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

Yaping Li, Haiyan Li, Zhen Chen and Ying Zhu
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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

Energies, 2022, vol. 15, issue 5, 1-13

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.

Keywords: hidden Markov model (HMM); autocorrelation; residual chart (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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