Modeling of Manufacturing Processes using Hidden Semi-Markov Model and RSSI data

Abstract

Temporal behaviors, e.g., cycle time and throughput, are among essential key performance indicators for the management of manufacturing processes. This paper presents a statistical model that captures the processing time spent throughout a production line using RSSI data acquired from Bluetooth Low Energy (BLE) network. First, a Hidden Semi-Markov Model (HSMM) is formulated based on the characteristics of production processes. Then, a learning problem is discussed for the re-estimation of state duration probability distribution using the forward-backward algorithm. The Kullback- Leibler Divergence is used to verify the accuracy by comparing between the original and estimated state duration probability distribution with a score of 0.0573. Finally, physical experiment was performed to evaluate the proposed method.