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Online performance and proactive maintenance assessment of data driven prediction models

Author

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  • Yingjun Shen

    (Nanjing University)

  • Taohong Wang

    (Nanjing University)

  • Zhe Song

    (Nanjing University
    Nanjing University)

Abstract

Many Data-driven decisions in manufacturing need accurate and reliable predictions. Due to high complexity and variability of working conditions, a prediction model may deteriorate over time after deployed. Traditional performance evaluation indexes mainly assess the prediction model from a static perspective, which is difficult to meet the actual needs of model selection and proactive maintenance, resulting in unstable online prediction performance. For regression-based prediction models, this paper designs online prediction performance evaluation indexes (OPPEI) to evaluate the prediction model in terms of its accuracy, degradation speed, and stability. For proactive maintenance, this paper proposes a model maintenance evaluation method based on Principal Component Analysis (PCA). We use PCA to transform various performance indexes and extract the first principal component as a model maintenance evaluation index, which could reduce the over-sensitive or insensitive phenomenon of single indicator. The effectiveness of online prediction performance evaluation indexes and PCA-based proactive maintenance evaluation method are verified by simulation and several real-world load forecasting experiments.

Suggested Citation

  • Yingjun Shen & Taohong Wang & Zhe Song, 2024. "Online performance and proactive maintenance assessment of data driven prediction models," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3959-3993, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-024-02357-8
    DOI: 10.1007/s10845-024-02357-8
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    References listed on IDEAS

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    Cited by:

    1. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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