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学者姓名：崔玲丽
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Abstract ：
The selection of optimal frequency band sensitive to fault is significant for bearing fault diagnosis. However, prior knowledge of fault characteristic frequency is usually essential in this operation. To address this issue, an optimal candidate fault frequency periodicity index optimization-gram is proposed. First, the spectral coherence theory is exploited to transform the vibration signal into a two-dimensional map consisting of cyclic and spectral frequencies. Second, a novel optimal candidate fault frequency periodicity index is constructed based on optimal candidate fault frequencies, which fully excavates the fault information hidden in a two-dimensional plane by utilizing modulation characteristics of bearing fault signal and transforms it into a specific numerical series. Then, the optimal candidate fault frequency periodicity index optimization-gram is further developed to identify the optimal frequency band, where the optimal candidate fault frequency periodicity index is utilized to quantify the fault information in the frequency bands separated by 1/3-binary tree filter bank. Finally, an improved envelope spectrum is obtained by integrating the spectral coherence over the optimal frequency band. The optimal candidate fault frequency periodicity index optimization-gram is demonstrated by simulated and experimental signals, and the results demonstrate that it is superior to other methods.
Keyword ：
improved envelope spectrum improved envelope spectrum rolling bearing rolling bearing spectral coherence spectral coherence Spectral correlation Spectral correlation optimal frequency band optimal frequency band
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| GB/T 7714 | Zhao, Xinyuan , Liu, Dongdong , Cui, Lingli . An optimal candidate fault frequency periodicity index optimization-gram for bearing fault diagnosis [J]. | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL , 2025 . |
| MLA | Zhao, Xinyuan 等. "An optimal candidate fault frequency periodicity index optimization-gram for bearing fault diagnosis" . | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2025) . |
| APA | Zhao, Xinyuan , Liu, Dongdong , Cui, Lingli . An optimal candidate fault frequency periodicity index optimization-gram for bearing fault diagnosis . | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL , 2025 . |
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Abstract ：
The graph neural network (GNN) has emerged as an effective way to mine relationships between data due to its powerful modeling capability for graph structure data, and it has garnered significant attention from researchers for intelligent fault diagnosis tasks. However, the adjacency matrix of most GNN models with deep architecture is always fixed during the aggregation process, and the edge connection relationship cannot be adaptively adjusted, which limits their performance for feature representation. Moreover, for few-shot diagnosis scenarios, the generalization performance of deep GNN models will be further degraded due to fixed receptive fields and limited training samples. To address these issues, a deep adaptively dynamic edge graph convolution network (DADE-GCN) with attention weight and a high-dimension affinity feature graph is proposed. First, a deep adaptively dynamic edge graph convolutional module with attention weight (DADE-GCNWAW) is developed to dynamically adjust the receptive field in different graph convolution layers. Subsequently, the output features of different layers are fused by a self-attention mechanism. Second, to overcome the effect of the time-shift problem existing in vibration signals and capture accurate interdependencies between data, a high-dimension affinity feature graph construction method is proposed to construct graph structure data. The effectiveness of the proposed method is quantitatively verified by two rotating machinery datasets, which indicate that the proposed DADE-GCN model can achieve average diagnosis accuracies of 98.80% in both the two few-shot diagnosis tasks, which outperform several state-of-the-art recognition methods.
Keyword ：
few-shot fault diagnosis few-shot fault diagnosis rotating machinery rotating machinery high-dimension affinity feature graph high-dimension affinity feature graph deep adaptively dynamic edge graph convolution module deep adaptively dynamic edge graph convolution module
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| GB/T 7714 | Jiang, Zhichao , Liu, Dongdong , Cui, Lingli . Deep adaptively dynamic edge graph convolution network with attention weight and high-dimension affinity feature graph for rotating machinery fault diagnosis [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (2) . |
| MLA | Jiang, Zhichao 等. "Deep adaptively dynamic edge graph convolution network with attention weight and high-dimension affinity feature graph for rotating machinery fault diagnosis" . | MEASUREMENT SCIENCE AND TECHNOLOGY 36 . 2 (2025) . |
| APA | Jiang, Zhichao , Liu, Dongdong , Cui, Lingli . Deep adaptively dynamic edge graph convolution network with attention weight and high-dimension affinity feature graph for rotating machinery fault diagnosis . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (2) . |
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Abstract ：
Graph neural network (GNN) is an effective tool for semi-supervised fault diagnosis of rotating machinery. However, existing GNN based-semi-supervised methods only rely on single graph structure to learn feature representation under limited labeled samples, while the information of different topology graph structures cannot be directly fused due to the large difference of feature extracting, leading to insufficient node relationships and label information mining. Besides, static or limited dynamic feature extraction of neighbor nodes will hinder the expressiveness of semi-supervised GNN models. To overcome these limitations, a dual graph drivenconsistent representation learning method (DGDCRL) is proposed in this paper. First, a dual graph structure with two different topology graphs is conducted using graph label passing method, in which limited labeled sample information are fully leveraged and richer topology structure information among nodes can be captured. Second, a consistent representation learning method with gated-dynamic enhanced graph attention module (GDEGAT) is proposed to extract the common embeddings from two topology graphs, where a DEGAT layer is developed to aggregate neighbor information more dynamically and expressively. Besides, to enhance the alignment between the embeddings of the same nodes across two topology graphs, we design a consistent representation loss. Two datasets are used to validate the performance of the proposed method, indicating that the proposed DGDCRL method with GDEGAT module can achieve the effective diagnosis results of rotating machinery under both constant and variable speed conditions, and the DGDCRL method can effectively enhance the semi-supervised diagnostic ability of baseline GNNs under low labeled rates.
Keyword ：
Consistent representation learning method Consistent representation learning method Semi-supervised fault diagnosis Semi-supervised fault diagnosis Gated-dynamic enhanced graph attention module Gated-dynamic enhanced graph attention module Dual graph construction Dual graph construction Rotating machinery Rotating machinery
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| GB/T 7714 | Jiang, Zhichao , Liu, Dongdong , Wang, Huaqing et al. Dual graph driven-consistent representation learning method for semi-supervised fault diagnosis of rotating machinery [J]. | ADVANCED ENGINEERING INFORMATICS , 2025 , 65 . |
| MLA | Jiang, Zhichao et al. "Dual graph driven-consistent representation learning method for semi-supervised fault diagnosis of rotating machinery" . | ADVANCED ENGINEERING INFORMATICS 65 (2025) . |
| APA | Jiang, Zhichao , Liu, Dongdong , Wang, Huaqing , Cui, Lingli . Dual graph driven-consistent representation learning method for semi-supervised fault diagnosis of rotating machinery . | ADVANCED ENGINEERING INFORMATICS , 2025 , 65 . |
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Abstract ：
In the actual operation, rotating machinery mostly works under normal condition. The collected monitoring data often exhibit serious distribution imbalance with far more normal label samples than fault label samples, leading to poor recognition performance of standard intelligent diagnosis models. Besides, many intelligent diagnosis models rely on data generation to overcome this problem, which is subject to data generation differences. Therefore, to address above limitations, a novel temporal-spatial multi-order weighted graph convolution network (TSMOW-GCN) with refined feature topology graph is proposed. First, a multi-order weight graph convolution layer is proposed to aggregate multi-order weighted mixing neighbor information in different distances, which achieves broader representation and mines more features and relationships without data generation and deep network structure. Further, the feature modeling in temporal dimensions is considered. Second, a refined feature topology graph construction method is developed to obtain compact and efficient feature topology graphs, which can improve the ability of graph representation. Besides, a dynamically adjusted label smoothing regularization loss is proposed to further improve generalization ability and avoid overfitting of the trained model under imbalance data. Two rotating machinery datasets are used to quantitatively verify proposed method, indicating that the TSMOW-GCN outperforms several advanced approaches under various imbalance ratios.
Keyword ：
Refined feature topology graph Refined feature topology graph Multi-order weighted graph convolution layer Multi-order weighted graph convolution layer Rotating machinery Rotating machinery regularization regularization Dynamically adjusted label smoothing Dynamically adjusted label smoothing Imbalance fault diagnosis Imbalance fault diagnosis
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| GB/T 7714 | Jiang, Zhichao , Liu, Dongdong , Cui, Lingli . A temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery [J]. | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2025 , 257 . |
| MLA | Jiang, Zhichao et al. "A temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery" . | RELIABILITY ENGINEERING & SYSTEM SAFETY 257 (2025) . |
| APA | Jiang, Zhichao , Liu, Dongdong , Cui, Lingli . A temporal-spatial multi-order weighted graph convolution network with refined feature topology graph for imbalance fault diagnosis of rotating machinery . | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2025 , 257 . |
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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 ：
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 ：
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 ：
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。
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| 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 . |
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Abstract ：
本发明公开了一种基于自适应奇异值分解的轴承微弱故障特征提取方法，首先针对正弦信号、复合正弦信号和周期性冲击信号各自SV的演变趋势，结合奇异值子对SVP的形成原理，分别提出最佳嵌入维数优化选取原则，明确了该参数的量化范围，进而根据信号自身特点，确定奇异值分解(SVD)的最佳嵌入维数。该方法可自适应匹配SVD的最佳嵌入维数，进而获得形成SVP分布的信号分解策略。随后，结合谐波干扰的能量及SVP分布，实现对包含轴承微弱故障成分的子信号进行定位。最后，采用反对角线平均法重构目标子信号，对其进行包络谱分析获得诊断结果。新方法能自适应匹配SVD的最佳嵌入维数，能有效实现滚动轴承微弱故障特征提取。
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| GB/T 7714 | 崔玲丽 , 刘银行 , 王鑫 . 一种基于自适应奇异值分解的滚动轴承微弱故障特征提取方法 : CN202210052833.7[P]. | 2022-01-18 . |
| MLA | 崔玲丽 et al. "一种基于自适应奇异值分解的滚动轴承微弱故障特征提取方法" : CN202210052833.7. | 2022-01-18 . |
| APA | 崔玲丽 , 刘银行 , 王鑫 . 一种基于自适应奇异值分解的滚动轴承微弱故障特征提取方法 : CN202210052833.7. | 2022-01-18 . |
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