High Pressure Liquefied Natural Gas Pump (HP-LNG Pump) is important equipment in the LNG receiving terminal process, and plays a key role in determining the total supply capacity of natural gas in LNG terminal. Therefore, the condition monitoring, fault diagnostics and prognostics technologies are applied to implement the CBM (Condition Based Maintenance) strategy for HP-LNG pumps, which can support for appropriate maintenance decision. Currently, a number of valuable diagnostic models and methods have been proposed in machine diagnostics. However, most intelligent diagnostic models have generally validated using an experimental data, and still not focus on industrial real data because of the complex nature in real industry. In this paper, intelligent fault diagnostic performances using three conventional classification algorithms such as SVM (Support Vector Machines), k-NN (k-Nearest Neighbor) and Fuzzy-ARTMAP have been evaluated for the CBM decision of HP-LNG pump. Comparative results indicate that the fault classification using SVM provides more accurate performance compared to other classification methods for HP-LNG pump.

LNG는 최근 해양 환경 규제 강화로 인해 친환경 선박 연료로 각광받고 있다. 본 연구에는, 해상 부유식 LNG 벙커링 터미널의 상업화 가능성을 분석하기 위해 울산항 LNG 벙커링 수요 전망을 조사하였다. LNG 벙커링 환경 분석과 전 세계 경쟁항만의 LNG벙커링 동향을 통하여 울산항의 LNG 벙커링 전망을 도출하였다. 연구결과, 정기운항을 하는 울산항의 자동차 운반선과 원유운반선은 LNG 연료 선박으로 전환될 가능성이 높고 울산항의 LNG 벙커링 수요는 2030년 650,000톤에서 900,000톤 규모로 예상되므로 울산항은 향후 국내 FLBT 시범사업에 적합한 항구가 될 것이라 예상된다. LNG is being spotlighted as a clean marine fuel because of recent trend in reinforcement of marine environmental regulation. In this paper, demand prospect of LNG bunkering for Ulsan port is carried out to analgize the possibility of commercialization of floating LNG bunkering terminal. Environmental analysis for LNG bunkering and LNG bunkering trends of competitive ports in the world are considered to draw out the prospection of LNG bunkering demand in Ulsan. As a result, car carrie and oil carrier were expected to have more possibility in switching to LNG fuelled ship. The LNG bunkering demand in Ulsan. As a result, car carrier and oil carrier were expected to have more possibility in switching to LNG fuelled ship. The LNG bunkering demand in Ulsan port was expected to be about from 650,000 ton to 900,000 ton in 2030 and Ulsan port is prospected to be a good port for FLBT business in th future.

The Liquefied Natural Gas (LNG) receiving terminal is designed to deliver a specified gas rate into a pipeline network, and High Pressure LNG pumps are crucial equipment because they determine the total supply capacity of natural gas in the terminal. Therefore, condition of HP-LNG pumps are regularly monitored and managed based on Condition Based Maintenance (CBM) technique. In general CBM system is composed of a number of functional capabilities such as data acquisition, signal processing, feature extraction, diagnostics, prognostics and decision reasoning. In this paper, a comparative study on evaluation of the performance of feature extraction techniques is carried out for intelligent fault diagnostics of HP-LNG pump using real industrial data. In order to estimate the abilities of feature extraction techniques, three methods such as Principal Component Analysis (PCA), Liner Discriminant Analysis (LDA) and Distance Evaluation Technique (DET) are employed and tested for the features based fault diagnostics. The accuracy of fault classification performance is estimated by using One-Against-All Multi-Class SVMs (MCSVMs) technique. The result shows that DET has a better capability than other conventional techniques as a feature extraction technique for fault diagnostics of HP-LNG pump.

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

CNG(Compressed Natural Gas)충전소의 주요 핵심설비는 압축기이며, 대부분의 압축기는 윤활이 필요하다. CNG압축기의 윤활유(oil)는 충전 중에 압력 레귤레이터, 엔진 연료공급시스템 등에 전이(Carry-over)되어 천연가스차량의 성능에 부정적인 영향을 미친다. 따라서 이러한 문제를 사전에 방지하기 위해서는 전이되는 오일의 양을 정량적으로 측정하여 공급되는 압축천연가스의 품질관리를 강화하는 것이 필요하다. 본 연구에서는 CNG오일전이를 측정하기 위해 중량법(Gravimetric method)을 사용한 샘플링 튜브 및 샘플링 장비를 개발하였다. 또한, CNG 충전소 6개소를 대상으로 CNG를 채취하였으며, 오일전이를 정량적으로 분석하였다. 측정된 전체 오일전이양은 2.569에서 6.509ppm 이었으며, 측정된 결과를 선행연구 결과와 비교 검증하였다. The core of the CNG fueling station is the compressor and most of CNG compressors in Korea require lubrication. Lubrication oil of CNG compressor that can be transferred into the pressure regulators and the engines of fueling system can cause a negative effect on NGV(Nature Gas Vehicle) performance during refueling due to oil Carry-over. In order to avoid the problem, it is necessary to enhance the quality of the compressed natural gas by measuring quantitatively the amount of the transferred oil. In this research, a sampling device and sampling tube were developed, which can be used with a gravimetric method of detection to measure CNG oil Carry-over. In addition, CNG samples were taken at 6 pre-selected CNG fueling stations and analysed for their trace oil Carry-over. The measured total oil Carry-over ranged from 2.569 to 6.509 ppm. This test measurements were compared with those of previous studies to verify the results.

Steel markets are very competitive and demand greater gauge precision and higher production rates. These growing requirements result in tandem rolling mill, which is of substantial interest to the steel industry, in order to improve quality and productivity. In such an environment, it is important to construct appropriate condition monitoring, which can lead to achieving the highest economic efficiency and avoiding equipment damage. This paper proposes a comprehensive condition monitoring methodology based on statistical feature extraction technique to increase the efficiency of feature extraction from high-dimensional feature space. It is examined that one can explore easily the effective features by using three-dimensional feature space for the condition monitoring. The method has been applied on condition monitoring of the stationary rolling in steel industry.

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Liquefied natural gas (LNG) takes up six hundreds of the volume of natural gas to be reached below the boiling temperature (-162 degrees C), which makes storage and transportation much easier. Imported Liquefied natural gas is transported to ground storage tanks via pipeline using cargo pump on the LNG carrier vessel. In LNG receiving terminal, primary LNG pumps are installed in the storage tanks and supply the LNG to secondary pumps with 8 bar. So the primary LNG pumps should be maintaining stable conditions to manage the LNG in stock. In this paper, to find out the cause of abnormal vibration at LNG primary pump, vibration analysis has been performed. Therefore, it was estimated and confirmed that random low frequencies were originated from fluid instabilities due to the inducer bolt being loosed because of the thermal expansion difference between bolt and other part in the cryogenic fluid. So, to prevent the looseness of inducer bolt, the dimensions of inducer bolts are modified. After modification, low frequency bands could be disappeared and pumps could recover its original performance characters.

In turbomachinery rotor, there are small differences in the structural and/or geometrical properties of individual blades, which are referred to as blade mistuning. Mistuning effects of the forced response of bladed disks can be extremely large as often reported in many studies. In this paper, the pattern optimization of intentional mistuning for bladed disks considering with intentional mistuning intensity effect is the focus of the present investigation. More specifically, the class of intentionally mistuned disks considered here is limited, for cost reasons, to arrangements of two types of blades (A and B, say) and Genetic Algorithm is used to optimize the arrangement of these blades around the disk to reduce the forced response of blade with intentional mistuning intensity levels.