Probabilities Of Basic Events Research Articles

Reliability analysis of diesel engine cooling system based on improved Bayesian network

For the benefit of solving the problem of absoluteness of conditional probability distribution and difficulty in obtaining basic event probability in Bayesian networks, this paper studies the reliability of diesel engine cooling systems through leakage noise or gate intuitionistic fuzzy Bayesian network. Firstly, the leakage noise or gate is introduced into the intuitionistic fuzzy Bayesian network, which makes the results more realistic. Secondly, prior probability in the Bayesian network is obtained based on the similarity aggregation method of triangular intuitionistic fuzzy numbers. The results show that the introduction of leakage noise or gate, intuitionistic fuzzy set, and Bayesian network can solve the problem that the probability of basic events in a Bayesian network is difficult to obtain. The conditional probability table corrected by leakage noise or gate is more realistic and greatly reduces the workload. It is determined that the two weakest components of the cooling system are the thermostat and the water pump. The results show that the way in this paper can provide support for the reliability analysis of the complex system.

An Interval-Valued Intuitionistic Fuzzy Bow-Tie Model (IVIF-BT) for the Effectiveness Evaluation of Safety Barriers in Natural Gas Storage Tank

Safety barriers (SBs) are important means of reducing failure risks of process systems. As barriers vary in type and function, their effectiveness needs to be evaluated in order to find a more reasonable configuration strategy. However, in practice, there is often a lack of accurate and complete data relating to SBs, which poses a significant challenge in quantitatively assessing their effectiveness. To address this issue, in this study, we propose a semi-quantitative approach for evaluating the effectiveness of both preventive and protective barriers in process systems by integrating expert elicitation, interval-valued intuitionistic fuzzy numbers (IVIFNs), and a bow-tie model. In this approach, the bow-tie model is first applied to describe the system failure scenarios and the action phases of the barriers, and then IVIFNs with expert judgment are introduced to obtain the failure probabilities of basic events and the effects of SBs. Subsequently, the effectiveness of each barrier is measured by comparing the relative change in failure risk due to the addition of the barrier. To verify the feasibility of this approach, a natural gas storage tank with some barriers was analyzed. The results show that the regular inspection of the deformation or damage of the storage tank has the highest effectiveness, followed by the installation and regular maintenance of safety electrical equipment. Furthermore, compared to a single barrier, multiple barriers can significantly reduce the system risk.

Process accident prediction using Bayesian network based on IT2Fs and Z-number: A case study of spherical tanks.

This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts' confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10-4 variance compared to the 10-5. This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.

Risk-informed design of advanced nuclear reactor: A target unavailability approach

Incorporating specified safety and production targets during the design phase of an engineered facility can effectively support reductions in construction and operation costs of nuclear power plants. In addition, this supports efforts toward making nuclear power technology more competitive and profitable than other electric power production forms by incorporating operational and design features into the design phase that directly support the increased likelihood of meeting such originating targets. Unavailability is the key parameter to establish plant-level capabilities that cascade down to function, system, and component levels to ensure that all sources of unavailability are addressed in the facility's design phases. In this article, two methods are presented to propose unavailability targets for nuclear reactor systems to risk-inform and optimize the design features, from an availability perspective: the first method is a simple method, and the second is a non-dominated sorting genetic algorithm III (NSGA-III) multi-objective optimization. These iterative methods are demonstrated in this paper for a hypothetical facility with a design maturity such that there is knowing the main components to comply with the engineering and security functions of the systems. With an assumed mature design, it is implied that generic data to execute quantitative reliability, and availability analyses are available at the systems levels that are sufficient to support a design-specific probabilistic risk assessment (PRA). The proposed methods modify the probabilities of basic events within the fault tree structures that model system and component unavailability sources that will be required for the facility’s operating license. The methods provided are applied to visualize the characteristics of the problem and the quantitative results. This work forms the basis for future work in which design alternatives affecting unavailabilities can be estimated and compared to their associated unavailability targets at different levels of granularity.

Dynamic risk assessment model for third-party damage to buried gas pipelines in urban location class upgrading areas

Buried gas pipeline leakage accidents caused by third-party interference in urban location class upgrading areas are time-varying and can cause serious consequences. This study proposed an integrated dynamic risk assessment model to address this issue. First, a bow-tie model combining a fault tree (FT) with an event tree (ET) was used to construct an overall evolutionary process from basic events to accidents and thus mapped into a Bayesian network (BN). The prior probabilities of basic events in FT and safety barriers in ET were determined by four types of methods, and the critical basic events were identified by sensitivity analysis. By integrating the Hierarchical Bayesian analysis method with time-varying precursor data, the dynamic probability updating of critical basic events and safety barriers was achieved, enabling adaptation of accident probabilities using BN. Then, a dynamic calculation method for accident consequences was proposed by integrating the appropriate leakage rates with a new time-varying method for calculating fatalities. Finally, a new risk matrix method was proposed to dynamically evaluate the risk level by integrating the accident probabilities with the consequences. A case study showed that the proposed model could dynamically evaluate the integrated accident risk, providing explicitly quantified support for risk management and emergency rescue of buried gas pipeline networks in urban location class upgrading areas.

Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation

The lithium battery energy storage system (LBESS) has been rapidly developed and applied in engineering in recent years. Maritime transportation has the advantages of large volume, low cost, and less energy consumption, which is the main transportation mode for importing and exporting LBESS; nevertheless, a fire accident is the leading accident type in the transportation process of LBESS. This paper applied fault tree analysis and Bayesian network methods to evaluate the fire accident risk of LBESS in the process of maritime transportation. The Bayesian network was constructed via GeNIe 2.3 software, and the probability of LBESS fire accidents during maritime transportation was calculated based on the probability of basic events occurring. The results showed that an unsuitable firefighting system for putting out lithium battery fires, high humidity, and monitoring equipment without a real-time alarm function have the most significant influence on the occurrence of LBESS fire accidents during maritime transportation. The research work of this paper provides a theoretical basis for the risk assessment of LBESS during maritime transportation.

Leverage Bayesian Network and Fault Tree Method on Risk Assessment of LNG Maritime Transport Shipping Routes: Application to the China–Australia Route

The China–Australia Route, which serves as the southern economic corridor of the ‘21st Century Maritime Silk Road’, bears great importance in safeguarding maritime transportation operations. This route plays a crucial role in ensuring the security and efficiency of such activities. To pre-assess the risks of this route, this paper presents a two-stage analytical framework that combines fault tree analysis and Bayesian network for evaluating the occurrence likelihood of risk of transporting liquefied natural gas (LNG) on the China–Australia Route. In the first stage, our study involved the identification of 22 risk influencing factors drawn from a comprehensive review of pertinent literature and an in-depth analysis of accident reports. These identified factors were then utilized as basic events to construct a fault tree. Later, we applied an expert comprehensive evaluation method and fuzzy set theory, and by introducing voting mechanism into expert opinions, the prior probability of basic events was calculated. In the second stage, a fault tree was transformed into a Bayesian network, which overcame the deficiency that the structure and conditional probability table of the Bayesian network find difficult to determine. Consequently, the employment of the Bayesian network architecture was applied to forecast the likelihood of LNG maritime transport along the China–Australia shipping pathway. The probability importance and critical importance of each basic event was calculated through an importance analysis. The development of a risk matrix was achieved by considering the two primary dimensions of frequency and impact, which were subsequently utilized to categorize all relevant risk factors into high, moderate, or low risk categories. This allowed for effective risk mitigation and prevention strategies to be implemented. Finally, assuming that the final risk occurs, we calculated the posterior probability of the basic event to diagnose the risk. The research findings indicate that the primary reasons for the risk of transporting LNG on the China–Australia Route are the impact of natural forces and epidemics, piracy and terrorist attacks, and the risk of LNG explosions. In the final section, we provide suggestions and risk control measures based on the research results to reduce the occurrence of risks.

Providing an approach to analyze the risk of central oxygen tanks in hospitals during the COVID-19 pandemic

The central oxygen unit of hospitals is considered a high-risk unit, requiring high safety standards to maintain the integrity of the system during the COVID-19 pandemic. The linear reasoning assumption of conventional risk analysis methods cannot adequately describe these modern systems, which are characterized by tight connections and complex interactions between technical, human, and organizational aspects. Therefore, this study presents a new and comprehensive approach to oxygen tanks in hospitals during the COVID-19 pandemic. In this study, trapezoidal fuzzy numbers were used to calculate failure rates. After determining the probability of basic events (BEs), intermediate events (IE), and top event (TE) with fuzzy logic and transferring it into Bayesian Network (BN), deductive and inductive reasoning, and sensitivity analysis were performed using RoV in GeNIe software. The results of the case study showed that the IE of “Human Error” had the highest probability of fuzzy fault tree (FFT) and the probability of oxygen leakage was lower using FBN than FFT. According to the results, BE16 (failure to use standard and updated instructions) and BE12 (defects in the inspection and testing program of tank devices) had the highest posterior probability, while based on the FFT results, BE4 (defects in the external coating system of the tank) and, BE3 (Corrosive environment (acidity state)) had the least probability. According to the sensitivity analysis, basic events 10, 11, and 16 were the most important in the oxygen leakage event with a very small difference, which was almost in line with the results of posterior FBN (FBNPO). Updating the existing guidelines, fixing defects in the inspection of all types of tank gauges, and testing related equipment can greatly help the reliability of these tanks. Root cause analysis of these events provides opportunities for prevention and emergency response in critical situations, such as the COVID-19 pandemic.

Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model

This paper presented a fuzzy Bayesian network (FBN) model for failure risk analysis of oil storage tank leakage. The Bow-tie model for storage tank failure in oil depots was established and used to determine key risk factors. Fuzzy set theory and expert judgments were used to calculate the prior probability of basic events, after which the model was mapped to a Bayesian network. The probability of storage tank failure was calculated as 7.80 E-02. As new evidence was obtained, probability updating was tested using forward inference. Posterior probability of a basic event, seal gasket aging, showed the greatest risk increase, from 1.05 E-02–1.10 E-01. Using these newly acquired key factors, probability adaptation was used to conduct a dynamic analysis of storage tank failures and the consequence states. The dynamic failure probability was found to increase by 28.67 %. The model underwent sensitivity analysis and literature data analysis. This fuzzy Bayesian network model is beneficial to dynamic risk analysis in oil depot management. The findings can be used to reduce the likelihood of oil tank failure and decrease the severity of accidents. Suggestions were made, for site engineers and management, to reduce the risk of hazardous events.

Development and Implementation of a Direct Evaluation Solution for Fault Tree Analyses Competing With Traditional Minimal Cut Sets Methods

Fault tree analysis (FTA) is a well-established technique to analyze the safety risks of a system. Two specific prominent FTA methods, largely applied in the aerospace field, are the so-called minimal cut sets (MCS), which uses an approximate evaluation of the problem, and the direct evaluation (DE) of the fault tree, which uses a top-down recursive algorithm. The first approach is only valid for small values of basic event probabilities and has historically yielded faster results than exact solutions for complex fault trees. The second one means exact solutions at a higher computational cost. This article presents several improvements applied to both approaches in order to upgrade the computing performance. First, improvements to the MCS approach have been performed, where the main idea has been to optimize the number of required permutations and to take advantage of the available information from previous solved subsets. Second, improvements to the DE approach have been applied, which deal with a reduction of the number of recursive calls through a deep search for independent events in the fault tree. This could dramatically reduce the computation time for industrial fault trees with a high number of repeated events. Additional implementation improvements have been also applied regarding hash tables, and memory access and usage, but also implementing the so-called “virtual gates”, which enable limitless children on each gate. The results presented hereafter are promising, not only because they show that both approaches have been highly optimized compared to the literature, but also because a DE solution has been achieved, which can compete in time resources (and obviously in precision) with the MCS approach. These improvements are relevant when considering the industrial, and more specifically the aeronautical, implementation and application of both techniques.

Oil Detection Fault Tree Analysis Based on Improved Expert’s Own Weight–Aggregate Fuzzy Number

Oil detection technology improves the reliability of machinery or equipment. The physical and chemical indicators of the fluid can reflect the cause of the failure in various aspects, which can prevent major accidents to the greatest extent by setting up a fault tree. Owing to the lack of data, it is difficult to accurately obtain the basic event probabilities, which makes it difficult to diagnose faults. The expert evaluation method and aggregated fuzzy numbers are used to exact the failure probability, where the event probability is evaluated as the subjective will of the expert. To improve the probabilistic accuracy, weights are improved by the combined assignment method as well as the reasonableness analysis. A fault tree diagnostic model is constructed for qualitative and quantitative analysis, taking the ship engine oil viscosity high fault as an example. According to the results, the model can provide a comprehensive analysis of physical and chemical indicators. Experts’ own weights have a large impact on the failure probability, with their weight changes leading to a change in the failure ranking. From the discrimination, following a Bland–Altman analysis of the results, the selected combined empowerment method improved the discrimination of the results by 4.8% compared to the traditional method, with 100% data consistency, which proved that the improvement was reliable and effective. The structure of this fault diagnosis model is clear, which can quickly give the fault cause and probability reference value.

Risk and Reliability Assessment of Metal Lathe Machining Operation with DBN‐FFTA Hybrid Approach

A metal lathe is a high‐performance tool used in the field of metalworking to remove excess material and shape metal parts. However, engaging in metal turning operations carries risks that can lead to serious accidents and physical harm. It is crucial to ensure that these systems are functioning correctly, as any malfunction or flaw can lead to dangerous situations. To maintain safety in industrial environments, it is important to assess the risks and reliability of the equipment. A study was conducted using a method called fuzzy fault tree analysis (FFTA), combined with fuzzy logic, to determine the probability of basic events. Bayesian networks (BNs) were utilized to update probabilities and overcome limitations of the fault tree (FT). A Dynamic Bayesian Network (DBN) was employed to estimate the reliability of a metal lathe in a specific scenario. The FT identified 57 root events and estimated the probability of workpiece FLY‐OUTS as 0.03174329 using the FT method and 0.031505849 using the BN method. Based on the predictions of the DBN, system reliability decreased by 19.89% after 24 months. The FT diagram comprehensively captured all the factors associated with FLY‐OUTS, highlighting that improper closing of the part on the tool was a significant contributing factor. The study concludes by proposing safety measures for turning operations based on the identified critical events.

Risk Analysis and Evaluation of Nuclear Security Radiation Events in Spent Fuel Reprocessing Plants

Spent fuel reprocessing is of great significance to the nuclear fuel cycle and the sustainable development of nuclear energy. At the same time, nuclear security radiation incidents in the spent fuel reprocessing plant are also related to national personal and property safety, which play a pivotal role. In this paper, the spent fuel reprocessing plant is divided into four plant areas: the main process area, the three-waste area, the auxiliary equipment area, and the pre-plant area, which are further subdivided into 12 evaluation units. The expert scoring method is used to score and evaluate the possibility of eight basic nuclear accident types in each area, namely radioactive dispersal device, computer nuclear security, destruction of nuclear facilities, transportation nuclear security, internal threat, potential threat, illegal transfer, and theft. According to the professional titles, length of service, education and other qualifications of experts, different weights are assigned to the experts. The scoring results are applied to the Fault Tree Analysis (FTA) of nuclear security events as the probability of basic events, so as to obtain the risk of each basic event. At the same time, the fuzzy comprehensive evaluation method and probability–mathematical statistics method are used to evaluate each evaluation unit to determine the risk of each evaluation unit and the plant area. There results show that the main process area has the highest risk degree, while the pre-plant area has the lowest risk degree, and there is a 1.5-fold relationship. This research provides theoretical and technical support for the safety management and operation of spent fuel reprocessing plants. The analysis results of this paper can be used as a reference for the proportion of nuclear security protection improvements in each plant area, so as to achieve an efficient safety protection effect. The research method in this paper can be also applicable to other similar places by providing as input the corresponding probability of occurrence to obtain the index of its risk degree, so as to reasonably allocate funds and manpower and reduce risks.

An intuitionistic fuzzy probabilistic Petri net method for risk assessment on submarine pipeline leakage failure

Under the influence of various factors, the submarine pipelines will be damaged, resulting in oil and gas leakage accidents. It is essential to perform risk assessments to prevent accidents. However, accurate and sufficient historical data usually can not be obtained. To solve this problem, this study proposes a probabilistic Petri net (PPN) method based on intuitionistic fuzzy evidential reasoning (IFER), which can determine the failure probability of basic events through domain expert experience, and quantitatively evaluate the probability of submarine pipeline leakage failure. The main contributes include: according to DNV-RP-F107, a group of intuitionistic fuzzy numbers is defined and an improved probability conversion method is proposed for defuzzification; an IFER approach is proposed, which uses belief structures as expert linguistic terms and transforms them into IFNs to deal with the uncertainty of expert evaluation; a probabilistic Petri net method is proposed to quantitatively calculate the failure probability of top event and identify the critical events. Finally, the proposed intuitionistic fuzzy probabilistic Petri net (IFPPN) method is illustrated by a case study. By comparing with the existing methods, the feasibility of this method is validated, and the evaluation results can provide a reference for the safe operation of the pipeline system.

Use of fuzzy fault tree analysis and Bayesian network for occurrence likelihood estimation of navigational accidents in the Qinzhou Port

This paper utilizes a fuzzy fault tree analysis and Noisy-OR gate Bayesian network to estimate the occurrence likelihood of navigational accidents. The kernel of this proposed method is first to construct the fault tree from investigation reports of navigational accidents, to calculate the occurrence probability of basic events using fuzzy set, to transform the fault tree to Bayesian network using the Noisy-OR gate. The merit of the developed model can overcome the problem of absolute description of the relationships between basic events and intermediate events. Finally, the model is applied to Qinzhou Port, the results are reasonable by comparing the results in other waterways. Moreover, the key influencing factors are identified from minimum cut set analysis and sensitive influencing factors are quantified sensitivity analysis. Consequently, the findings are beneficial for the maritime authorities to take countermeasures for navigational accidents prevention.

Risk analysis of collision accidents during underway STS berthing maneuver through integrating fault tree analysis (FTA) into Bayesian network (BN)

Ship to ship tanker maneuvering operation, which take place while both ships are underway and in open sea circumstances, present a number of unique challenges in terms of collision that differ from conventional port berthing maneuvers. Given the flammable quality of the commodities transported and the limited access to emergency response vehicles owing to the operation's distance from the land, risk analysis of these berthing maneuvers is critical. Therefore, the fundamental purpose of this research is to conduct a probabilistic risk analysis of collision incidents by integrating fault tree analysis with bayesian network analysis using a fuzzy method. Initially, the fault tree analysis (FTA) was utilized to determine critical events and their logical structure. Expert opinion and fuzzy theory is used to deal effectively with uncertainty in probabilities of basic events. Considering the constraints of FTA in terms of conditional dependencies and static nature, a Bayesian network mapping from FTA is developed to overcome this limitation. BN is utilized to simulate the risk associated with the entire operating process by probability updating and sensitivity analysis. The results indicated that the “mooring line breakdown”, “main engine failure” and “steering system failure” root nodes are the most contributing factors to the STS collision accidents. The findings show that this research technique is realistic and reasonable, and makes an important contribution to the literature by combining FTA-BN as a dynamic risk assessment methodology to the improvement of STS operation.

Assessing the life cycle risks of offshore wind turbines with suction bucket foundations

The risk assessment of offshore wind turbines is increasingly significant due to the increase in accidents. However, the life cycle risk assessment of suction bucket foundations is challenging owing to the limited number of installations and lack of relevant data. In this study, an improved fuzzy fault tree analysis method (IFFTA) based on the analytic hierarchy process (AHP) is proposed to evaluate the life cycle risks of offshore wind turbines (OWTs) with suction bucket foundations. First, a fault tree analysis (FTA) model was employed to establish a risk index system for the suction bucket foundation. Then, the fuzzy set theory was performed to obtain the crisp probabilities of basic events, and AHP was adopted to determine the importance weights of basic events. Finally, the risk occurrence probability and risk level of OWTs with suction bucket foundations were determined, and the critical risk factors were identified. A case study demonstrated the applicability of the proposed method. The results show that the risk occurrence probabilities of OWTs with suction bucket foundations in the construction, service, survey and design periods are 3.25e-03, 4.47e-03, and 5.48e-04, respectively, and the corresponding risk levels according to DNV-RP-F107, one of the most authoritative recommended practices in the field of ocean engineering, are high, high, and medium respectively. The proposed method overcomes the limitations of traditional methods and provides some risk reduction measures for engineering references. • Improved fuzzy fault tree analysis is developed for the life cycle risk assessment of offshore wind turbines. • The risk factors of offshore wind turbines with suction bucket foundations are identified. • The critical risk factors are identified, and several risk reduction measures are recommended. • The economic advantages of offshore wind turbines with suction bucket foundations are studied.

Fuzzy fault tree analysis of chlorine gas release hazard in Chlor-Alkali industry using [formula omitted]-cut interval-based similarity aggregation method

In chemical industries, the release of toxic gases and chemicals is always a serious concern as it has the potential to harm human life and the environment, leading to significant losses. Risk assessment in chemical industries is essential to identify and prevent such type of unwanted hazards. Conventional fault tree analysis has been used effectively to identify the system failure causes and evaluate the system reliability, but it requires quantitative historical failure data of system components. In many real-world applications, it can be difficult to obtain precise and sufficient quantitative failure data of system components. This research paper proposes a novel fuzzy set theory-based fault tree analysis to evaluate system failure probability by utilizing the available qualitative data such as expert opinions/views when quantitative historical failure data of system components is unavailable or insufficient. To aggregate the different opinions of selected experts, a new similarity aggregation method is developed by using interval distance-based similarity measure function. The proposed fuzzy fault tree analysis applies system fault tree, α-cut intervals of fuzzy membership functions, and interval arithmetic operations. To validate the applicability and effectiveness of proposed approach, the basic event probabilities in fault tree analysis of chlorine release from storage and filling facility are generated using proposed approach and then compared with the known probabilities. Further, importance analysis is performed to evaluate the contribution of each basic event in top event “chlorine release” occurrence that would help the decision makers to identify the risk factor of chlorine release. The obtained results may be helpful for decision makers to decide whether and where to take protective actions in the risk management process.

Investigation of Loss of Feedwater (LOFW) Accident in the APR-1400 Using Fault Tree Analysis

Nuclear power plants play a significant role in the contribution of electricity generation on a global scale. Various reactor designs have advantages over others in different aspects. APR-1400 is a pressurized water reactor that is deemed safe due to the redundancy and independence of the multiple safety systems. Probabilistic safety assessment (PSA) is well known for its effectiveness in the representation of risk and safety analysis of the systems in a nuclear power plant. It provides different scenarios of system failure and accident progression via fault tree analysis. A loss of feedwater (LOFW) accident may occur due to numerous reasons such as spurious closure of valves, component failure of heaters, pumps, tanks, or a loss of offsite power (LOOP) event. In the present research, a methodology has been developed that aims to investigate different factors contributing to the loss of feedwater. This research also aims to analyze LOFW accidents by developing fault tree models for the main feedwater system of the APR-1400 to identify the basic events, which may lead to a loss of feedwater accidents. The results of the top event probabilities, risk decrease factor (RDF), risk increase factor (RIF), minimal cut sets (MCS), basic event probabilities, and sensitivity analysis were compared with the WASH-1400 database. It has been found that the control valve (V04) and main feedwater isolation valve (V05) have more contribution to the LOFW accident. The common cause failure (CCF) analysis has been carried out, and it was found that the flow toward the check valve and steam generator are most critical for CCF.

Reliability analysis for the power system of the nuclear ship Savannah

The power system, including the power transmission, distribution, and support system, is one of the largest and most complex systems established by humans. In addition to the normal considerations of power plants on board, the design of nuclear power merchant ships is also based on the need for uninterruptible power supplies. Based on the relevant data of the nuclear power ship “Savannah,” the qualitative and quantitative analysis is performed using the fault tree analysis method to analyze the reliability of its power system. The probability of basic events in certain accident conditions is evaluated according to the literature survey. In addition, this study also examines the common cause failure of diesel generators in the power system, as well as the analysis of sensitivity and importance of basic events. The installation of primary, auxiliary, and emergency power stations, the use of automated equipment for generator synchronization and power conversion, and the application of special internal communication and alarm systems unique to nuclear-powered ships can effectively eliminate the possibility of complete power outages for Savannah.