In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

Intermittent duty of emergency generator has problems emitting large quantities of PM and NOx in exhaust gas. The aim of this study is to propose DPF system which can be applied to medium-large emergency generators. The system is composed of soot dust collector, silencer and filter trap, which is designed to reduce PM emissions at the emergency generator start-up. The pneumatic system controls a flow direction of exhaust gas to pass through the soot collector and filter trap until the engine reaches complete combustion condition. An experiment is performed to measure PM content and concentration to analyze the performance and characteristics of the proposed system.