The Sichuan–Tibet Railway is facing extraordinary challenges in terms of construction, operation, and maintenance because of its extremely complicated natural environment and geological conditions. Consequently, countermeasures are necessary and urgent to ensure its safety and reliability in the whole life cycle. This study proposes a novel reliability framework to guarantee the ideal operation state of the Sichuan–Tibet Railway. Reliability application in many fields are summarized, including military equipment, rail locomotive, and railway engineering. Given the fact that the Sichuan–Tibet Railway is a complex giant system, Nine-Connotation was summarized (i.e., safety, inherent reliability, testability, maintainability, supportability, environmental adaptability, predictability, resilience, and durability) under the goal of optimizing the operational efficiency. On the basis of the concept of the Nine-Connotation and the understanding of reliability transmission mechanism, the framework of reliability for the Sichuan–Tibet Railway was established, which can facilitate a comprehensive and real-time evaluation of all situations with a clear hierarchy. The proposed framework is composed of a resilience management system, an integrated technology system, and a dynamic reliability assessment system. The pathway for its application on railway construction was developed in this study. The proposed framework can assist in well-informed decisions for the construction, as well as the operation of the Sichuan–Tibet Railway. On the basis of a top–down design concept for the first time, this study emphasizes the railway’s availability and validity to complete the assigned tasks as a whole, that is, operational efficiency. It also shows the reliability transmission and control mechanism of the railway’s giant complex system, innovating and establishing the management principle of great safety and great reliability over the life cycle.
This study provides a systematic overview of the advent and evolution of reliability systems engineering (RSE) in China, and the latest RSE development, that is, model-based RSE (MBRSE), is emphatically introduced. The establishment of the system architecture and conceptual models of MBRSE is first described. The fundamental theory and methodology of MBRSE are then elaborated, with a V-model as the core of this approach. The development of various MBRSE platforms and the effectiveness of their implementation over the past 30 years are presented. The prospective trends in the development of RSE in China are outlined.
The Cyber–Physical Power System (CPPS) is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development. In recent years, resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs. Accordingly, the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study. Then, a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided. On the basis of these assessment measures, the optimization methods of CPPS resilience are reviewed from three perspectives, which are mainly focused on the current research, namely, optimizing the recovery sequence of components, identifying and protecting critical nodes, and enhancing the coupling patterns between physical and cyber networks. The recent advances in modeling methods for cascading failures within the CPPS, which is the theoretical foundation for the resilience assessment and optimization research of CPPSs, are also presented. Lastly, the challenges and future research directions for resilience optimizing of CPPSs are discussed.
Burn-in has been proven effective in identifying and removing defective products before they are delivered to customers. Most existing burn-in models adopt a one-shot scheme, which may not be sufficient enough for identification. Borrowing the idea from sequential inspections for remaining useful life prediction and accelerated lifetime test, this study proposes a sequential degradation-based burn-in model with multiple periodic inspections. At each inspection epoch, the posterior probability that a product belongs to a normal one is updated with the inspected degradation level. Based on the degradation level and the updated posterior probability, a product can be disposed, put into field use, or kept in the test till the next inspection epoch. We cast the problem into a partially observed Markov decision process to minimize the expected total burn-in cost of a product, and derive some interesting structures of the optimal policy. Then, algorithms are provided to find the joint optimal inspection period and number of inspections in steps. A numerical study is also provided to illustrate the effectiveness of our proposed model.
Power grids deliver energy, and telecommunication networks transmit information. These two facilities are critical to human society. In this study, we conduct a comprehensive overview of the development of reliability metrics for power grids and telecommunication networks. The main purpose of this review is to promote and support the formulation of communication network reliability metrics with reference to the development of power grid reliability. We classify the metrics of power grid into the reliability of power distribution and generation/transmission and the metrics of telecommunication network into connectivity-based, performance-based, and state-based metrics. Then, we exhibit and discuss the difference between the situations of the reliability metrics of the two systems. To conclude this study, we conceive a few topics for future research and development for telecommunication network reliability metrics.
Given the complexity of power grids, the failure of any component may cause large-scale economic losses. Consequently, the quick recovery of power grids after disasters has become a new research direction. Considering the severity of power grid disasters, an improved power grid resilience measure and its corresponding importance measures are proposed. The recovery priority of failed components after a disaster is determined according to the influence of the failed components on the power grid resilience. Finally, based on the data from the 2019 Power Yearbook of each city in Shandong Province, China, the power grid resilience after a disaster is analyzed for two situations, namely, partial components failure and failure of all components. Result shows that the recovery priorities of components with different importance measures vary. The resilience evaluations under different repair conditions prove the feasibility of the proposed method.
System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio–temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.
The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.
Safety is one of the most critical themes in any large-scale railway construction project. Recognizing the importance of safety in railway engineering, practitioners and researchers have proposed various standards and procedures to ensure safety in construction activities. In this study, we first review four critical research areas of risk warning technologies and emergency response mechanisms in railway construction, namely, (i) risk identification methods of large-scale railway construction projects, (ii) risk management of large-scale railway construction, (iii) emergency response planning and management, and (iv) emergency response and rescue mechanisms. After reviewing the existing studies, we present four corresponding research areas and recommendations on the Sichuan–Tibet Railway construction. This study aims to inject new significant theoretical elements into the decision-making process and construction of this railway project in China.
Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit systems. Different from most existing studies that assume deterministic parameters as model inputs, this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling parameters. Specifically, we first construct a scenario-based position–time–speed (PTS) network by considering resistance parameters as discrete scenario-based random variables. Then, a percentile reliability model is proposed to generate a robust train speed profile, by which the scenario-based energy consumption is less than the model objective value at
