Work

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

Year: 2022

Type: article

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.

Source: Energies

DOI: 10.3390/en15051685

Authors: Yaping Li, Haiyan Li, Zhen Chen, Ying Zhu

Institutions: Nanjing Forestry University, Shanghai Jiao Tong University

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,303

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, Qinglan Project of Jiangsu Province of China