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Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

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  • Alexandre Martins

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 62001-001 Covilhã, Portugal)

  • Balduíno Mateus

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 62001-001 Covilhã, Portugal)

  • Inácio Fonseca

    (Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal)

  • José Torres Farinha

    (Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal
    Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal)

  • João Rodrigues

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 62001-001 Covilhã, Portugal)

  • Mateus Mendes

    (Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal)

  • António Marques Cardoso

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 62001-001 Covilhã, Portugal)

Abstract

The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

Suggested Citation

  • Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2651-:d:1094400
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    References listed on IDEAS

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    1. Sang M. Lee & DonHee Lee & Youn Sung Kim, 2019. "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-11, December.
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    Cited by:

    1. Balduíno César Mateus & José Torres Farinha & Mateus Mendes, 2024. "Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks," Energies, MDPI, vol. 17(2), pages 1-18, January.
    2. Ruiqi Tian & Santiago Gomez-Rosero & Miriam A. M. Capretz, 2023. "Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems," Energies, MDPI, vol. 16(20), pages 1-21, October.

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