方根误差最小化为目标确定最优阈值曲线；依据最优动态阈值曲线将信号频谱分割为高、低两部分，对低幅值部分进行傅里叶逆变换及平方包络谱分析进而诊断故障。此方法能有效消除强干扰成分，最大化提取轴承故障特征。实验分析结果表明，相比于传统MB...
Keyword ：
MBCV算法 MBCV算法 轴承 轴承 阈值曲线 阈值曲线 故障诊断 故障诊断 平方包络谱 平方包络谱
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| GB/T 7714 | 吴超 , 崔玲丽 , 张建宇 et al. 改进MBCV法在滚动轴承故障诊断中的应用 [J]. | 振动工程学报 , 2022 , 35 (04) : 942-948 . |
| MLA | 吴超 et al. "改进MBCV法在滚动轴承故障诊断中的应用" . | 振动工程学报 35 . 04 (2022) : 942-948 . |
| APA | 吴超 , 崔玲丽 , 张建宇 , 王鑫 . 改进MBCV法在滚动轴承故障诊断中的应用 . | 振动工程学报 , 2022 , 35 (04) , 942-948 . |
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Abstract ：
Accurate modeling of the vibration signal model of planetary gearboxes is essential for the subsequent fault diagnosis. According to the existing improved phenomenological model based on the meshing vibration, this paper conducts further investigations on the vibration mechanism of the gearbox. The time delay phenomenon of faulty ring gear tooth participating in meshing is theoretically analyzed, and the assisted phases for experimental verification are proposed and deduced. Based on the improved model, the vibration signal under the fault condition is simulated and compared with the results of traditional methods. Subsequently, the paper proposes to divide and reconstruct this signal and use the maximum correlation kurtosis deconvolution (MCKD) to enhance its impact characteristics. The results show that the phase between the fault impact and its adjacent meshing impact is consistent with the proposed assisted phase. Finally, the correctness of the vibration mechanism and the improved phenomenological model are verified experimentally.
Keyword ：
Planetary gearbox Planetary gearbox MCKD MCKD Gear fault Gear fault Phenomenological model Phenomenological model Vibration mechanism Vibration mechanism
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| GB/T 7714 | Luo, Yingchao , Cui, Lingli , Zhang, Jianyu et al. Vibration mechanism and improved phenomenological model of the planetary gearbox with broken ring gear fault [J]. | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY , 2021 , 35 (5) : 1867-1879 . |
| MLA | Luo, Yingchao et al. "Vibration mechanism and improved phenomenological model of the planetary gearbox with broken ring gear fault" . | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 35 . 5 (2021) : 1867-1879 . |
| APA | Luo, Yingchao , Cui, Lingli , Zhang, Jianyu , Ma, Jianfeng . Vibration mechanism and improved phenomenological model of the planetary gearbox with broken ring gear fault . | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY , 2021 , 35 (5) , 1867-1879 . |
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Abstract ：
The lumped parameter model (LPM) is a common modeling method for simulating the vibration signal of the planetary gearbox. Generally, the simulation results are not directly used for the subsequent analysis, because two factors need to be considered: the transmission paths of vibration signals and the amplitude modulation effect of the carrier. However, both of these factors are subjectively affected. In order to overcome this problem, a new method for generating the gearbox vibration signal is proposed. Based on LPM, the vibration displacement of each component is solved, and meshing forces of the internal gear pairs are calculated. Then, the ring gear with bolt constraints is simplified to the Euler-Bernoulli beam, and the vibration signal of any point on the ring gear is established by solving the vibration of the beam. Subsequently, based on the new signal model, the transmission mechanism, the amplitude modulation and the overlapping phenomenon of the vibration signal, and the effect of bolt constraint on the vibration of the gearbox are analyzed. Finally, the correctness of the new method and the effect of bolt constraint on the vibration signal are verified by experiments. © 2021
Keyword ：
Epicyclic gears Epicyclic gears Amplitude modulation Amplitude modulation Bolts Bolts Vibration analysis Vibration analysis
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| GB/T 7714 | Luo, Yingchao , Cui, Lingli , Ma, Jianfeng . Effect of bolt constraint of ring gear on the vibration response of the planetary gearbox [J]. | Mechanism and Machine Theory , 2021 , 159 . |
| MLA | Luo, Yingchao et al. "Effect of bolt constraint of ring gear on the vibration response of the planetary gearbox" . | Mechanism and Machine Theory 159 (2021) . |
| APA | Luo, Yingchao , Cui, Lingli , Ma, Jianfeng . Effect of bolt constraint of ring gear on the vibration response of the planetary gearbox . | Mechanism and Machine Theory , 2021 , 159 . |
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Abstract ：
变转速齿轮箱由于工况复杂导致转频不稳定,齿轮箱的微弱故障信号可能会被掩盖在强噪声中,不能直接应用传统的时频分析方法,为故障特征的提取增加一定的难度。针对变转速信号的处理,传统的计算阶次分析方式(COT)很好地解决了变转速齿轮箱的故障特征难以提取出来的问题,但由于传统COT中所使用的重采样方法是基于样条插值法的,无法根据转频选取转频,导致重采样间隔并不均匀;提出了改进的阶次分析方法,根据采样的各点角速度依次进行重采样,提高了阶次分析的精度。同时,变转速齿轮箱因动力传递复杂,导致变转速齿轮箱噪声更加严重。变分模态分解(VMD)常被被用来去除复杂信号噪声,提取被掩盖在强噪声中的微弱故障信号。提出了自...
Keyword ：
变转速齿轮箱 变转速齿轮箱 阶次分析 阶次分析 自适应分解 自适应分解 变分模态分解 变分模态分解
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| GB/T 7714 | 冯刚 , 刘桐桐 , 崔玲丽 . 基于改进阶次分析与自适应VMD的变转速齿轮箱故障诊断研究 [J]. | 机械传动 , 2021 , 45 (01) : 34-39,84 . |
| MLA | 冯刚 et al. "基于改进阶次分析与自适应VMD的变转速齿轮箱故障诊断研究" . | 机械传动 45 . 01 (2021) : 34-39,84 . |
| APA | 冯刚 , 刘桐桐 , 崔玲丽 . 基于改进阶次分析与自适应VMD的变转速齿轮箱故障诊断研究 . | 机械传动 , 2021 , 45 (01) , 34-39,84 . |
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