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A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

Author

Listed:
  • Kevin Villalobos

    (University of the Basque Country UPV/EHU)

  • Johan Suykens

    (Katholieke Universiteit Leuven)

  • Arantza Illarramendi

    (University of the Basque Country UPV/EHU)

Abstract

The introduction of data-related information technologies in manufacturing allows to capture large volumes of data from the sensors monitoring the production processes and different alarms associated to them. An early prediction of those alarms can bring several benefits to manufacturing companies such as predictive maintenance of the equipment, or production optimization. This paper introduces a new system that allows to anticipate the activation of several alarms and thus, warns the operators in the plants about situations that could hamper the machines operation or stop the production process. The system follows a two-stage forecaster–analyzer approach on which first, a long short-term memory recurrent neural network based forecaster predicts the future sensor’s measurements and then, distinct analyzers based on residual neural networks determine whether the predicted measurements will trigger an alarm or not. The system supports some features that make it particularly suitable for smart manufacturing scenarios: on the one hand, the forecaster is able to predict the future measurements of different types of time-series data captured by various sensors in non-stationary environments with dynamically changing processes. On the other hand, the analyzers are able to detect alarms that can be modeled with simple rules based on the activation condition, and also more complex alarms on which it is unknown when the activation condition will be fulfilled. Moreover, the followed approach for building the system makes it flexible and extensible for other predictive analysis tasks. The system has shown a great performance to predict three different types of alarms.

Suggested Citation

  • Kevin Villalobos & Johan Suykens & Arantza Illarramendi, 2021. "A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1323-1344, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01614-w
    DOI: 10.1007/s10845-020-01614-w
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    References listed on IDEAS

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    1. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

    1. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.

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