Detection of machines producing defective products for prevention and maintenance planning using Hidden Markov model : Case study for soft drink manufacturing factory

Abstract

This research proposes a hidden Markov model for detecting machines, with multiple fillers, producing soft drink, for quality control, in order to reduce defective products. The proposed model is compared with a classical generalized estimating equations model (GEE). The monthly data, the number of defective products, the correctness and the time spending in correction, were collected from a machine in a sample factory in Pratumthanee province. The two hidden states of the fillers are workable and unworkable. The states has a logistic distribution. The results show that the correctness of the filler error occurrence does not improve the probability of being in a unworkable state, neither the period of time spending on correction. The current state seems to be the same as the previous state