Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

Di, Pei; Yue, Jianhai; Jiao, Jing

doi:10.1088/1757-899x/241/1/012035

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In terms of the multi-sensor data fusion, a multi-sensor data fusion classification method based on the Linear Discriminant Analysis (LDA) is presented [28]. The Dempster-Shafer (D-S) evidence theory is used to fuse the multi-sensor data to improve the diagnostic accuracy [29].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

Nan¹,

Wang²,

Ding³

2023

J. vibroeng.

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A fault diagnosis method for the rotating machinery based on improved Convolutional Neural Network (CNN) with Gray-Level Transformation (GLT) is proposed to increase the accuracy of the recognition adopting the multiple sensors. The Symmetrized Dot Pattern (SDP) in this method is applied to fuse the data of the multiple sensors, and the multi-color value method is adopted to increase the feature dimension. The grayscale and GLT are used to reduce the dimension of the SDP image. The SDP grayscale image is finally input to the CNN network for training recognition. The research results show that the diagnosis accuracy of the rolling bearing system based on the novel method is up to 98.6 %. Compared with the method without the multi-color value and GLT, the recognition accuracy of the proposed method is improved by 22.3 %, and the training time is reduced by about one third. The research work reveals that the developed method has the potential application value under the multi-sensor working conditions for the fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

Nan¹,

Wang²,

Ding³

2023

J. vibroeng.

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show abstract

“…As a way to increase target trust evaluation, many scholars have used the DS (Dempster–Shafer) evidence theory algorithm to obtain better diagnostic performance. Pei [ 17 ] et al proposed a rolling bearing fault diagnostic method based on DS evidence theory fusion. DS evidence theory is used to fuse multi-sensor data.…”

Section: Introductionmentioning

confidence: 99%

Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration

Tang

Chen

2022

Entropy

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Recent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud–edge collaborative bearing fault diagnostic method based on the tradeoff between the advantages and disadvantages of cloud and edge computing. First, a collaborative cloud-based framework and an improved DSCNN–GAP algorithm are suggested to build a general model using the public bearing fault dataset. Second, the general model is distributed to each edge node, and a limited number of unique fault samples acquired by each edge node are used to quickly adjust the parameters of the model before running diagnostic tests. Finally, a fusion result is made from the diagnostic results of each edge node by DS evidence theory. Experiment results show that the proposed method not only improves diagnostic accuracy by DSCNN–GAP and fusion of multi-sensors, but also decreases diagnosis time by migration learning with the cloud–edge collaborative framework. Additionally, the method can effectively enhance data security and privacy protection.

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Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

Cited by 2 publications

References 5 publications

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration

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