Skip to main content
Log in

A zero-shot learning method for fault diagnosis under unknown working loads

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Data-based fault diagnosis is an important technology in modern manufacturing systems. However, most of these diagnosis methods assume that all the data should be identically distributed. In diagnosis tasks, this assumption means that these methods can only handle faults from the same working load. In real-world applications, the working load of the equipment varies for the different productions; if an unknown working load with no prior data available is given, then these traditional methods may be invalid. Zero-shot learning, using known data to diagnose the fault under unknown working loads, provides a transfer approach to solve this problem. In this paper, a zero-shot learning method based on contractive stacked autoencoders is proposed. The proposed method is only trained by the data from the known working load and can diagnose the fault from unknown but related working loads without prior data. The experimental results on the Case Western Reserve University dataset indicate that the proposed method performs better than the traditional methods under unknown working loads and has an accuracy of 97.82%. In addition, the analysis of the singular value and feature space also suggests that the proposed method is more robust and the feature representation is more contractive.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of China (NSFC) under Grants 51825502, 51775216 and 51711530038, the Natural Science Foundation of Hubei Province under Grant 2018CFA078, and the Program for HUST Academic Frontier Youth Team 2017QYTD04.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xinyu Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Y., Gao, L., Li, X. et al. A zero-shot learning method for fault diagnosis under unknown working loads. J Intell Manuf 31, 899–909 (2020). https://doi.org/10.1007/s10845-019-01485-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01485-w

Keywords

Navigation