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Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data

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  • Shen Yin
  • Guang Wang
  • Xu Yang

Abstract

In practical industrial applications, the key performance indicator (KPI)-related prediction and diagnosis are quite important for the product quality and economic benefits. To meet these requirements, many advanced prediction and monitoring approaches have been developed which can be classified into model-based or data-driven techniques. Among these approaches, partial least squares (PLS) is one of the most popular data-driven methods due to its simplicity and easy implementation in large-scale industrial process. As PLS is totally based on the measured process data, the characteristics of the process data are critical for the success of PLS. Outliers and missing values are two common characteristics of the measured data which can severely affect the effectiveness of PLS. To ensure the applicability of PLS in practical industrial applications, this paper introduces a robust version of PLS to deal with outliers and missing values, simultaneously. The effectiveness of the proposed method is finally demonstrated by the application results of the KPI-related prediction and diagnosis on an industrial benchmark of Tennessee Eastman process.

Suggested Citation

  • Shen Yin & Guang Wang & Xu Yang, 2014. "Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1375-1382, July.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:7:p:1375-1382
    DOI: 10.1080/00207721.2014.886136
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    References listed on IDEAS

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    1. Serneels, Sven & Verdonck, Tim, 2009. "Principal component regression for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3855-3863, September.
    2. Jinyong Yu & Ming Liu & Wei Yang & Ping Shi & Shuya Tong, 2013. "Robust fault detection for Markovian jump systems with unreliable communication links," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(11), pages 2015-2026.
    3. Shen Yin & Steven Ding & Adel Abandan Sari & Haiyang Hao, 2013. "Data-driven monitoring for stochastic systems and its application on batch process," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1366-1376.
    4. Dongsheng Du & Bin Jiang & Peng Shi, 2012. "Sensor fault estimation and compensation for time-delay switched systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(4), pages 629-640.
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    Cited by:

    1. Huang, Wenyang & Wang, Huiwen & Wei, Yigang, 2023. "Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Kandukuri, Surya Teja & Klausen, Andreas & Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar, 2016. "A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 697-708.

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