Modeling of a Bernoulli-type data quality control process using hidden Markov chains

  • Authors

    • Mohammad D. AL-Tahat Department of Industrial Engineering, University of Jordan,Amman 11942, Jordan
    • Sami Abusafia
    2019-06-30
    https://doi.org/10.14419/ijet.v7i4.29392
  • Markov Chains, Hidden Markov Models, Control Charts, Attribute Data.

  • This article seeks to employ hidden Markov chains in quality control area in general and on binary-data process in particular. A hidden Markov model (HMM) has been applied on a Bernoulli-type data process to monitor its stability over time. In quality control respect, binary variables are widely used when an inspected item is classified into either conforming or nonconforming as per some specific specifications. In this article, we present a new scheme to monitor a quality process yielding binary outcomes or variables such that a new variable is proposed to regulate/evaluate process stability as time passes by. This variable determines the process probability of being statistically in control at each point in time and can be calculated using the developed hidden Markov model. As a result, it was found that it is straightforward to obtain inferences about process stability whether or not it is statistically in-control which, in turn, helps making decisions associated with actions needed when the process goes in an out-of-control way. Furthermore, unlike control charts, the judgment on the process state depends on the entire observation sequence not the current sample only.

     

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    AL-Tahat, M. D., & Abusafia, S. (2019). Modeling of a Bernoulli-type data quality control process using hidden Markov chains. International Journal of Engineering & Technology, 7(4), 6609-6614. https://doi.org/10.14419/ijet.v7i4.29392