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An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

Author

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  • Yaping Li

    (College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China)

  • Haiyan Li

    (College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China)

  • Zhen Chen

    (Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Ying Zhu

    (Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.

Suggested Citation

  • Yaping Li & Haiyan Li & Zhen Chen & Ying Zhu, 2022. "An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations," Energies, MDPI, vol. 15(5), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1685-:d:757380
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    References listed on IDEAS

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