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学者姓名:崔玲丽
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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 等. "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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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 等. "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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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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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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The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.
Keyword :
VMSMF VMSMF fault diagnosis fault diagnosis variable speed variable speed TDR TDR planetary gearbox planetary gearbox
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GB/T 7714 | Liu, Tongtong , Cui, Lingli , Zhang, Chao . Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering [J]. | SYMMETRY-BASEL , 2021 , 13 (1) . |
MLA | Liu, Tongtong et al. "Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering" . | SYMMETRY-BASEL 13 . 1 (2021) . |
APA | Liu, Tongtong , Cui, Lingli , Zhang, Chao . Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering . | SYMMETRY-BASEL , 2021 , 13 (1) . |
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Abstract :
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
Keyword :
自适应分解 自适应分解 阶次分析 阶次分析 变分模态分解 变分模态分解 变转速齿轮箱 变转速齿轮箱
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GB/T 7714 | 冯刚 , 刘桐桐 , 崔玲丽 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 [J]. | 冯刚 , 2021 , 45 (1) : 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" . | 冯刚 45 . 1 (2021) : 34-39,84 . |
APA | 冯刚 , 刘桐桐 , 崔玲丽 , 机械传动 . 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 . | 冯刚 , 2021 , 45 (1) , 34-39,84 . |
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The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed, and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.
Keyword :
Fault frequency amplitude Fault frequency amplitude Singular value decomposition Singular value decomposition Fault information content index Fault information content index Rolling bearing Rolling bearing
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GB/T 7714 | Cui, Lingli , Sun, Mengxin , Zha, Chunqing . Early bearing fault diagnosis based on the improved singular value decomposition method [J]. | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2021 , 124 (11-12) : 3899-3910 . |
MLA | Cui, Lingli et al. "Early bearing fault diagnosis based on the improved singular value decomposition method" . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 124 . 11-12 (2021) : 3899-3910 . |
APA | Cui, Lingli , Sun, Mengxin , Zha, Chunqing . Early bearing fault diagnosis based on the improved singular value decomposition method . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2021 , 124 (11-12) , 3899-3910 . |
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Compound faults diagnosis of bearings and gears in gearboxes is a full-challenging task. The rational usage of meshing resonance is probable to be one of the breakthroughs to solve this problem. However, the existing detection method of meshing resonance is sensitive to noise and has risks of false demodulation. Meanwhile, it is difficult to guarantee that isolated components only contain one fault characteristic. Based on the mentioned problems, a spectrum of full-band preprocessing and 2-D separation method is raised. First, the inverted editing method of the original signal is proposed to reduce noise in the full frequency band. Second, the established resonance detection diagram and the introduced amplitude-level decomposition technique separate the processing results in the frequency and amplitude dimensions. Finally, the separate components are detected, respectively, by envelope analysis. Both the simulation analysis and experimental verification do support the effectiveness of this method. In addition, this article compares the existing method and solves the essential problems as well. © 1963-2012 IEEE.
Keyword :
Separation Separation Fault detection Fault detection Resonance Resonance
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GB/T 7714 | Cui, Lingli , Sun, Yin , Wang, Xin et al. Spectrum-Based, Full-Band Preprocessing, and Two-Dimensional Separation of Bearing and Gear Compound Faults Diagnosis [J]. | IEEE Transactions on Instrumentation and Measurement , 2021 , 70 . |
MLA | Cui, Lingli et al. "Spectrum-Based, Full-Band Preprocessing, and Two-Dimensional Separation of Bearing and Gear Compound Faults Diagnosis" . | IEEE Transactions on Instrumentation and Measurement 70 (2021) . |
APA | Cui, Lingli , Sun, Yin , Wang, Xin , Wang, Huaqing . Spectrum-Based, Full-Band Preprocessing, and Two-Dimensional Separation of Bearing and Gear Compound Faults Diagnosis . | IEEE Transactions on Instrumentation and Measurement , 2021 , 70 . |
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Recently, the diagnosis of rotating machines based on deep learning models has achieved great success. Many of these intelligent diagnosis models are assumed that training and test data are subject to independent identical distributions (IIDs). Unfortunately, such an assumption is generally invalid in practical applications due to noise disturbances and changes in workload. To address the above problem, this article presents a high-stability diagnosis model named the multiscale feature fusion convolutional neural network (MFF-CNN). MFF-CNN does not rely on tedious data preprocessing and target domain information. It is composed of multiscale dilated convolution, self-adaptive weighting, and the new form of maxout (NFM) activation. It extracts, modulates, and fuses the input samples' multiscale features so that the model focuses more on the health state difference rather than the noise disturbance and workload difference. Two diagnostic cases, including noisy cases and variable load cases, are used to verify the effectiveness of the present model. The results show that the present model has a strong health state identification capability and anti-interference capability for variable loads and noise disturbances.
Keyword :
rotating machines rotating machines intelligent diagnosis intelligent diagnosis Convolutional neural network (CNN) Convolutional neural network (CNN) feature learning feature learning feature fusion feature fusion
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GB/T 7714 | Wang, Pengxin , Song, Liuyang , Guo, Xudong et al. A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
MLA | Wang, Pengxin et al. "A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70 (2021) . |
APA | Wang, Pengxin , Song, Liuyang , Guo, Xudong , Wang, Huaqing , Cui, Lingli . A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
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