Abstract
Mechanical fault diagnosis is an essential means to reduce maintenance cost and ensure safety in production. Aiming to improve diagnosis accuracy, this paper proposes a novel data-driven diagnosis method based on deep learning. Nonstationary signals are preprocessed. A feature learning method based on deep learning model is designed to mine features automatically. The mined features are identified by a supervised classification method – support vector machine (SVM). Thanks to mining features automatically, the proposed method can overcome the weakness that manual feature extraction depends on much expertise and prior knowledge in traditional data-driven diagnosis method. The effectiveness of the proposed method is validated on two datasets. Experimental results demonstrate that the proposed method is superior to the traditional data-driven diagnosis methods.
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Acknowledgments
This research is supported by the National Natural Science Foundation of China NO. 51435009, NO. 51375004 and NO. 51421062.
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Zhang, Y., Gao, L., Li, X., Li, P. (2017). A Novel Data-Driven Fault Diagnosis Method Based on Deep Learning. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_44
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DOI: https://doi.org/10.1007/978-3-319-61845-6_44
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