SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis

Yang, Chaoying; Zhou, Kaibo; Liu, Jie

doi:10.1109/tie.2021.3075871

Cited by 103 publications

(36 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With respect to the collected data, typical statistical features can be used for balance control ability evaluation, such as mean, root mean square, and so forth [ 5 – 7 ]. In the past years, many signal processing methods have been proposed for better feature extraction [ 8 – 10 ], including wavelet analysis, stochastic resonance techniques, and so on.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Athlete balance control ability plays an important role in different types of sports. Accurate and efficient evaluations of the balance control abilities can significantly improve the athlete management performance. With the rapid development of the athlete training field, intelligent and automatic evaluations have been highly demanded in the past years. This study proposes a deep learning-based athlete balance control ability evaluation method through processing the time-series movement pressure measurement data. An end-to-end model structure is proposed, which directly analyzes the raw data and provides the evaluation results, which largely facilitates practical utilization. A multi-scale feature extraction scheme is employed, by exploring the learned features in different scales. A residual connected neural network architecture is further proposed. By using the short-cut connection, the deep neural network model can be more efficiently trained. Experiments on the real athlete balance control ability tests are carried out for validations. Through comparisons with different related methods, the results show the proposed deep multi-scale residual connected neural network model is well suited for the athlete balance control ability evaluation problem, and promising for actual applications in the real scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…To handle the unbalanced dataset problem for fault diagnosis, Liu et al (2021) use an autoencoder-based SuperGraph feature learning method. However, the constructed Super-Graph by Yang et al (2022) has redundant edges; all labeled signals with the same fault type are interconnected, resulting in excessive computational costs. Li et al (2021b), on the other hand, used a multireceptive field graph convolutional network (MRF-GCN) to solve the constraints of GCNs for effective intelligent fault diagnosis.…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

et al. 2022

View full text Add to dashboard Cite

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.

show abstract

SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis

Cited by 103 publications

References 30 publications

Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation

Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation

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

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Contact Info

Product

Resources

About