Detection of defective machines in auto parts factories using bayesian hidden markov models

Abstract

The objectives of this research are to propose a proper model for detecting defective machines in auto parts factories, to apply the proposed model to the defective product data, and to investigate the factors influencing on the machine producing defective products. A Bayesian hidden Markov model (HMM) is adopted. The results show that if the number of defective products increases, the machine will be in the hidden bad state. The probability of changing from a hidden bad state to a hidden good state is higher than the probability of changing from a hidden good state to a hidden good state. The highest probability that the machine will be in a good state is at the work process 4, followed by 1, 3 and 2 respectively. The highest probability that the machine will be in hidden good state is at the Worker no. 10 followed 9, 3, 12, 7, 2, 1, 6, 11, 4, 8 and 5, respectively, and The highest probability that the machine will be in the hidden good state is at the product type 1 followed by 2 and 3, respectively.