Explore the vast range of titles available.

The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.

Abstract In Takagi and Sugeno (T-S) fuzzy fault tree analysis (FFTA), the construction of T-S fuzzy gates relies too much on expert experience, which will result in inevitable subjective errors. In order to overcome this disadvantage, a new method was proposed in which the construction of T-S gates no longer relies solely on historical data and expert experience but is also determined by the importance of the basic events to the top event. In the proposed method, fault degrees were described as fuzzy numbers; fault probabilities were described as fuzzy possibilities. The importance index of basic events can be solved through the analysis of the fuzzy fault tree model by Monte Carlo (MC) simulation. The proposed method is suitable for systems where exact information on the fault probabilities of the components and the magnitude of failure and effect on the system are not available. The concept and calculation method of T-S probability importance was presented. Finally, the proposed method is applied to analyze the reliability of the NC turret seal subsystem, the accuracy of the method is verified by comparing with the methods based on traditional FFTA and T-S FFTA, and the weak points of the system are obtained by importance analysis, which will provide data for system fault diagnosis and preventive maintenance.

Safety has been a primary concern in every industry. It includes system, personnel, environmental safety, etc. Maritime transportation safety is of the utmost importance because a lot of economic and environmental damage has been caused by ship-related accidents. The majority of these accidents have resulted from human factors. For the analysis of accidents and future safety, various accident models have been created. In this study, human-based errors are analyzed and quantified by using the fuzzy fault tree analysis, which helps calculate the failure probability of the causes. A real-life case of a chemical tanker Key Bora was studied and analyzed, which happened on 28 March 2020, at Kyleakin Pier, Isle of Skye, Scotland. The ship’s hull was seriously damaged and was flooded. According to the analysis, two main human factors that contributed the most to the occurrence of this accident were found. These incidents can be avoided by ensuring proper measures are followed, and the results can be used as guidelines for future marine accident investigations and safety.

The stable operation of aerospace equipment is important for space safety, and the fault diagnosis of aerospace equipment is of practical significance. A fault diagnosis system needs to establish clear causal relationships and provide interpretable determination results. Fuzzy fault tree analysis (FFTA) is a flexible and powerful fault diagnosis method, which can deeply understand causes and fault mechanisms. The interval belief rule base (IBRB) can describe uncertainty. In this paper, an interpretable fault diagnosis model (FFDI) for aerospace equipment based on FFTA and the IBRB is presented for the first time. Firstly, the initial FFDI is constructed with the assistance of FFTA. Second, a model inference is implemented based on an evidential reasoning (ER) parsing algorithm. Then, a projection covariance matrix adaptive evolutionary strategy algorithm with an interpretability constraints (IP-CMA-ES) optimization algorithm is used for optimization. Finally, the effectiveness of the FFDI is verified by a flywheel dataset. This method ensures the completeness of the rule base and the interpretability of the model, avoids the problem of exploding certain combinations of rules, and is suitable for the fault diagnosis of aerospace equipment.

In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree.

This paper presents a risk assessment method for a ship’s power system under the conditions of an engine room fire based on the expert comprehensive evaluation (ECE) method combined with the fuzzy fault tree analysis (FFTA) method. The composition of the main engine system in the engine room and the failure logic of each subsystem were analyzed, and the fuzzy fault tree of a ship engine room fire was constructed. The probability of system failure and the importance of basic events were calculated. The fire safety risk assessment model was established using the safety risk matrix. The risk assessment of a ship engine room fire was implemented. The results demonstrated that the fire frequency of the ship engine room was 5.232 × 10−6 h−1. The fire risk of the main engine fuel system was the highest. Fuel leakages from diesel supply tanks and heavy fuel oil tanks are the main cause of accidents. The proposed method eliminated the influence of incomplete statistics in the risk assessment process and improved the accuracy and credibility of the reassessment results.

Fire and explosion accidents occur frequently in tankers because they transport large quantities of dangerous cargo. To prevent fire and explosion accidents, it is necessary to analyze factors that cause accidents and their effects. In this study, factors that cause fire and explosion accidents were classified using the 4M disaster analysis method, and each factor’s effect on the accident was analyzed using fault tree analysis (FTA). First, the unsafe tank atmosphere environment was identified as a primary cause of fire and explosion accidents in tankers, and the underlying causes of these accidents were investigated. The probability of underlying causes leading to primary causes was derived using an expert survey. The results showed that management and media factors had a greater impact on the unsafe tank atmosphere environment than human factors. To prevent fire and explosion accidents, it is necessary to ensure sufficient working and resting times for seafarers and compliance with procedures and work guidelines. A generalization of the results of present and future studies will enable the identification of the cause and preventive measures for fire and explosion accidents in tankers. Furthermore, this will reduce accidents in tankers and contribute to future safety management measures of ships and companies.

Maritime Autonomous Surface Ships (MASS) introduce new safety challenges associated with complex cyber–physical systems, distributed control architectures, and remote supervisory operation. Traditional maritime risk assessment approaches primarily focus on component failures and historical accident data and may therefore be insufficient for capturing interaction-driven hazards arising in autonomous vessel systems. This study develops a parallel and architecturally synchronized risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Fault Tree Analysis (FTA) for the safety assessment of MASS. Within the proposed framework, both analyses evolve concurrently within a shared system architecture, enabling explicit traceability between hazards, unsafe control actions, causal scenarios, failure events, and accident propagation pathways. The framework is demonstrated through a case study of a Degree of Autonomy 3 short-sea freight vessel operating in a high-density North Sea traffic environment. The integrated analysis identifies dominant accident pathways related to perception degradation, communication disturbance, authority coordination conflicts, maneuver execution deviations, and incorrect collision-risk assessment. The results illustrate how the framework supports structured safety assessment of MASS while preserving traceability between systemic control deficiencies and accident propagation mechanisms.

The working conditions of underground mining are complex and variable, and roof fall and rib spalling are one of the main types of accidents that can occur. Building an integrated model to evaluate the risk of roof fall and rib spalling is the foundation of mine safety. On the basis of the inherent attributes of event risk, the fuzzy evaluation set and probability of basic events are obtained by using the fuzzy fault tree analysis method based on the sample’s fuzzy information. Subsequently, the likelihood of roof fall and rib spalling is determined. Consequence severity data are obtained by using the dynamic fuzzy logic method, and the consequence severity grade of roof fall and rib spalling is evaluated via the dynamic fuzzy comprehensive evaluation method. The event risk level is determined by the risk matrix method. Roof fall and rib spalling in a non-coal mine is analyzed and evaluated by using fuzzy fault tree analysis and dynamic fuzzy comprehensive evaluation. The weak links in the operation of an underground mine are identified by fuzzy fault tree analysis as “mining process, roof management, support and reinforcement.” Then, the risk development trend is determined by the dynamic fuzzy comprehensive evaluation method. The risk matrix method is integrated to determine whether the risk level of the mine is “high risk, unacceptable” and expected to deteriorate in the future. The results show the validity and feasibility of the risk analysis and prediction model for roof fall and rib spalling.

Catalyst loss is a typical fault that impacts the long-term operation of the fluidized catalytic cracking (FCC) in the oil refining process. The FCC disengager is a critical place for separating the catalyst from oil gas. A fast and precise fault-cause judgment of catalyst loss is vital for avoiding catalyst loss failures. In this study, a novel fault judgment method of catalyst loss failures with quantitative criteria was established via the fault tree analysis (FTA) method, based on the relationship model between flow field signals and faults in the FCC disengager investigated by computational fluid dynamics (CFD). The FTA method defines three intermediate events: catalyst fragmentation, process fault and mechanical fault. In CFD results, it was found that the detailed fault reason can be inferred based on the changes in the characteristic parameters within the disengager. For example, when the catalyst loss rate of the FCC disengager may rapidly increase by a factor of around 200. Furthermore, the pressure drop of the cyclone separator decreases by around 35%, which indicates that the dipleg has fractured. The new fault judgment method has been applied in cases of catalyst loss in two industrial disengagers. The method accurately pinpointed the sudden reduction in inlet velocity and blockage fault at the cyclone separator as the main factors leading to catalyst loss faults, respectively. The judgment results are consistent with actual reasons, demonstrating the reliability of the method. This study could contribute to providing theoretical support and enhancing the accuracy for the diagnosis of catalyst loss faults, thereby ensuring the safe and stable operation of the FCC unit.