Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

The input pattern problem on deep learning applied to signal analysis and processing to achieve fault diagnosis

This is a preview of subscription content, log in to check access.

References

  1. 1

    Chen K, Hu J, He J. A framework for automatically extracting over-voltage features based on sparse autoencoder. IEEE Trans Smart Grid, 2016, 9: 594–604

  2. 2

    Ren H, Chai Y, Qu J F, et al. A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system. Neurocomputing, 2018, 275: 2111–2125

  3. 3

    Zhang Q, Yang L T, Chen Z. Deep Computation Model for Unsupervised Feature Learning on Big Data. IEEE Trans Serv Comput, 2016, 9: 161–171

  4. 4

    Ren H, Chai Y, Qu J F, et al. Deep learning for fault diagnosis: the state of the art and challenge. Control Decis, 2017, 32: 1345–1358

  5. 5

    Xie Z W, Zeng Z, Zhou G Y, et al. Topic enhanced deep structured semantic models for knowledge base question answering. Sci China Inf Sci, 2017, 60: 110103

  6. 6

    Qu W, Wang D L, Feng S, et al. A novel cross-modal hashing algorithm based on multimodal deep learning. Sci China Inf Sci, 2017, 60: 092104

  7. 7

    Xu Z B, Sun J. Model-driven deep-learning. Natl Sci Rev, 2018, 5: 22–24

  8. 8

    Guo L H, Guo C G, Li L, et al. Two-stage local constrained sparse coding for fine-grained visual categorization. Sci China Inf Sci, 2018, 61: 018104

  9. 9

    Jiang P, Hu Z, Liu J, et al. Fault diagnosis based on chemical sensor data with an active deep neural network. Sensors, 2016, 16: 1695

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61633005, 61673076, 61773080), Natural Science Foundation of Chongqing, China (Grant No. cstc2016jcyjA0504), Fundamental Research Funds for the Central Universities (Grant Nos. 106112016CDJXZ238826, 2018CDYJSY0055), and Natural Science Research Project of the Higher Education Institutions of Jiangsu Province (Grant No. 18KJB510006).

Author information

Correspondence to Nan Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ren, H., Li, N., Chai, Y. et al. The input pattern problem on deep learning applied to signal analysis and processing to achieve fault diagnosis. Sci. China Inf. Sci. 62, 229202 (2019). https://doi.org/10.1007/s11432-018-9564-6

Download citation