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Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment

Author

Listed:
  • Juan Bucay-Valdiviezo

    (Centro de Investigaciones de Ciencias Humanas y de la Educación (CICHE), Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador)

  • Pedro Escudero-Villa

    (Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba 060108, Ecuador)

  • Jenny Paredes-Fierro

    (Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba 060108, Ecuador)

  • Manuel Ayala-Chauvin

    (Centro de Investigaciones de Ciencias Humanas y de la Educación (CICHE), Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador)

Abstract

Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms for datasets with sufficient data. However, replicating these results with limited data is challenging. This work proposes the use of time series models to implement predictive maintenance in the equipment of an automotive assembly company with few records available. For this purpose, three models are explored—Holt–Winters Exponential Smoothing (HWES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)—to determine the most accurate forecasting of future equipment downtime and advocate the use of SAP PM for effective maintenance process management. The data were obtained from five equipment families from January 2020 to December 2022, representing 36 registers for each piece of equipment. After data fitting and forecasting, the results indicate that the SARIMA model best fits seasonal characteristics, and the forecasting offers valuable information to help in decision-making to avoid equipment downtime, despite having the highest error. The results were less favorable when handling datasets with random components, requiring model recalibration for short-term forecasting.

Suggested Citation

  • Juan Bucay-Valdiviezo & Pedro Escudero-Villa & Jenny Paredes-Fierro & Manuel Ayala-Chauvin, 2023. "Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment," Sustainability, MDPI, vol. 15(21), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15604-:d:1273783
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    References listed on IDEAS

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    1. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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