Reliability Engineering & System Safety最新文献

Transmission lines (TL) are a very important part of our infrastructure. Their design is still mainly based on a single extreme value wind speed, evaluated from synoptic or mixed wind speed records, whereas non-synoptic (e.g. tornado and downbursts) winds are responsible for up to 80% of weather-related TL collapses. In this manuscript a methodology is proposed to evaluate the reliability of complete TL segments, considering the large uncertainties in wind speeds, tornado diameter and relative trajectory w.r.t. TL supports (tower offset). The Performance-Based Wind Engineering framework is employed to account for the uncertainties in wind speeds, tornado diameter and tower offset. A compact non-linear dynamic scheme is employed to handle the material and geometric non-linearities of a tower-cable TL segment, capturing the influence of cables in the dynamic response of the TL. Mean wind profiles and turbulent velocity field models are employed for simulating tornado loading in time domain. Fragility analysis is carried out for three performance levels (Serviceability, Damage control and Collapse). Results show that TL collapse is conditional on tower-hit events. The probability of a tower hit event is determined from geometrical relationships between tower span, tornado radius and tornado trajectory. The probability of a tower hit event increases significantly with tornado radius. Yet, uncertainty in tornado radius is found to be less relevant to TL vulnerability than uncertainty in wind speeds and tower offset.

This research presents a new approach to analyze the reliability of a ‘k-out-of-n’ system by calculating lower bounds for system reliability incorporating Jensen–Shannon divergence information. The system’s reliability is computed using a crossover polynomial function that connects subsystems, and the design life of subsystems is determined for a given mission time. A bi-objective redundancy reliability model is proposed to enhance the system’s reliability at the lowest cost. The model uses Jensen–Shannon lower bound scores and a non-dominated search procedure to produce optimal system reliability and cost. Two algorithms are presented — one for subsystem characteristic analysis and the other for producing optimal system reliability and cost. The proposed algorithm is demonstrated through numerical illustrations.

The gradual decrease capacity serves as a pivotal health indicator, reflecting the condition of lithium-ion batteries. Accurate forecasting of capacity can ascertain the remaining lifespan of these batteries at any given cycle, which is crucial for managing batteries in electric vehicles. This paper proposes an Autoregression with Exogenous Variables (AREV) model, which continually updates itself through a sliding window, offering predictions of battery state of health and remaining useful life, which extends battery prognostics at a fixed operating condition to different operating conditions. In addition, unlike most models that require multiple battery data of the same type for training, the proposed model only requires the use of fragmented data of the target battery with length around 30-50 cycles for capacity prediction and determines battery life based on battery failure thresholds. The above two points enable this model to be updated online without the need for any offline training. Finally, four different types of battery dataset , with different chemical substances and different charge and discharge conditions (especially dataset that follows random walk discharging profile to stimulate the real power consumption process) , are applied to verify the effectiveness and robustness of proposed RUL prediction approach. It shows that the proposed model can accurately predicting future capacity values. Timely warning signals can be issued before the end of life of battery, thereby ensuring the safe driving of electric vehicles and timely battery replacement.

For the $k$-out-of-$n$ system consisting of components that have different weights, the system is in a good state if the total weight of working components is at least $k$. Such a system is known to be weighted $k$-out-of- $n$:G system. Although the weighted $k$-out-of-$n$ system that has continuously distributed components’ lifetimes has been extensively studied, the discrete weighted $k$-out-of-$n$:G system has not been considered yet. The present paper fills this gap by modeling and analyzing the weighted $k$ -out-of-$n$:G system that consists of discretely distributed components’ lifetimes. In particular, the behavior of the total capacity/weight of the system with respect to the component failures is evaluated. An optimization problem that is concerned with the determination of optimal number of spare components is also formulated by utilizing the mean lost capacity of the system.

Recently, researchers have used the pairwise networks (lower-order) or hypergraphs (higher-order) to separately model different real systems. However, some real systems may simultaneously exist lower-order and higher-order relationships, for example, in scientist collaboration networks, there exist lower-order relationships, representing whether scientists know each other, as well as higher-order relationships, indicating shared research tasks among multiple scientists, such as co-authors of papers. In light of this, a hybrid hypergraph model that considers both lower-order and higher-order relationships is firstly considered in this work. And then, we focus on studying the robustness of hybrid hypergraphs owing to the importance of this issue. Specifically, we first introduce a collaboration efficiency (CE) metric to measure the robustness of hybrid hypergraphs. We further propose a novel edge removal strategy, termed EIHED (Edge-Included Hyperedge Efficiency Discrepancy), to study its impact on the robustness of hybrid hypergraphs. Finally, we further validate the effectiveness of our proposed strategy from both node attacking strategy and edge protection strategy.

The reliability and resilience of communication base stations are critical to the post-earthquake performance of the communication system, and consequently influence the communication, rescue, and emergence management after an earthquake. There is a lack of models that can fully evaluate the post-earthquake functional states of base stations with the consideration of the dependencies between different components. This paper proposes a Bayesian network method to evaluate the post-earthquake functionality of communication base stations. The method considers the dependence between the equipment and its hosting building structure, and the impact of power outages. This model produces seismic functional fragility curves for typical base stations that enable both qualitative and quantitative evaluations of base station functionality. The model is validated using seismic damage data from the Ludian Earthquake. It was found that the proposed model can reasonably predict the post-earthquake functional failure of base stations, in good agreement with the observed seismic damage data. The application of this model provides support for the operation and maintenance of communication base stations, and insights for managing seismic risk and resilience of the communication system.

Preventive maintenance of balanced systems has garnered increasing research attention due to its significance in production and servicing processes. This paper investigates the optimization problem of a condition-based maintenance policy for a two-component balanced system with dependent degradation processes. The system’s degradation follows a bivariate gamma process. Once the degradation level of one component exceeds a critical value, the system fails and triggers an immediate corrective replacement. Moreover, if the degradation difference between two components exceeds a threshold, the system is in an unbalanced state, which incurs a penalty cost. Periodic inspections are implemented to reveal the degradation of the system. At each inspection epoch, the decision-maker chooses an action among do-nothing, repair, and preventive replacement. Different from previous research, a repair action does not reduce the degradation levels of either component but increases the degradation level of the lower one to rebalance the system. The objective is to determine the optimal condition-based maintenance policy that minimizes the long-run average cost rate. The optimization problem is analyzed in a semi-Markov decision process framework. A policy-iteration dynamic programming algorithm is developed to derive the optimal policy. A numerical example is presented to illustrate the effectiveness of the proposed approach.

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

Ballistic design is crucial for widely utilised concrete structures in today’s unpredictable world. In the past, extensive experimental and numerical studies have investigated concrete panels, resulting in design guidelines and empirical formulations for local damage parameters. However, with the advent of performance-based design, notably in seismic design, the ballistic design of concrete structures lacks a comprehensive design philosophy. Additionally, deterministic empirical formulations for local damage parameters, particularly in the case of reinforced concrete (RC) panels’ crater formation, necessitate a probabilistic treatment. Consequently, this paper addresses the need for a well-defined ballistic design philosophy for concrete structures and introduces a probabilistic approach for crater quantification. Firstly, a novel multi-level and multi-parameter performance-based design framework for RC panels is proposed, centred on damage states, namely depth of penetration and crater area. This framework establishes an innovative design philosophy featuring four damage levels for each state. Secondly, using dimensionless explanatory functions, a Bayesian inference model is developed to estimate the crater diameter in RC panels under hard projectile impact, accounting for the associated uncertainties. LS-Dyna is used for numerical modelling of RC panels and rigid projectiles. The numerical models are validated with the experimental data and are utilised to develop the probabilistic model. This study reveals the profound influence of projectile and concrete properties in addition to the incidence angle of the projectile on crater formation. The developed probabilistic model is validated with experimental results demonstrating its reliability and accuracy. Consequently, a reliable design formula for estimating crater diameter for RC panels under hard projectile impact is proposed in addition to the novel ballistic design framework.

Metro stations are important infrastructures ensuring people's daily commuting with tremendous travels every day, but susceptible to disruptions posing challenges in the resilience of the metro system. Previous studies mainly contributed to the resilience of the whole metro network, neglecting the resilience methodology and analysis of important stations. Integrating the infrastructure characteristics and the evolvement of passengers, a metro station resilience analysis methodology is developed. The approach is demonstrated with the stations of the metro system of Xi'an, China. Results show that station resilience varies differently across stations dominated by infrastructure types and queuing delay. Based on the comparison analysis under different infrastructure failures, failures of auto-ticket gates would have the highest impact on station resilience. When incidents on one infrastructure last for a period of time over 15 min, the resilience of station would be reduced by 9.8%, and dramatic resilience reductions could be observed if there are short-time incidents within 5 min on more than five infrastructures of a station. The findings would have practical significance for the resilience improvement of metro stations in infrastructure planning and passenger flow control against metro incidents.