The on-line quality monitoring procedure for attributes proposed by Taguchi has been critically studied and extended by a few researchers. Determination of the optimum diagnosis interval requires estimation of some parameters related to the process failure mechanism. Improper estimates of these parameters may lead to incorrect choice of the diagnosis interval and consequently huge economic penalties. In this paper, we highlight both the theoretical and practical problems associated with the estimation of these parameters, and propose a structured approach to solve them. For the so-called Case II model, two estimation methods, one based on Bayesian procedure and the other on the EM algorithm, are developed and compared using extensive simulations.
These two methods are demonstrated using a case study from a hot rolling mill. A Bayesian method is proposed for estimation of parameters in Case III. A systematic way to utilize available engineering knowledge in eliciting the prior for the parameters is also discussed.