• Journal of Korean Society of Steel Construction
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• v.14 no.2
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• pp.365-373
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• 2002

In this paper, the system reliability concept was presented to predict the lifespan of bridges. Lifetime distribution functions (survivor functions) were used to model real bridges to predict their remaining life. Using the system reliability concept and lifetime distribution functions (survivor functions), a program called LIFETIME was developed. The survivor functions give the reliability of component at time t. The program was applied to an existing Colorado state highway bridge to predict the failure probability of the time-dependent system. The bridge was modeled as a system, with failure probability computed using time-dependent deteriorating models.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1173-1184
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• 2018

The lifetime of a lithium-ion battery is one of the most important issues of the energy storage system (ESS) because of its stable and reliable operation. In this paper, the lifetime management method of the lithium-ion battery for energy storage system is proposed. The lifetime of the lithium-ion battery varies, depending on the power usage, operation condition, and, especially the selected depth of discharge (DOD). The proposed method estimates the total lifetime of the lithium-ion battery by calculating the total transferable energy corresponding to the selected DOD and achievable cycle (ACC) data. It is also demonstrated that the battery model can obtain state of charge (SOC) corresponding to the ESS operation simultaneously. The simulation results are presented performing the proposed lifetime management method. Also, the total revenue and entire lifetime prediction of a lithium-ion battery of ESS are presented considering the DOD, operation and various condition for the nations of USA and Korea using the proposed method.

• Journal of the Korean Society for Railway
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• v.8 no.5
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• pp.439-443
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• 2005

The biggest challenge bridge agencies face is the maintenance of bridges, keeping them safe and serviceable, with limited funds. To maintain the bridges effectively, there is and urgent need to predict their remaining life from a system reliability viewpoint. In this paper, a model using lifetime functions to evaluate the overall system probability of survival of a rail road bridge is proposed. In this model, the rail load bridge is modeled as a system. Using the model, the lifetime of the rail road bridge is predicted.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.92-98
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• 2017

The rapid growth of engineering technology and the emergence of systemized and large-scale engineering systems have resulted in complexity and uncertainty throughout the lifecycle activities of engineering systems. This complex and large-scale engineering system consists of numerous components, but system failure can be caused by failure of any one of a number of components. There is a real difficulty in managing such a complex and large-scale system as a part. In order to efficiently manage the system and have high reliability, it is necessary to structure a system with a complex structure as a sub-system. Also, in the case of a system in which cause of failures exist at the same time, it is required to identify the correlation of the components lifetime and utilize it for the design policy or maintenance activities of the system. Competitive risk theory has been used as a theory based on this concept. In this study, we apply the competitive risk theory to the models with combined structure of series and parallel which is the basic structure of most complex engineering systems. We construct a competing risks model and propose a mathematical model of net lifetime and crude lifetime for each cause of failure, assuming that the components consisting a parallel system are mutually dependent. In addition, based on the constructed model, the correlation of cause of failure is mathematically analyzed and the hazard function is derived by dividing into net lifetime and crude lifetime.

• Journal of Astronomy and Space Sciences
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• v.32 no.4
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• pp.367-377
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• 2015

Atmospheric drag force is an important source of perturbation of Low Earth Orbit (LEO) orbit satellites, and solar activity is a major factor for changes in atmospheric density. In particular, the orbital lifetime of a satellite varies with changes in solar activity, so care must be taken in predicting the remaining orbital lifetime during preparation for post-mission disposal. In this paper, the System Tool Kit (STK$^{(R)}$) Long-term Orbit Propagator is used to analyze the changes in orbital lifetime predictions with respect to solar activity. In addition, the STK$^{(R)}$ Lifetime tool is used to analyze the change in orbital lifetime with respect to solar flux data generation, which is needed for the orbital lifetime calculation, and its control on the drag coefficient control. Analysis showed that the application of the most recent solar flux file within the Lifetime tool gives a predicted trend that is closest to the actual orbit. We also examine the effect of the drag coefficient, by performing a comparative analysis between varying and constant coefficients in terms of solar activity intensities.

• Journal of Environmental Science International
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• v.27 no.6
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• pp.467-475
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• 2018

The lifetime of the electrode is one of the most important factors on the stability of the electrode. Since the lifetime of the DSA (Dimensionally stable anode) electrode is long, an accelerated lifetime test is required to reduce the test time. Beacuse there is no basis or standard method for accelerated lifetime testing, many researchers use different methods. Therefore, there is a need for basis and methods for accelerated lifetime testing that other researchers can follow. We designed a reactor system for accelerated lifetime testing and planned specific methods. Reactor system was circulating batch reactor. Reactor volume and cooling water tank were 12.5 L and 100 L, respectively. Electrode size was $2cm{\times}3cm$ (real electrolysis area, $5cm^2$). In order to maintain the harsh conditions, accelerated lifetime test was carried out in a high current density ($0.6A/cm^2$) and low electrolyte concentration (NaCl, 0.068 mol/L). Maintaining a constant temperature was an important operation parameter for exact accelerated lifetime test. As the accelerated lifetime test progressed, the active component of electrode surface was consumed and desorption occurred. At the point of 5 V rise, corrosion of the surface of the base material(titanium) also started.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.1-6
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• 2022

Modern computer systems usually have special hardware for operations used in deep learning workload even edge computing environment. Non-volatile memories (NVMs) have been considered for alternative memory storage because they consume little static energy and occupy small area. However, there is a problem for NVMs to be directly adopted. An NVM cell has limited write endurance, so that the lifetime of NVM-based memory system is much shorter than that of conventional memory system. To overcome this problem for the deep learning system, this paper proposes a novel method to extend the lifetime based on the analysis of the deep learning workloads. If an incoming block has more than a predefined number of frequently used values, the cacheline is defined as write friendly block. During the victim selection, the cacheline has lower possibility to be chosen as victim. The experimental results show that the lifetime is increased by about 50% and energy consumption is decreased by 3% with a little performance hurt.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.339-345
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• 2013

This paper addresses the system lifetime maximization algorithm in multi-hop sensor network system. A multi-hop sensor network consists of many battery-driven sensor nodes that collaborate with each other to gather, process, and communicate information using wireless communications. As sensor-driven applications become increasingly integrated into our lives, we propose a energy-aware scheme where each sensor node transmits informative data with adaptive data rate to minimize system energy consumption. We show the optimal data rate to maximize the system lifetime in terms of remaining system energy. Furthermore, the proposed algorithm experimentally shows longer system lifetime in comparison with greedy algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.221-227
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• 2022

The reliability improvement of PV systems is an important factor in reducing the cost of PV energy because it is closely related to the annual energy production as well as the maintenance cost of PV systems. The reliability of PV inverters plays a key role in the reliability of PV systems because it is regarded as one of the most reliable critical parts of PV systems. The lifetime evaluation of PV inverters considering the mission profile in the design phase plays an important role in reliability design to ensure the required lifetime of PV inverters. In this paper, the lifetime of representative single-phase T-type and I-type NPC inverters are comparatively evaluated by considering the mission profile of a PV system recorded at Iza, Spain. Furthermore, the effect of the pulse width modulation methods on the lifetime is also discussed. The lifetime evaluation of PV inverters is performed at the component-level first and then the system level by considering all power devices.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.243-245
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• 2000

In general, aluminum electrolytic capacitors are used in the DC link of PWM inverters fur ac motor drives. The capacitor usually has the shortest lifetime in the system and then determines the lifetime of the inverter system. In this paper, a method of capacitor lifetime estimation is proposed by using an ESR(equivalent series resistance) model and a heat transfer model of capacitor, from which internal operating temperature is estimated. Then, the lifetime is predicted by Arrhenius's equation. A practical example is presented.