Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Author:

Liu Xiaoping,Xia Lijian,Shi Jian,Zhang Lijie,Wang Shaoping

Funder

National Natural Science Foundation of China

Postdoctoral Research Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Microbiology (medical),Immunology,Immunology and Allergy

Reference45 articles.

1. Sun Y, Li S, Wang X (2021) Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image[J]. Measurement 176:109100

2. Sun Y, Li S (2022) Bearing fault diagnosis based on optimal convolution neural network[J]. Measurement 190:110702

3. Zheng Y, Chen Q, Zhang Y (2014) Deep learning and its new progress in object and behavior recognition[J]. J Image Grap 19(2):175–184

4. Zhao G, Ge Q, Liu X et al (2016) Fault feature extraction and diagnosis method based on deep belief network[J]. Chinese J Scient Inst 37(9):1946–1953

5. Lei Y, Jia F, Lin J et al (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Trans Industr Electron 63(5):3137–3147

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