International Journal of Reliability, Risk and Safety: Theory and Application
Volume:6 Issue: 1, Jul 2023

The reliability analysis of multi-state phased mission systems (MS-PMS) is a crucial area of study in systems engineering and reliability engineering. An MS-PMS consists of multiple phases where the system can exist in different operational states in each phase. The system transitions from one phase to the next based on the success or failure of the current phase. The reliability of an MS-PMS depends on the reliabilities of each phase and the transition probabilities between system states across phases. By thoroughly analyzing the reliability of each phase and accurately estimating the probabilities of state transitions, the overall system reliability can be determined. Several methods are used for MS-PMS reliability analysis, such as Markov models, Universal Generating Function (UGF) technique, Petri nets, fault trees, etc. This study evaluated the reliability analysis of an MS-PMS with a combination of Markov and UGF techniques. This method is defined as a combined technique in the literature. The Markov modeling approach represents the system as a set of states with transitions between states based on the failure and repair of components. In addition, the UGF technique converts the Markov model into a set of algebraic equations that can be solved to obtain reliability metrics such as system availability, mean time to failure, etc. In this research, a three-phased multi-state repairable system was discussed. Transition diagrams were created based on components for all phases, and the resulting differential equations were solved. Then, the UGF method was applied according to the system structure of the phases, and the reliability metrics of the system were obtained.

Chlorine is a toxic and oxidizing gas used in Iran to purify drinking water, and failure to control the effects of a gas leak or tank explosion could cause irreparable harm to workers and residents around the station. There has been no research on this issue in the city of Abadan. This research investigated the release of chlorine gas from a 1000-liter tank at 1 tonne and 1.5 tonnes at the Abadan Chlorination Station. Aloha software was used to investigate the rate of diffusion, different risk areas, and the population at risk. The investigations show that the release of gas in the event of damage to the one-inch outlet valve of the tank can be lethal up to a radius of 2 km, effective up to a radius of 6.2 km, and felt up to 10 km. Considering the possibility of an accident and the location of the station in the direction of the wind, it is possible to harm a large number of citizens living within a 5 km radius of the station. Effective measures are therefore needed to inform the local population, raise awareness among employees and emergency services, install a suitable scrubber, and increase the safety level of the station.

Due to the importance of the fundamental role of turnouts in network operations and their higher vulnerability than other assets, turnout condition monitoring is necessary for reliability-centered maintenance. Along with periodic visual inspections, real-time infrastructure condition detection can help introduce the structure's performance so that infrastructure maintenance is more reliable. A new approach for railway turnout pass-by condition detection is provided based on statistical process control (SPC) of damage-sensitive features (DSF) using switchblade lateral displacement (BLD) measurements.  BLD time series data is modeled using a neural network model to extract DSF. This approach is applied to 33 passenger trains. The results of the proposed approach are validated by analysis of BLD and switch rod force sensor outputs. This method can be applied in turnout short-term condition monitoring for condition detection, leading to preventive maintenance, proper track operation management, and increased reliability.

Remaining useful life (RUL) prediction is crucial in prognostics and health management (PHM) systems. The primary objective is to forecast the time to failure (TTF) or anticipate the RUL of a system. In real industrial cases, systems typically consist of multiple components that can affect each other, and ignoring these dependencies when modeling PHM systems can lead to erroneous RUL predictions and ineffective maintenance planning. Recognizing this, the focus of this paper is on the prognostics of multi-component systems, where the degradation processes of the system are influenced by both internal factors specific to the components and external factors related to the environment.

The high risk of operational facilities and processes in strategic industries has made the continuous effort of operations managers to improve system reliability an undeniable necessity. The existing knowledge in fuzzy sets limits the sum of degrees of membership and non-membership of each element to more than one. However, in the real world, many ill-defined or highly complex situations require more consideration that is careful. Unlike normal fuzzy sets, the spherical fuzzy set pays attention to the degree of uncertainty of each element in decision-making situations and the degree of membership and non-membership. In addition, to help generalize the decision set, it considers the sum of squares of each membership function to be less than or equal to one. Achieving the success function is determined by maximizing the degree of membership and minimizing the degree of non-membership and uncertainty of each objective function in the spherical fuzzy set. Therefore, this paper develops a new algorithm based on the spherical fuzzy set called the spherical fuzzy geometric programming problem in system reliability. To evaluate the performance of the proposed algorithm, a descriptive example in the field of the rolling process of aluminum products is modeled in the form of a dual-objective problem, including maximization of reliability and minimization of cost.

Recently, reliability has become a critical criterion for product quality and decision-making that covers a wide range of subjects, including failure analysis systems. Performing a reliability analysis is essential for the study of operating safety in industrial systems. In this study, we list evaluation methods and perform real-time reliability analyses. The real-time reliability modeling of a Reverse Osmosis system is addressed in this paper. The model will help create effective maintenance while extending the subsystems' lifespan. To achieve our goal, we suggested the 2-parameter modified Weibull distribution. The simulation was performed using Maple software. The evaluation for each subsystem was displayed in the result and analyses section. The conclusion, however, draws a broad conclusion about the study.

Considering the nature of cyberspace as the main information base of the country and the possibility of harming it is very likely, it is necessary to take a special look at the issue of cyberspace security, especially at the level of national applications, because the main infrastructure of the country is located in this space. The emergence of any security problem will cause a serious threat to the national security of the country. Therefore, in this research, cyber defense in the social security organization was investigated by aiming to rank the factors affecting cyber security among the employees of the social security organization of Shiraz City in 2023, one of the country's largest organizations. The research findings indicated that six factors of budgeting and awareness, security behavior and understanding, employee position, capacity building, inefficient human resources, and information protection culture are effective on cyber security, among which the most important factors are inefficient human resources, Employee position, and information protection culture. Therefore, attention to these factors is recommended to the relevant officials, and it is suggested to hold training classes to inform and empower the personnel in this area.

The iHLRF algorithm is a popular iterative algorithm for determining the failure probability in structural reliability problems. It belongs to the family of first-order reliability methods (FORM) and is known for its fast convergence and remarkable simplicity. However, in cases where the limit state function oscillates significantly near the design point, which often occurs in high nonlinear limit state functions, the iHLRF algorithm may suffer from convergence issues. To address these convergence issues, this paper proposes three two-step direction determination techniques for first-order analysis. These techniques are based on two-step root-finding methods with a higher convergence rate than existing methods. The proposed techniques aim to improve the accuracy and robustness of the iHLRF algorithm, especially in cases where the limit state function shows highly nonlinear behavior. A numerical example with high nonlinear limit state functions in standard normal space is presented to demonstrate the proposed techniques' efficiency and capability. The performance of each proposed technique is compared with other existing methods, highlighting the advantages and limitations of each approach. Overall, this paper aims to contribute to developing more accurate and reliable methods for determining the reliability index in structural reliability problems, with the potential to be applied in various engineering fields.

This paper compares the traditional approach against reinforcement learning algorithms to find the optimal preventive maintenance policy for equipment composed of multi-non-identical components with different time-to-failure distributions. As an application, we used the data from military trucks, which consisted of multiple components with very different failure behavior, such as tires, transmissions, wheel rims, couplings, motors, brakes, steering wheels, and shifting gears. The literature proposes Four different strategies for preventive maintenance of these components. To find the optimal preventive manganocene policy, we used the traditional approach (renewal theory-based) and the conventional reinforcement learning algorithms and compared their performance. The main advantages of the latter approach are that, unlike the traditional approach, they are not required to estimate the model parameters (e.g., transition probabilities). Without any explicit mathematical formula, they converge to the optimal solution. Our results showed that the traditional approach works best when the component time-to-failure distributions are available. However, the reinforcement learning approach outperforms where no such information is available or the distributions are misspecified.

This study first discusses the importance of data collection and sensor placement in engineering. The Value of Information (VoI) method is introduced as a new approach for optimizing sensor placement. The decision-making theories, the VoI method, and its foundations are then explained. The application of this method for optimizing sensor placement is also described. Two case examples in the field of sensor placement in engineering are presented and analyzed. The first case involves determining the load-bearing status of land, the associated risks and costs, and the need to install piles. The second case involves monitoring the creep phenomenon in high-pressure vessels and pipes, where sensor placement is determined using the VoI method based on relevant risks. The results are compared with the UNI 11096 standard for pressure and high-temperature vessels.

This research has investigated the reliability of conventional three-level inverters. In recent years, many multi-level inverters have been introduced and developed. The most well-known of them are Neural Point Clamped (NPC), Floating Capacitor (FC), and Cascade H-Bridge (CHB). Through these structures, various types of multi-level inverters have been created, which are used to achieve higher efficiency, reduce the number of diodes, switches, and most importantly, increase reliability. Increasing reliability in aerospace systems is very important. In this paper, we will determine the reliability of conventional three-level inverters that used in the Hybrid Drone motor drive system. The result shows that the CHB inverter structure is more reliable than the other two types. On the other hand, in applications like hybrid Drone motor drives that use multiple energy sources, the use of the CHB structure provides greater flexibility in design and increases reliability.

In this article, risk management in the aerospace industry based on supply chain management has been discussed to reduce risks, human casualties and the safety of air operations. The aerospace industry operates in a high-risk and sensitive environment where safety and risk management are of great importance, so that any mistake or negligence can lead to an unfortunate disaster. This paper comprehensively analyzes risk assessment methods, risk mitigation strategies, risk communication practices and continuous improvement processes in the aerospace sector. This article uses relevant case studies and industry best practices to provide insights into effective risk management techniques specific to the aerospace industry. By examining these key aspects, this following article tries to provide a better risk management scheme and its critical role in ensuring safety in the aerospace industry.