The research of hidden Markov models for overall equipment effectiveness analysis in smart manufacturing system

Industrial Engineering of Management, National Taipei University of Technology, Taipei, Taiwan

^* Corresponding author: claire830110@gmail.com

Abstract

In the manufacturing industry, excellent product quality and increased production flexibility can be achieved by eliminating waste and improving production efficiency. In the past, the manufacturing industry used manual records of production information, but this method is characterized by low efficiency and high error rates. Even if a programmable logic controller and radio-frequency identification are employed, problems still occur because of constraints such as different machine types and high costs. The use of a cyber–physical system and information visualization requires the collection of manufacturing information in order to facilitate the analysis of manufacturing data. Monitoring the machining status. This study proposes an approach for segmenting machine-processed signals. With plug-and-play noninvasive current-sensing equipment to collect machine production information, this approach can immediately determine the state of the manufacturing process and calculate the machine utilization, machine production cycle, and production quantity. The goal is to enable the use of this method with this equipment, improve machine utilization, instantly identify the production quantity, and reduce equipment idle time to reduce manufacturing waste, thus rendering production management more convenient and faster.