您的检索：
学者姓名：崔玲丽
精炼检索结果：
年份
成果类型
收录类型
来源
综合
合作者
语言
清除所有精炼条件
摘要 ：
As key rotational components of wind turbines, planetary gearboxes, and bearings need special health monitoring and fault diagnosis for reducing downtime and maintenance costs. However, it is still a challenging issue for time-frequency analysis (TFA) techniques to analyze nonstationary and close-spaced fault frequencies of wind turbines. Hence, a high- concentration TFA technique, termed frequency-chirprate synchrosqueezing-based scaling chirplet transform (FCSSCT) is developed. In the FCSSCT, the novel dimension of chirprate is introduced to map the signal into the three-dimensional space of time-frequency-chirprate in contrast to the time-frequency domain in the synchrosqueezing transform (SST), herein, the three-dimensional space of time, frequency and chirprate is calculated based on the scaling-basis chirplet transform (SBCT); then, a frequency-chirprate synchrosqueezing operator (FCSO) is defined in the frequency-chirprate domain to reassign the amplitude coefficients of the SBCT results and a three-dimensional representation of time-frequency-chirprate is obtained; finally, the time-frequency representation (TFR) with concentrated energy is obtained by transforming the three-dimensional representation of the time-frequency-chirprate space into the time-frequency domain. The effectiveness of the developed FCSSCT is verified by the simulated multi-component signals with close-spaced frequencies or crossover frequencies. Experimental analysis results on wind turbine planetary gearbox and bearing show that the developed technique is effective in characterizing nonstationary fault frequencies. Re ' nyi entropies demonstrate that the FCSSCT has much better energy concentration compared with several advanced TFA methods.
关键词 ：
Frequency-chirprate domain Frequency-chirprate domain Time -frequency analysis Time -frequency analysis operator operator Wind turbine Wind turbine Frequency-chirprate synchrosqueezing Frequency-chirprate synchrosqueezing Fault diagnosis Fault diagnosis
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Zhao, Dezun , Wang, Honghao , Cui, Lingli . Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time-frequency representation [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 209 . |
| MLA | Zhao, Dezun 等. "Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time-frequency representation" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 209 (2024) . |
| APA | Zhao, Dezun , Wang, Honghao , Cui, Lingli . Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time-frequency representation . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 209 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
This paper addresses the significant challenge of predicting the Remaining Useful Life (RUL) of mechanical equipment, a critical aspect of predictive maintenance and reliability engineering. Traditional deep learning methods in RUL prediction have been hindered by key challenges, including the scarcity of comprehensive lifecycle data, the prevalence of high -frequency noise in sensor readings, and a heavy reliance on supervised learning. To overcome these challenges, we propose a novel methodology that synergizes self -supervised and supervised learning. Our approach uniquely leverages non -full lifecycle data abundant in industrial settings, thereby bypassing the limitations posed by data scarcity. The model undergoes a two -stage training process, initially learning from vast quantities of non -full lifecycle data in a self -supervised manner, followed by finetuning in a supervised phase with available full lifecycle data. We employ Contrastive Predictive Coding (CPC) for the encoder and a Transformer -based decoder, a combination adept at extracting low -frequency, significant features from the sensor data and effectively predicting RUL. The paper demonstrates the efficacy of our approach through comprehensive experiments testing on both bearing datasets from experimental setup and wheelset datasets from urban rail train, showing superior or comparable performance against state-of-the-art methods. Our results, supported by ablation studies, suggest the potential robustness and innovative aspects of our model, indicating it could contribute meaningfully to the field of predictive maintenance.
关键词 ：
Multi-head self-attention mechanism Multi-head self-attention mechanism Remaining Useful Life (RUL) Remaining Useful Life (RUL) Self-supervised learning Self-supervised learning Deep Learning (DL) Deep Learning (DL)
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Lin, Tianjiao , Song, Liuyang , Cui, Lingli et al. Advancing RUL prediction in mechanical systems: A hybrid deep learning approach utilizing non-full lifecycle data [J]. | ADVANCED ENGINEERING INFORMATICS , 2024 , 61 . |
| MLA | Lin, Tianjiao et al. "Advancing RUL prediction in mechanical systems: A hybrid deep learning approach utilizing non-full lifecycle data" . | ADVANCED ENGINEERING INFORMATICS 61 (2024) . |
| APA | Lin, Tianjiao , Song, Liuyang , Cui, Lingli , Wang, Huaqing . Advancing RUL prediction in mechanical systems: A hybrid deep learning approach utilizing non-full lifecycle data . | ADVANCED ENGINEERING INFORMATICS , 2024 , 61 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The complexity of the internal structure of rolling bearings and the harshness of their operating environment result in strong non-stationarity and nonlinearity of the vibration signals. It remains a challenging and attractive task to accomplish more accurate classification through signal processing techniques and pattern recognition methods. To realize this aim, a novel one-dimensional improved self-attention-enhanced convolutional neural network (1D-ISACNN) with empirical wavelet transform (EWT) is proposed for rolling bearing fault classification. Firstly, the EWT algorithm is employed to decompose the raw signal into three frequency components, allowing for further extraction of multi-frequency components to enhance signal characteristics. Subsequently, a creative1D-ISACNN leverages the merits of a newly developed attention mechanism and an optimized meta-activation concatenation function in feature learning to better capture and map crucial information within the signal. Furthermore, label smoothing regularization is designed as the loss function of the 1D-ISACNN, which takes into account not only the loss of correctly labeled positions in the training samples but also the loss of other mislabeled positions. Finally, the adaptive moment projection estimation is designed to ensure a more robust gradient update strategy for updating the parameters of the proposed model. The developed model tested on three different sets of bearing data, has achieved a classification accuracy of 100%. In ablative experiments and other comparative experiments, the proposed method demonstrates higher recognition accuracy and more robust generalization capabilities compared to other excellent approaches.
关键词 ：
Meta-activation concatenation Meta-activation concatenation Label smooth regular Label smooth regular Attention mechanism Attention mechanism Adaptive moment projection estimation Adaptive moment projection estimation Empirical wavelet transforms Empirical wavelet transforms
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Dong, Zhilin , Zhao, Dezun , Cui, Lingli . An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform [J]. | NONLINEAR DYNAMICS , 2024 , 112 (8) : 6439-6459 . |
| MLA | Dong, Zhilin et al. "An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform" . | NONLINEAR DYNAMICS 112 . 8 (2024) : 6439-6459 . |
| APA | Dong, Zhilin , Zhao, Dezun , Cui, Lingli . An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform . | NONLINEAR DYNAMICS , 2024 , 112 (8) , 6439-6459 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Dictionary learning has gradually attracted attention due to its powerful feature representation ability. However, the time -shift property of collected signals hinders the recognition of various bearing states. In addition, existing dictionary learning methods are mostly designed based on a single domain, while common data fusion methods used in data -driven cannot be directly extended to dictionary learning. In this paper, a novel adaptive generalized domain data fusion -driven kernel sparse representation classification method (AGDFDK-SRC) is proposed. First, to avoid the effect of the time -shift property on dictionary learning, a class -specific kernel sub -dictionary learning method is proposed, by which the non-linear signal data is mapped into high -dimensional feature space via a kernel trick. Second, the class -specific kernel sub -dictionaries are learned by kernel K -singular value decomposition in a data -driven manner. Then, an adaptive generalized domain data fusion strategy is developed for dictionary learning, which implements data fusion of multiple domain signals to enhance the feature mining ability and representation ability of the learned dictionary. Finally, a kernel sparse classification method is designed to achieve intelligent bearing fault diagnosis. Two bearing datasets are exploited to verify the recognition performance of AGDFDK-SRC, indicating that the AGDFDK-SRC obtains superior average classification accuracies of 98.23% and 99.50%, respectively.
关键词 ：
Adaptive generalized domain data fusion Adaptive generalized domain data fusion Kernel sub -dictionary learning Kernel sub -dictionary learning Bearing fault diagnosis Bearing fault diagnosis Sparse representation classification Sparse representation classification Data -driven Data -driven
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Cui, Lingli , Jiang, Zhichao , Liu, Dongdong et al. A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| MLA | Cui, Lingli et al. "A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis" . | EXPERT SYSTEMS WITH APPLICATIONS 247 (2024) . |
| APA | Cui, Lingli , Jiang, Zhichao , Liu, Dongdong , Wang, Huaqing . A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In current transient signals processing-related time-frequency analysis (TFA) algorithms, the group delay (GD) is primarily obtained by its definition, meanwhile, these TFA methods suffer from repeated assignment and nonreassigned point problems, which result in unsatisfactory time-frequency resolution. Hence, a novel TFA technique called horizontal reassigning transformation (HRT) is proposed for handling transient signals. In the developed technique, the Gaussian window function-based GD estimation criterion, which is fully different from traditional GD calculation methods, is defined, and the criterion contains three parts, namely, local maximum locating of the Gaussian window function, zeros searching of the first-order derivative of the Gaussian window function, and local minima locating of the absolute value of the first-order derivative of the Gaussian window function. With the estimated GD, the horizontal reassigning operator (HRO) is devised to adaptively extract the time-frequency coefficients along the GD trajectory and discard the coefficients that cause the time-frequency blurring. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HRT. Compared with the other seven typical TFA algorithms, the developed technique can accurately extract GD and eliminate repeated assignment and nonreassigned point problems. Analysis results of the bearing fault impact signals show that the proposed HRT can display the time when pulses occur while ensuring high time-frequency resolution, making it suitable for detecting the bearing fault.
关键词 ：
Uncertainty Uncertainty Time-frequency analysis Time-frequency analysis Transforms Transforms Group delay (GD) estimation Group delay (GD) estimation time-frequency analysis (TFA) time-frequency analysis (TFA) post-processing post-processing Signal resolution Signal resolution transient signal transient signal Transient analysis Transient analysis Sensor phenomena and characterization Sensor phenomena and characterization Interference Interference
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Zhao, Dezun , Huang, Xiaofan , Cui, Lingli . Horizontal Reassigning Transform for Bearing Fault Impulses Characterizing [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (2) : 1837-1846 . |
| MLA | Zhao, Dezun et al. "Horizontal Reassigning Transform for Bearing Fault Impulses Characterizing" . | IEEE SENSORS JOURNAL 24 . 2 (2024) : 1837-1846 . |
| APA | Zhao, Dezun , Huang, Xiaofan , Cui, Lingli . Horizontal Reassigning Transform for Bearing Fault Impulses Characterizing . | IEEE SENSORS JOURNAL , 2024 , 24 (2) , 1837-1846 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Conventional convolutional neural networks (CNNs) predominantly emphasize spatial features of signals and often fall short in prioritizing sequential features. As the number of layers increases, they are prone to issues such as vanishing or exploding gradients, leading to training instability and subsequent erratic fluctuations in loss values and recognition rates. To address this issue, a novel hybrid model, termed one-dimensional (1D) residual network with attention mechanism and bidirectional gated recurrent unit (BGRU) is developed for rotating machinery fault classification. First, a novel 1D residual network with optimized structure is constructed to obtain spatial features and mitigate the gradient vanishing or exploding. Second, the attention mechanism (AM) is designed to catch important impact characteristics for fault samples. Next, temporal features are mined through the BGRU. Finally, feature information is summarized through global average pooling, and the fully connected layer is utilized to output the final classification result for rotating machinery fault diagnosis. The developed technique which is tested on one set of planetary gear data and three different sets of bearing data, has achieved classification accuracy of 98.5%, 100%, 100%, and 100%, respectively. Compared with other methods, including CNN, CNN-BGRU, CNN-AM, and CNN with an AM-BGRU, the proposed technique has the highest recognition rate and stable diagnostic performance.
关键词 ：
fault diagnosis fault diagnosis residual network residual network attention mechanism attention mechanism bidirectional gated recurrent unit bidirectional gated recurrent unit rotating machinery rotating machinery
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Dong, Zhilin , Zhao, Dezun , Cui, Lingli . Rotating machinery fault classification based on one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (8) . |
| MLA | Dong, Zhilin et al. "Rotating machinery fault classification based on one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 8 (2024) . |
| APA | Dong, Zhilin , Zhao, Dezun , Cui, Lingli . Rotating machinery fault classification based on one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (8) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
fault diagnosis of wind turbines under nonstationary conditions is still challenging. This paper proposes a novel tacho-less generalized demodulation (NTLGD) method for the wind turbine fault diagnosis. First, one instantaneous frequency is extracted from the time-frequency representation of the vibration signal. Second, a novel phase function design method is developed based on the extracted frequency, by which, different from traditional methods, the fault-related frequencies are mapped into the fixed predefined values. Then, the bandpass filters are designed according to the designed phase functions to separate the mapped frequencies. Finally, the diagnosis template is constructed, and the fault is localized by matching the peaks in the demodulated spectrum with the spectral lines in the template. The method is evaluated by the vibration signals of support bearings and the planetary gearbox in a test rig of wind turbine drive train. The results demonstrate that the proposed method can well pinpoint the fault-related frequency components without a tachometer and the demodulated values are independent of speed profiles.
关键词 ：
Frequency demodulation Frequency demodulation fault diagnosis fault diagnosis Wind turbine Wind turbine Nonstationary condition Nonstationary condition Tacho-less Tacho-less
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | Liu, Dongdong , Cui, Lingli , Cheng, Weidong . Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation [J]. | RENEWABLE ENERGY , 2023 , 206 : 645-657 . |
| MLA | Liu, Dongdong et al. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation" . | RENEWABLE ENERGY 206 (2023) : 645-657 . |
| APA | Liu, Dongdong , Cui, Lingli , Cheng, Weidong . Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation . | RENEWABLE ENERGY , 2023 , 206 , 645-657 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于自适应模型粒子滤波算法的滚动轴承剩余使用寿命预测方法，该方法基于滚动轴承性能退化的演变规律，将退化过程划分为健康、退化和失效三个阶段。引入Box‑Cox变换及3σ原则，准确地确定了轴承开始退化的时刻及失效阈值，实现了健康状态的自主识别；针对单一预测模型难以准确跟踪轴承退化状态的难点，提出自适应模型匹配策略选择最优滤波模型的方法，实现了退化状态的动态追踪；创新性地提出了基于已有数据的全局/局部信息融合方法预测轴承寿命，避免了单次预测的偶然性，从而获得了剩余使用寿命概率密度函数的最佳估计。
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 崔玲丽 , 李文杰 , 王华庆 et al. 一种基于自适应模型粒子滤波算法的轴承寿命预测方法 : CN202211067564.8[P]. | 2022-09-01 . |
| MLA | 崔玲丽 et al. "一种基于自适应模型粒子滤波算法的轴承寿命预测方法" : CN202211067564.8. | 2022-09-01 . |
| APA | 崔玲丽 , 李文杰 , 王华庆 , 乔文生 . 一种基于自适应模型粒子滤波算法的轴承寿命预测方法 : CN202211067564.8. | 2022-09-01 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于数字孪生字典的自适应稀疏图学习轴承寿命预测方法，该方法建立了扩展指数模型及线性分段模型，生成涵盖多种退化行为的数字孪生字典，设计了新的图学习优化目标函数，引入稀疏正则化方法降低模型复杂度及参数敏感度，自适应获取数据的精确拓扑结构，基于构建的数字孪生字典及自适应稀疏图学习实现了剩余使用寿命的准确预测。
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 崔玲丽 , 王鑫 . 一种基于数字孪生字典的自适应稀疏图学习轴承寿命预测方法 : CN202210174135.4[P]. | 2022-02-24 . |
| MLA | 崔玲丽 et al. "一种基于数字孪生字典的自适应稀疏图学习轴承寿命预测方法" : CN202210174135.4. | 2022-02-24 . |
| APA | 崔玲丽 , 王鑫 . 一种基于数字孪生字典的自适应稀疏图学习轴承寿命预测方法 : CN202210174135.4. | 2022-02-24 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于时变卡尔曼滤波的滚动轴承剩余使用寿命预测方法，该方法可自动匹配滚动轴承不同退化阶段特点，分别建立基于一次线性函数和二次非线性函数的时变卡尔曼滤波器模型，以时移窗滤波相对误差指标因子自适应的判断轴承退化状态，自动切换卡尔曼滤波器处理不同阶段的监测数据，实现轴承剩余使用寿命的有效预测。
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 崔玲丽 , 王鑫 , 王华庆 et al. 一种基于时变卡尔曼滤波的滚动轴承剩余使用寿命预测方法 : CN202210173117.4[P]. | 2022-02-24 . |
| MLA | 崔玲丽 et al. "一种基于时变卡尔曼滤波的滚动轴承剩余使用寿命预测方法" : CN202210173117.4. | 2022-02-24 . |
| APA | 崔玲丽 , 王鑫 , 王华庆 , 乔文生 . 一种基于时变卡尔曼滤波的滚动轴承剩余使用寿命预测方法 : CN202210173117.4. | 2022-02-24 . |
| 导入链接 | NoteExpress RIS BibTex |
导出
| 数据： |
选中 到 |
| 格式： |
