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Persistent fault analysis is effective when a single element of the S-box is altered. However, most fault injection techniques tend to induce multiple faults. In such multiple-fault scenarios, existing key recovery methods either fail to identify the unique key or require high computational complexity. To address this, we propose the count-ranking method to accelerate Persistent-Fault Based Differential Analysis (PFDA). The method counts subkey byte frequencies and selects the most frequent value per byte. Additionally, this approach allows us to further explore faults occurring during deeper rounds of encryption, as it independently determines the unique value of each subkey byte. Consequently, we successfully recover the unique key for both serial and parallel implementations of AES with a complexity of O ( N f 2 × N c ) table lookups. Finally, the count-ranking method facilitates the recovery of several subkeys in Feistel ciphers without dramatically increasing the number of key candidates. We apply the revised PFDA to both DES and Camellia, efficiently achieving full-key recovery. These results demonstrate the practical applicability of the extended PFDA across both serial and parallel cipher architectures.

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.

A serious accident might occur as a direct result of a defective component or system, especially at an automated vehicle. The objective of this study is to establish a fault analysis methodology that can effectively improve the reliability of electric and automated vehicles. To achieve this, We proposed a method for identifying potential faults by combining Failure Modes and Effects Analysis (FMEA) with Fault Tree Analysis (FTA), creating a database of possible (example) faults that maps the causal relationship between causes, symptoms and faults, which enables more thorough fault analysis and serves as the foundation for further study. Using the fault database, we demonstrate a practical application involving fault injection and simulation, which can provide a more intuitive and practical representation of the effects of faults. The methodology is validated with the demonstrator vehicle from the joint project. This approach is scalable and can also be well applied to other electric automated vehicles with similar structure, providing a reliable tool to the system fault analysis for future work.

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.

This study describes the Computing Platforms (CPs) and the hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission and safety-critical applications demanding a failure-free operation. While the rapid development of the UAV technologies was recently reviewed by survey reports focusing on the architecture, cost, energy efficiency, communication, and civil application aspects, the computing platforms’ reliability perspective was overlooked. Moreover, due to the rising complexity and diversity of today’s UAV CPs, their reliability is becoming a prominent issue demanding up-to-date solutions tailored to the UAV specifics. The objective of this work is to address this gap, focusing on the hardware reliability aspect. This research studies the UAV CPs deployed for representative applications, specific fault and failure modes, and existing approaches for reliability assessment and enhancement in CPs for failure-free UAV operation. This study indicates how faults and failures occur in the various system layers of UAVs and analyzes open challenges. We advocate a concept of a cross-layer reliability model tailored to UAVs’ onboard intelligence and identify directions for future research in this area.

基于持久性故障的碰撞攻击可以有效恢复 AES 加密/解密模块 (基于 S 盒实现) 中使用的密钥. 现实中处理长消息需要调用相应的工作模式, 不能满足基于持久性故障的碰撞攻击的前提假设. 此外, 广泛应用的开源密码库 OpenSSL中 AES 密码模块采用多个 T 盒而非 S 盒实现, 导致已有的持久性故障注入模式失效. 本文针对 OpenSSL 中的不同工作模式分别研究.对于 ECB 模式, 通过分别在 T 盒注入置零故障或随机故障, 分别攻击 ECB 模式加/解密实现; 对于 CBC 模式, 通过挑战密文的方式攻击 CBC 模式的解密实现, 从而避免加密时输入随机初始向量对中间值的干扰; 对于 OFB 和 CFB 这种不直接操作消息的模式, 通过挑战密文的方式仍能成功攻击. 本文还证明了获得加密模块 (或解密模块) 的直接输出并非持久性故障碰撞攻击的必要条件, 并通过对 CMAC 的分析验证了只要可以观测到中间状态的碰撞信息, 就可以恢复密钥. 通过在 PC 上仿真注入故障, 针对上述工作模式实施密钥恢复实验, 表明不论是单字节故障还是多字节故障, 攻击成功率都为 100%.

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.

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.

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.