Reliability Of Critical Systems Research Articles

IoT-Powered UPS Battery Monitoring: Ensuring High availability and reliability for Critical Systems

An Uninterrupted Power Supply (UPS) is a critical component in any high availability system. However, the effectiveness of a UPS depends largely on its battery backup, which must be continuously monitored to ensure that it is working properly. In the past, this monitoring has been done manually or through local monitoring systems, but advances in IoT technology now make it possible to remotely monitor the status of UPS batteries and receive real-time alerts if any issues arise. The proposed system will have several benefits over existing battery monitoring systems. First, it will be fully automated, reducing the need for manual monitoring and minimizing the risk of human error. Second, it will be accessible remotely, allowing system administrators to monitor the status of the battery backup system from anywhere in the world. Finally, the system will be scalable, allowing additional sensors to be added to the network as needed. Nevertheless, physically checking the UPS battery is highly challenging since it requires more money and time. Data center operators, at the centre of the digital economy, are under pressure from several directions. sustaining the highest level of availability at the most affordable level. A leading provider of battery management solutions.

Reliability Analysis of Special Vehicle Critical System Using Discrete-Time Bayesian Network

The reliability assessment of special vehicles has become very important. However, due to the special structure of special vehicles, it is difficult to collect a large amount of experimental data. The use of traditional fault tree analysis cannot accurately assess product reliability. In this paper, dynamic fault trees are used to model the critical systems of special vehicles, and discrete Bayesian networks are used to evaluate the reliability of critical systems of special vehicles, which solved the problems of difficulty in accurately describing complex systems in the process of system reliability analysis and difficulty in obtaining accurate data in the process of analysis. Finally, through the combination of expert experience and the evaluation of the calculation results, the rationality of the method used in this paper in the reliability evaluation of special vehicles is verified.

Editorial: security and reliability of critical systems

Editorial: security and reliability of critical systems

System Safety Analysis for Critical System Applications Using Bayesian Networks

Safety Assessment (SA) is a well-established process for assuring the safety and reliability of critical systems. It uses probabilistic analysis to provide precise measures about the safety requirements of a system. This research work introduces SA into safety critical systems like unmanned aerial vehicles (UAV) that have prominent roles in both the commercial and military aerospace industries. The absence of thinking, observing, reacting and decision making pilot reduces the capability of UAVs to manage adverse situations such as faults and failures. There are number of various sensors in UAV, any major faults or failures in such critical sensors affect the total functionality of the system and hence will make the system unsafe. So for safety analysis of UAV here the functionality of different sensors is considered. The goal is to monitor the safety critical sensor outputs and ensure successful performance of UAV sensors using Bayesian Networks (BN), and this is implemented using the software BayesiaLab 5.4.3 DE. Simulation results comprise the influence analysis on the system safety by the sensor functionality modes and the total precision of the network using samples.

A conservative bound for the probability of failure of a 1-out-of-2 protection system with one hardware-only and one software-based protection train

Redundancy and diversity have long been used as means to obtain high reliability in critical systems. While it is easy to show that, say, a 1-out-of-2 diverse system will be more reliable than each of its two individual “trains”, assessing the actual reliability of such systems can be difficult because the trains cannot be assumed to fail independently. If we cannot claim independence of train failures, the computation of system reliability is difficult, because we would need to know the probability of failure on demand (pfd) for every possible demand. These are unlikely to be known in the case of software. Claims for software often concern its marginalpfd, i.e. average across all possible demands. In this paper we consider the case of a 1-out-of-2 safety protection system in which one train contains software (and hardware), and the other train contains only hardware equipment. We show that a useful upper (i.e. conservative) bound can be obtained for the system pfd using only the unconditional pfd for software together with information about the variation of hardware failure probability across demands, which is likely to be known or estimatable. The worst-case result is obtained by “allocating” software failure probability among demand “classes” so as to maximize system pfd.

A Program to Estimate Repairable Systems Reliability

The reliability of critical systems within an organization can dramatically impact many different aspects of its operations such as production Vols., labor requirements, and inventory levels. This article discusses the application of a widely used stoch..