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.

This study has suggested an image analysis system based on the Deep Learning for CCTV pedestrian detection and tracing improvement and did experiments for objective verification by designing study model and evaluation model. The study suggestion is that if someone’s face did not be recognized in crime scene CCTV footage, the same pedestrian would be traced and found in other image data from other CCTV by using Color Intensity Classification method for clothes colors as body features and body fragmentation technique into 7 parts (2 arms, 2 legs, 1 body, 1 head, and 1 total). If one of other CCTV footage has recorded its face, the identity of the person would be secured. It is not only detection but also search from stored bulk storage to prevent accidents or cope with them in advance by cost reduction of manpower and a fast response. Therefore, CIC7P(Color Intensity Classification 7 Part Base Model) had been suggested by learning device such as Machine Learning or Deep Learning to improve accuracy and speed for pedestrian detection and tracing. In addition, the study has proved that it is an advanced technique in the area of pedestrian detection through experimental proof.

딥러닝 모델은 주어진 학습용 데이터에서 탐지하고자 하는 물체의 특징을 추출하기 때문에, 딥러닝 모델 학습을 위한 학습용 데이터 구축은 매우 중요하다. 본 연구에서는 균열을 탐지하는 딥러닝 모델의 성능을 향상시키기 위해, 실제 콘크리트 구조물이나 아스팔트 도로 표면에서 자주 발견될 수 있는 나뭇가지, 거미줄, 전선 등을 학습 데이터에 자동으로 포함시키고, negative 영역으로 분류하는 알고리즘을 개발하였다. 제안된 알고리즘을 사용하여 학습된 딥러닝 모델을 실제 도로 표면에 발생한 균열 탐지에 적용하여 실제 균열 탐지에 사용될 수 있음을 보였다.

최근 사회기반시설(SOC)의 증가와 노후화에 따라 기존의 인력중심의 육안검사를 기반으로 한 안전점검은 경제성과 안전성, 효율성 면에서 한계를 가지고 있다. 본 연구에서는 육안점검의 한계를 개선하기 위해 딥러닝 모델 기반 물체를 탐지하는 기술을 활용하여 터널 콘크리트 균열을 자동으로 탐지하는 기술을 개발하였으며, 이를 실제 터널 영상에 적용하여 그 성능을 검증하였다.

Last few years, many researches on deep learning-based crack detection model have been reported in order to develop an efficient structure inspection method. While developing crack detection deep learning model, many research results reported the importance of the training data. Since most of the research results only qualitatively discussed the importance of training data, this study examine the influence of the training data by experiment, especially in the case of negative samples such as construction joint, spider web and concrete blocks.