A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case

Li, Xin; Cheng, Jian; Shao, Haidong; Liu, Kan; Cai, Baoping

doi:10.1109/tii.2021.3125385

Cited by 91 publications

(29 citation statements)

References 25 publications

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“…Accuracy is a direct and simple method to provide a quantitative comparison, roughly. Imbalanced data occur now and then [21], and accuracy is not a suitable metric in this situation. Unfortunately, network traffic dataset, in most cases, is imbalanced dataset with much more benign instances than malicious ones.…”

Section: Results Evaluation Methodsmentioning

confidence: 99%

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

Sun

Geng

Zhang

et al. 2022

J Wireless Com Network

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The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving to the 6G networks, which leads to dramatically raised security issues. Using machine learning, including deep learning, to find out malicious network traffic is one of practical ways. Though much work has been done in this direction, we found little investigating the effect of using fused network conversation datasets to train and test models. Thus, this work proposes to check conversation dataset characteristics and find suitable ones to fuse into one dataset in order to improve the capability of malicious traffic and malware detection performance. The experiments using real data show that conditioned combination of datasets can be used to enhance algorithm performance and improve detection results. For this reason, it is recommended to profile datasets and conduct conditional fusion of network conversation datasets before using machine learning or deep learning. As the characterization is done using general statistical calculation, it is promising to be used for other domains too.

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Section: Results Evaluation Methodsmentioning

confidence: 99%

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

Sun

Geng

Zhang

et al. 2022

J Wireless Com Network

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show abstract

“…However, the success of deep learning largely depends on the sufficiency of training samples. Unfortunately, with the increasing improvement of reliability and quality, the machinery works under healthy states most of time in the real industrial scenarios, which means that acquiring sufficient fault samples is difficult [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

et al. 2023

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“…However, these data-driven methods need sufficiently labeled training samples to learn the model [20,21], which is difficult for TCM in the machining process due to the high cost of a lot of experiments [22]. The performance of data-driven methods could be poor with few labeled training samples [23,24]. To solve this problem, transfer learning (TL) has been developed with a small labeled sample in the target domain [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

et al. 2022

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Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

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A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case

Cited by 91 publications

References 25 publications

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

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