IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i4d10.1007_s10845-021-01907-8.html
   My bibliography  Save this article

Recognition of abnormal patterns in industrial processes with variable window size via convolutional neural networks and AdaBoost

Author

Listed:
  • Ahmed Maged

    (City University of Hong Kong
    Benha Faculty of Engineering, Benha University)

  • Min Xie

    (City University of Hong Kong
    City University of Hong Kong)

Abstract

In industrial settings, it is inevitable to encounter abnormal patterns monitoring a process. These patterns point out manufacturing faults that can lead to significant internal and external failure costs unless treated promptly. Thus, detecting such abnormalities is of utmost importance. Machine learning algorithms have been widely applied to this problem. Nevertheless, the existing control chart pattern recognition (CCPR) method can only deal with a fixed input size rather than dealing with different input sizes according to the actual production needs. In order to tackle this problem, an original CCPR method relying on convolutional neural network (CNN) named as VIS-CNN is proposed. Signal resizing is performed using resampling methods, then CNN is used to extract the abnormal patterns in the dataset. Five different input sizes are generated for model training and testing. The optimal hyperparameters, as well as the best structure of the used CNN are obtained using Bayesian Optimization. Simulation results show that the correct recognition rate of the VIS-CNN is 99.78%, based on different window size control charts. Furthermore, we address the issue of the mixed CCP and provide a modified scheme to achieve high recognition ratio for 8 mixed patterns on top of 6 standard patterns. The modified scheme includes wavelet noise reduction and Adaptive Boosting. A case study on metal galvanization process is presented to show that the method has potential applications in the industrial environment.

Suggested Citation

  • Ahmed Maged & Min Xie, 2023. "Recognition of abnormal patterns in industrial processes with variable window size via convolutional neural networks and AdaBoost," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1941-1963, April.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01907-8
    DOI: 10.1007/s10845-021-01907-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01907-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-021-01907-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ling-Jing Kao & Tian-Shyug Lee & Chi-Jie Lu, 2016. "A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 653-664, June.
    2. Xueliang Zhou & Pingyu Jiang & Xianxiang Wang, 2018. "Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 51-67, January.
    3. Min Zhang & Wenming Cheng, 2015. "Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, October.
    4. Tao Zan & Zhihao Liu & Hui Wang & Min Wang & Xiangsheng Gao, 2020. "Control chart pattern recognition using the convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 703-716, March.
    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. Tao Zan & Zhihao Liu & Hui Wang & Min Wang & Xiangsheng Gao, 2020. "Control chart pattern recognition using the convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 703-716, March.
    2. Pei-Hsi Lee & Shih-Lung Liao, 2023. "Residual Control Chart Based on a Convolutional Neural Network and Support Vector Regression for Type-I Censored Data with the Weibull Model," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    3. Ethel García & Rita Peñabaena-Niebles & Maria Jubiz-Diaz & Angie Perez-Tafur, 2022. "Concurrent Control Chart Pattern Recognition: A Systematic Review," Mathematics, MDPI, vol. 10(6), pages 1-31, March.
    4. Pamela Chiñas-Sanchez & Ismael Lopez-Juarez & Jose Antonio Vazquez-Lopez & Jose Luis Navarro-Gonzalez & Aidee Hernandez-Lopez, 2021. "Out-of-Control Multivariate Patterns Recognition Using D 2 and SVM: A Study Case for GMAW," Mathematics, MDPI, vol. 9(5), pages 1-14, February.
    5. Yuehjen E. Shao & Po-Yu Chang & Chi-Jie Lu, 2017. "Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process," Complexity, Hindawi, vol. 2017, pages 1-10, October.
    6. Aamir Majeed Chaudhary & Aamir Sanaullah & Muhammad Hanif & Mohammad M. A. Almazah & Nafisa A. Albasheir & Fuad S. Al-Duais, 2023. "Efficient Monitoring of a Parameter of Non-Normal Process Using a Robust Efficient Control Chart: A Comparative Study," Mathematics, MDPI, vol. 11(19), pages 1-30, October.
    7. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    8. Chuen-Sheng Cheng & Pei-Wen Chen & Yu-Chin Hsieh & Yu-Tang Wu, 2023. "Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-26, July.

    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:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01907-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.