Reliability Block Diagram Model Research Articles

An effective hybrid method for analysis the large-scale reliability block diagram model

The reliability block diagram (RBD) is a graphical tool used for reliability modeling and analysis in various industries, including shipbuilding, aviation, and aerospace. Typically, RBDs are transformed into Bayesian networks for quantitative analysis of systems. Bayesian networks are probabilistic graphical models that can capture the uncertainties and causal relationships in complex systems. They can provide various reliability metrics such as failure probability, mean time to failure, availability, etc. However, these techniques have several drawbacks, especially for large-scale models, such as being extremely time and memory-consuming. To address these issues, we propose a hybrid method for quantitative analysis of large-scale RBDs based on the structure identification approach and binary decision diagrams. Theoretical analysis and case verification demonstrate that the proposed method is significantly more efficient than the current one.

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.

Reliability, availability, and life-cycle cost (LCC) analysis of combined cooling, heating and power (CCHP) integration to data centers considering electricity and cooling supplies

The utilization of digital technology is increasing rapidly and it is causing a growth in the number of data centers around the world. Data centers provide essential services, making them known as mission-critical. Like other mission-critical facilities, data centers often use reliability and availability analysis to evaluate the level of safety of their operations. Reliable electricity and cooling supplies are vital for the data center’s operation, and almost all data centers rely on the power grid to feed their electrical and cooling systems. However, the power grid is considered an unreliable source, and power grid fragility is a raising concern among data center operators. An alternative to the power grid is a sustainable combined cooling, heating, and power (CCHP) system, which can supply continuous electricity and cooling to a data center while providing local energy generation, energy cost saving, and emission benefits. To understand the impact of a CCHP integration on the reliability and availability of a grid-fed data center, an up-to-date electrical topology design and reliability block diagram models were created that take into account both electricity and cooling supplies. The results showed that the overall system reliability of a grid-fed data center increased from 49.23% to 74.62% with a CCHP integration. In terms of availability, a grid-fed data center with 2N redundant topology achieved the industry-accepted high availability level of 0.99999, also known as 5-nines. Moreover, the use of CCHP at the grid-fed data center increased the availability from 5-nines to 7-nines for the overall system. Thus, CCHP integration to a grid-fed data center provided higher reliability and availability for the overall system for even 2N redundant topology data centers. Moreover, based on the life-cycle cost (LCC) analysis, integrating CCHP systems into data centers has a promising outcome. However, when conducting an LCC analysis, we recommend considering the realistic cost of downtime along with the reliability-related exposure to properly assess the viability of alternative energy systems by data center investors and operators.

Reliability Assessment of Double-Sided Linear Switched Reluctance Generator System Based on Hierarchical Markov Model

This paper presents a hierarchical Markov model to assess the reliability of a double-sided linear switched reluctance generator (DLSRG) system. The proposed reliability model can be applied to find the balance point of reliability assessment accuracy and model complexity. First, the dominant fault modes of the DLSRG system are highlighted to quantitatively obtain the operation states, survival or failure, with a novel failure criterion. Second, a hierarchical Markov model is adopted to build a systematic reliability model of the DLSRG system, wherein a state transition diagram can be built in the detailed description to constitute the graphical Markov reliability model. And then the solving process to get state probability matrix P ( t ) is intensively investigated. Notably, a reliability block diagram model is also built to compare with the hierarchical Markov model, whose results verify that the Markov model can capture the effect of fault-tolerance capability on system reliability. Moreover, it is expected that the one-level Markov model is the most suitable model for the DLSRG system for its ability of decreasing the model complexity and improving the assessment accuracy. Analytic investigation in conjunction with simulation and experimental measurements demonstrates the feasibility and validity of the proposed model.

A petri net based reliability block diagram model for category I medical devices reliability analysis

A petri net based reliability block diagram model for category I medical devices reliability analysis