Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis

Zhou, Kaibo; Yang, Chaoying; Liu, Jie; Xu, Qi

doi:10.1109/tie.2021.3121748

Cited by 65 publications

(18 citation statements)

References 40 publications

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“…The existing works for driver activity basically treat it as a classification problem, then it can be tackled by the efficient deep learning approach [34][35][36]. A commonly used input is an in-cabin image, and many convolutional neural network (CNN)-based approaches have been proposed from different perspectives [11,13,37].…”

Section: Related Workmentioning

confidence: 99%

Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Xing

Gu³

et al. 2023

IEEE Trans. Intell. Veh.

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Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of dataset and provide limited recognition capability for unknown activities.To overcome these challenges, this paper proposes a contrastive learning approach with the aim of building a model that can quantify driver anomalies with a continuous variable. In addition, a novel clustering supervised contrastive loss is proposed to optimize the distribution of the extracted representation vectors to improve the model performance. Compared with the typical contrastive loss, the proposed loss can better cluster normal representations while separating abnormal ones. The abnormality of driver activity can be quantified by calculating the distance to a set of representations of normal activities rather than being produced as the direct output of the model. The experiment results with datasets under different modes demonstrate that the proposed approach is more accurate and robust than existing ones in terms of recognition and quantification of unknown abnormal activities.

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Section: Related Workmentioning

confidence: 99%

Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Xing

Gu³

et al. 2023

IEEE Trans. Intell. Veh.

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“…Digital Twin (DT), proposed by Grieves in 2003 [1] and constantly improved since by several researchers [2][3][4], featured among Gartner's top ten most promising technological trends for 2018 [5]. Moreover, it is considerably popular as a multiphysics, multiscale, ultrafidelity simulation that reflects the state of a corresponding twin in real-time based on historical data, real-time sensor data, and physical models, which provides remarkable opportunities for many industrial applications [4,[6][7][8][9][10]. The important milestones in the development of DT are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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“…Due to sensor failure, electromagnetic interference, and missing communication packets, the data quality of the actual measured signal is usually low, which is mainly manifested as data anomaly and data loss [5,6]. Most signal denoising methods, including spectrum analysis and signal reconstruction, are effective for signals with a high sampling rate and consistent sampling frequency [7][8][9]. However, data loss has a great influence on their practical application.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

Self Cite

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The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

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Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis

Cited by 65 publications

References 40 publications

Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

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