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Methods for Improving the Fault Diagnosis Accuracy of Rotating Machines

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

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Abstract

The paper considers the use of fully connected networks for classifying the states of a rotary machine based on a vibration signal. An experimental stand is proposed. We worked with three different states of the experimental setup. The new approach is to use generative adversarial networks to create artificial data and various architectures of fully connected neural networks. We also tested different combinations of training and validation datasets. As a result, the use of all these methods makes it possible to improve the accuracy of the network by about 6.5%.

The work has been carried out at the Oryol State University named after I.S. Turgenev with the financial support of the Ministry of Science and Higher Education of the Russian Federation within the project “Creation of a digital system for monitoring, diagnosing and predicting the state of technical equipment using artificial intelligence technology based on domestic hardware and software”, Agreement No. 075-11-2021-043 from 25.06.2021.

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Acknowledgment

The work has been carried out at the Oryol State University named after I.S. Turgenev with the financial support of the Ministry of Science and Higher Education of the Russian Federation within the project “Creation of a digital system for monitoring, diagnosing and predicting the state of technical equipment using artificial intelligence technology based on domestic hardware and software”, Agreement No. 075-11-2021-043 from 25.06.2021.

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Authors and Affiliations

  1. Department of Mechatronics, Mechanics, and Robotics, Orel State University, Komsomolskaya Street, 95, Orel, 302026, Russian Federation

    Yuri Kazakov, Ivan Stebakov, Alexander Fetisov, Alexey Kornaev & Roman Polyakov

  2. Lab of Artificial Intelligence, Innopolis University, Universitetskaya Street, 1, Innopolis, 420500, Russian Federation

    Alexey Kornaev

Authors
  1. Yuri Kazakov
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  2. Ivan Stebakov
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  3. Alexander Fetisov
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  4. Alexey Kornaev
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  5. Roman Polyakov
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Contributions

Yu. Kazakov and A. Fetisov developed the test rig and collected data for training neural networks. A. Kornaev proposed the idea of using GANs to generate artificial data. Kazakov recreated and trained the GANs. Stebakov and Kazakov conducted computational experiments on training fully connected neural networks with different parameters. R. Polyakov was in charge of supervising this work.

Corresponding author

Correspondence to Yuri Kazakov .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Kazakov, Y., Stebakov, I., Fetisov, A., Kornaev, A., Polyakov, R. (2023). Methods for Improving the Fault Diagnosis Accuracy of Rotating Machines. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_12

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