Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies’ attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

The O&M (Operation and Maintenance) phase of offshore plants with a long life cycle requires heavy charges and more efforts than the construction phase, and the occurrence of an accident of an offshore plant causes catastrophic damage. So previous studies have focused on the development of advanced maintenance system to avoid unexpected failures. Nowadays due to the emerging ICTs (Information Communication Technologies) and sensor technologies, it is possible to gather the status data of equipment and send health monitoring data to administrator of an offshore plant in a real time way, which leads to having much concern on the condition based maintenance policy. In this study, we have reviewed previous studies associated with CBM (Condition-Based Maintenance) of offshore plants, and introduced an algorithm predicting the next failure time of the compressor which is one of essential mechanical devices in LNG FPSO (Liquefied Natural Gas Floating Production Storage and Offloading vessel). To develop the algorithm, continuous time Markov model is applied based on gathered vibration data.