A novel multi-source sensing data fusion driven method for detecting rolling mill health states under imbalanced and limited datasets

Shi, Peiming; Yu, Yue; Gao, Hao; Chen, Hua

doi:10.1016/j.ymssp.2022.108903

Cited by 33 publications

(12 citation statements)

References 25 publications

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“…In big data teaching, teachers can use relevant software to build the teaching environment and make full use of the big data function. Teachers categorize the resources that students will use in learning, compile guiding outlines, guide students to establish their own learning garden, and build a ubiquitous learning platform [21][22][23][24][25]. Under the guidance of teachers, students use massive resources to learn actively and use big data to carry out discussions and exchanges to promote their own progress.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Pan

2022

Discrete Dynamics in Nature and Society

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With the rapid development of educational information technology, “online and offline” hybrid teaching has become the main trend of foreign language teaching reform in colleges and universities. The blended teaching mode integrates the traditional teaching mode with modern educational technology and reconstructs the traditional college English teaching mode in the aspects of teaching object, teaching content, teaching method, teaching environment, and teaching evaluation. Based on the multisource data such as students’ learning situation, learning duration, and academic performance, this paper builds a student English learning analysis system that realizes the integration of multisource data, completes the unified processing and analysis of multisource data, and then displays it through the interface. The system helps English teachers to guide students in different degrees according to the students’ English learning situation and academic performance, so that English teachers have a clearer and more comprehensive understanding of students’ learning and living conditions and timely guide students with incorrect learning attitudes, so as to avoid students’ detours.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Pan

2022

Discrete Dynamics in Nature and Society

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“…For example, Yu et al designed a multi-source information based on enhanced DBNs to achieve health states of the rolling mill [18] . Shi et al proposed a heterogeneous information fusion framework to diagnose mechanical faulty types [19] . Based on the above observations, it can be observed that the reason behind the significant success of DL-based methods lie in its powerful feature extraction and non-linear fitting capabilities.…”

Section: Introductionmentioning

confidence: 99%

Weighted multi-source information fusion network-based framework for mechanical fault diagnosis

Song,

Zhang,

2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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To alleviate the loss of potentially valuable and discriminative features as the number of networks increases in deep learning (DL). A weighted multi-source information fusion network-based framework is proposed for mechanical fault diagnosis. Firstly, a feature improved Deep Belief Network (FIDBNs) and a feature improved Convolutional Neural Network (FICNNNs) are designed to repetitively extract valuable information from original data. Meanwhile, an attention mechanism is adopted to further extract important fault characteristics Then, a weighted feature fusion module is used to integrate better for more diagnostic performance. Finally, an experimental dataset based on bearing failures is used to prove the effectiveness and superiority of the proposed framework compared with other methods.

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“…The data-driven fault diagnosis method uses historical data to obtain implicit features that represent the system variables by statistical analysis or artificial intelligence methods and has been widely used in industrial systems and aerospace vehicles. A variety of data-driven methods, especially machine learning methods,such as CNN [8], autoencoder [9], LSTM [10], GAN [11], and DNN [12] etc, are used for fault diagnosis. Some review articles have systematically demonstrated the history, existing methods, and development direction of intelligent fault diagnosis based on data-driven methods [13,14].…”

Section: Introductionmentioning

confidence: 99%

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Song,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic Mode Decomposition with control (DMDc) is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k) is input into the k-nearest neighbor (kNN) classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and V k (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k, and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

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A novel multi-source sensing data fusion driven method for detecting rolling mill health states under imbalanced and limited datasets

Cited by 33 publications

References 25 publications

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

[Retracted] Construction and Application of College English Blended Teaching System Based on Multidata Fusion

Weighted multi-source information fusion network-based framework for mechanical fault diagnosis

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

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