Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

This study tries to develop the models of measuring the level of product availability accommodated for features of specific customers dividing customers into VIP customers and general customers. Functions of costs that the models are composed of are cost

Recently many researchers contributed to the understanding of Quality Control System, but the use of economics in the design of quality assurance system is limited in treatment of the relationship between the average incoming quality level (or average process quality level) of the incoming lot and the average outgoing quality level of this lot. In this study, a traditional concept of sampling inspection plan for the quality assurance system is extended to a consideration of economic aspects in total production system by representing and analyzing the effects between proceeding and succeeding production process including inspection process. This approach recognizes that the decision at each manufacturing process (or assembly process), is to be determined not only by the cost and the average outgoing quality level of that process, but also by the input parameters of the cost and the incoming quality to the succeeding process. By analyzing the effects of the average incoming and outgoing quality, manufacturing or assembly process quality level and sampling inspection plan on the production system, mathematical models and solution technique to minimize the total production cost for a general product manufacturing system with specified average outgoing quality limit are suggested.

본 연구에서는 이산성 연속형 최적성규준방법(DCOC)을 이용하여 직사각형 단면을 갖는 철근콘크리트 연속보의 최적설계 알고리즘을 유도하였고, 최적설계 프로그램을 개발하였다. 목적함수로서 건설경비는 콘크리트 경비, 철근 경비 그리고 거푸집 경비를 포함하였으며 이를 최소화하였다. 설계제약조건으로는 시방서상의 최대처짐제약, 휨 및 전단강도제약, 연성제약 그리고 설계변수에 대한 상하한 제약을 고려하였다. 쿤-터커 필요조건을 이용하여 최적성 규준을 설계변수의 항으로 명시적으로 유도하였으며, 이때 설계변수로는 보의 유효깊이와 철근비를 취하였다. 구조물 자중의 영향을 실제 시스템의 평형방정식에서 고려하였다. 설계변수들의 개선을 위한 반복과정과 컴퓨터 프로그램을 개발하였으며, 수치예를 들어 개발된 기법의 적용성과 효율성을 보였다.

본 연구의 목적은 동아시아 지역을 중심으로 글로벌 항만 물류네트워크를 구축하여 우리나라의 새로운 항만정책을 제안하는 것에 있다. 이러한 항만 물류네트워크의 구축을 위해 세계 50위 항만 중 21개의 항만을 중심으로, 컨테이너 화물량과 기항지를 분석하여 EU, 북미를 연결시 최소 물류비용인 동아시아 지역의 4개의 대표항만을 추출하였다. 그 결과 동아시아에서는 싱가포르, 홍콩, 상하이, 부산항이 추출되었다. 따라서, 우리나라는 싱가포르, 홍콩, 상하이항에 해외 터미널을 운영하고 부산항과 연계하는 글로벌 항만 물류네트워크를 구축하여 안정적인 화물을 확보해야 한다.