IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3677-d1225668.html
   My bibliography  Save this article

SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection

Author

Listed:
  • Jianmin Zhou

    (Key Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, China
    State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee, Nanchang 330013, China)

  • Lulu Liu

    (Key Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, China
    State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee, Nanchang 330013, China)

  • Xiwen Shen

    (Key Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, China
    State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee, Nanchang 330013, China)

Abstract

In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption.

Suggested Citation

  • Jianmin Zhou & Lulu Liu & Xiwen Shen, 2023. "SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3677-:d:1225668
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3677/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/17/3677/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    2. Yongbo Li & Xianzhi Wang & Shubin Si & Xiaoqiang Du, 2019. "A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

    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. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    2. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    3. Naveen Venkatesh Sridharan & Jerome Vasanth Joseph & Sugumaran Vaithiyanathan & Mohammadreza Aghaei, 2023. "Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules," Energies, MDPI, vol. 16(15), pages 1-17, August.

    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:11:y:2023:i:17:p:3677-:d:1225668. 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.