IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4683-d999308.html
   My bibliography  Save this article

Deep Transfer Learning Framework for Bearing Fault Detection in Motors

Author

Listed:
  • Prashant Kumar

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Prince Kumar

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India)

  • Ananda Shankar Hati

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India)

  • Heung Soo Kim

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.

Suggested Citation

  • Prashant Kumar & Prince Kumar & Ananda Shankar Hati & Heung Soo Kim, 2022. "Deep Transfer Learning Framework for Bearing Fault Detection in Motors," Mathematics, MDPI, vol. 10(24), pages 1-14, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4683-:d:999308
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4683/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Levent Eren, 2017. "Bearing Fault Detection by One-Dimensional Convolutional Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, July.
    2. Lefa Zhao & Yafei Zhu & Tianyu Zhao, 2022. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Prashant Kumar & Prince & Ashish Kumar Sinha & Heung Soo Kim, 2024. "Electric Vehicle Motor Fault Detection with Improved Recurrent 1D Convolutional Neural Network," Mathematics, MDPI, vol. 12(19), pages 1-17, September.
    2. Changchun Mo & Huizi Han & Mei Liu & Qinghua Zhang & Tao Yang & Fei Zhang, 2023. "Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method," Mathematics, MDPI, vol. 11(13), pages 1-28, June.
    3. Manlin Chen & Zhijie Zhou & Xiaoxia Han & Zhichao Feng, 2023. "A Text-Oriented Fault Diagnosis Method for Electromechanical Device Based on Belief Rule Base," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    4. Izaz Raouf & Prashant Kumar & Hyewon Lee & Heung Soo Kim, 2023. "Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    5. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    6. Mohammed H. Qais & Seema Kewat & Ka Hong Loo & Cheung-Ming Lai & Aldous Leung, 2023. "LSTM-Based Stacked Autoencoders for Early Anomaly Detection in Induction Heating Systems," Mathematics, MDPI, vol. 11(15), pages 1-19, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Amir Khan & Bilal Asad & Karolina Kudelina & Toomas Vaimann & Ants Kallaste, 2022. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art," Energies, MDPI, vol. 16(1), pages 1-54, December.
    2. Shi, Jiayu & Zhong, Jingshu & Zhang, Yuxuan & Xiao, Bin & Xiao, Lei & Zheng, Yu, 2024. "A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Rahul R. Kumar & Mauro Andriollo & Giansalvo Cirrincione & Maurizio Cirrincione & Andrea Tortella, 2022. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors," Energies, MDPI, vol. 15(23), pages 1-36, November.
    4. Shiza Mushtaq & M. M. Manjurul Islam & Muhammad Sohaib, 2021. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review," Energies, MDPI, vol. 14(16), pages 1-24, August.
    5. Xiang Li & Shuo Zhang & Wei Zhang, 2023. "Applied Computing and Artificial Intelligence," Mathematics, MDPI, vol. 11(10), pages 1-4, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4683-:d:999308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.