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A novel technique for multiple failure modes classification based on deep forest algorithm

Author

Listed:
  • John Taco

    (University of Cincinnati)

  • Pradeep Kundu

    (KU Leuven)

  • Jay Lee

    (University of Maryland College Park)

Abstract

Deep learning is one of the emerging techniques that shows good failure modes classification prediction results due to its flexibility in recognizing patterns from raw sensor data. However, it requires complex hyperparameter optimization, high training time, and high computational hardware resources for neural network architecture. On the other hand, classical machine learning algorithms rely heavily on domain knowledge and manual feature engineering which is not always available in the industry. Therefore, we present an alternative method that learns characteristics of multivariate raw time series data to perform failure mode classification. The method is based on the deep forest algorithm, which is composed of two main processes: multi-grained scanning and cascade forest. The multi-grained scanning process windows the data and screens the times series to generate feature vectors automatically based on class probability distribution and hence recognize patterns from data. The cascade forest uses the output of the multi-grained scanning process and creates layers of random forests to make predictions. Each layer will perform fault classification, and the number of layers will increase until the accuracy of the classification does not improve. This layer-by-layer process is similar to deep learning, where the algorithm architecture is composed of different hidden layers. The presented methodology directly works with raw data in three domains: time, frequency, and time & frequency domain. Also, the method is validated using data provided by the Prognosis and Health Management (PHM) data challenge 2022 competition for hydraulic rock drill multiple failure mode classifications. The results show that the presented methodology is faster, less complex, and more accurate than deep learning algorithms.

Suggested Citation

  • John Taco & Pradeep Kundu & Jay Lee, 2024. "A novel technique for multiple failure modes classification based on deep forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3115-3129, October.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02185-2
    DOI: 10.1007/s10845-023-02185-2
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    References listed on IDEAS

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    1. Kainan Guan & Guang Yang & Liang Du & Zhengguang Li & Xinhua Yang, 2023. "Method for fusion of neighborhood rough set and XGBoost in welding process decision-making," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1229-1240, March.
    2. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    3. Tobias Schlosser & Michael Friedrich & Frederik Beuth & Danny Kowerko, 2022. "Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1099-1123, April.
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