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Objectives: As part in appraising the usefulness of the new bite-type method, a reliability assessment of the classification system was performed. Materials and Methods: Eighty-five selected dental casts were appraised by six operators and compared to a “Gold Standard.” The reliability of the bite-type method was assessed by double registrations of each of the nine bite-type classes by every operator. Results: An overall acceptable bite-type classification of all examiners existed. Correct assessments were found in 75–90% of the dental casts. Thus, the bite-type classification is a reliable method for categorizing sagittal malocclusions. Conclusion: The procedure could favorably replace the angle classification as it is more precise and detailed, without losing the strength of the basic angle system as an uncomplicated communication tool.

Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by DTR. Energy dispatch and/or load curtailment must be scheduled before the actual energy delivery, thus DTR and load should be predicted prior the actual transformer loading. Since both are random variables, this problem is prone to be addressed by stress-strength analysis. In this paper, the novel comprehensive probabilistic tool “SmarTrafo” is presented. It allows predicting the probability of the DTR to be greater than the load (i.e., the probability of success) through an exact analytic stress-strength model, and formulating an alarm-setting strategy in order to establish a warning if the expected probability is greater than a threshold. Specifically, the threshold is optimized in a multi-objective formulation, exploiting three different indices which differently evaluate the predictive skill of alarm-setting strategy. Numerical experiments based on actual data confirm the suitability of the proposal in predicting the probability of success and in establishing high-performance alarms based on such predictions.

The reliability of an Engine Electronic Controller (EEC) attracts attention, which has a critical impact on aircraft engine safety. Reliability assessment is an important part of the design phase. However, the complex composition of EEC and the characteristic of the Phased-Mission System (PMS) lead to the difficulty of assessment. This paper puts forward an advanced approach, considering the complex products and uncertain mission profiles to evaluate the Mean Time Between Failures (MTBF) in the design phase. The failure mechanisms of complex components are deduced by Bayesian Deep Learning (BDL) intelligent algorithm. And copious samples of reliability simulation are solved by cloud computing technology. Based on the result of BDL and cloud computing, simulations are conducted with the Physics of Failure (PoF) theory and Failure Behavior Model (FBM). This reliability assessment approach can evaluate MTBF of electronic products without reference to physical tests. Finally, an EEC is applied to verify the effectiveness and accuracy of the method.

Introduction. At present, numerical methods enjoy widespread use in construction practice. They enable performing and analyzing complex non-linear, multi-factor models without excessive analytical procedures. However, as a rule, the most complex tasks, performed in a three-dimensional setting with account taken of physical, geometric and other nonlinearities, are performed in deterministic formulations without the analysis of the stochastic nature of physical processes. This seems particularly strange, given that numerical methods are well-suited for modeling stochastic processes. Numerical probabilistic and statistical approaches (PSA) can be applied to simulate and take into consideration various spatiotemporal aspects of the probabilistic nature of loads and forces, structural system resistances, materials and geological terrains. Even the most advanced numerical models of deterministic physical systems are merely a specific case of probabilistic and statistical modeling: they enable obtaining only one value (point) on the whole field of possible implementations, being unable to demonstrate an objective and exhaustive variety of probable outcomes. This article presents a case study of numerical probabilistic and statistical analyses of loads and forces. Methods of research. Materials from different sources, such as reference books, regulatory documents, laboratory test results, as well as available experimental data, were used as input parameters. The principal calculation and analysis of the integral function of loads was performed using the Monte Carlo numerical method of probabilistic and statistical modeling and various theoretical (statistical) and empirical distributions, followed by the quantitative assessment of design loads at various confidence probability values. Results. This study provides an example of the probabilistic and statistical calculation (determination) of the integral function of loads and forces with account taken of different origins of loads and varied input parameter distribution patterns, including empirical distributions. It has proven great importance of accurate description of initial distributions of a random value for the determination of reliable design load values. Conclusions. Probabilistic and statistical approaches have the ability to objectively assess the performance of structural systems based on the quantitative assessment of the probabilistic nature of load factors. These approaches have huge potential for increasing the reliability of buildings and structures and the cost effectiveness of construction projects.

A space station is a typical phased-mission system, and assessing its reliability during its configuration is an important engineering action. Traditional methods usually require extensive data to carry out a layered reliability assessment from components to the system. These methods suffer from lack of sufficient test data, and the assessment process becomes very difficult, especially in the early stage of the configuration. This paper proposes a reliability assessment method for the space station configuration mission, using multi-layer and multi-type risks. Firstly, the risk layer and the risk type for the space station configuration are defined and identified. Then, the key configuration risks are identified comprehensively, considering their occurrence likelihood and consequence severity. High load risks are identified through risk propagation feature analysis. Finally, the configuration reliability model is built and the state probabilities are computed, based on the probabilistic risk propagation assessment (PRPA) method using the assessment probability data. Two issues are addressed in this paper: (1) how to build the configuration reliability model with three layers and four types of risks in the early stage of the configuration, (2) how to quantitatively assess the configuration mission reliability using data from the existing operational database and data describing the propagation features. The proposed method could be a useful tool for the complex aerospace system reliability assessment in the early stage.

Integration of non-conventional renewables such as wind and solar to the power system may affect the system reliability, especially when the proportion of renewable power in the system is large. Therefore, with a significant level of renewable penetration, the intermittency and both diurnal and seasonal variations of renewable power generation should be deliberately modeled in order to accurately quantify the power system reliability. This paper presents a novel method based on Kernel Density Estimation (KDE) for modeling intermittency and both diurnal and seasonal variations of wind and solar power generation using historical renewable power generation data. The proposed KDE based renewable power models are used with non-sequential Monte Carlo simulation to evaluate the generation system adequacy. Several case studies are conducted on IEEE reliability test system to analyze the impact of increasing renewables on the generation system adequacy. The results show that the generation system adequacy tends to decay exponentially when the renewable integration is increased. It is shown that the reliability values obtained using the proposed approach are very close to those provided by the time-consuming sequential simulations. Importance of modeling seasonal variations of wind and solar is also investigated.

With the high integration of power electronic technologies in microgrids, the reliability assessment considering power electronic devices has become a hot topic. However, so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the microgrid. This paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources (RESs). In addition, to improve the reliability of islanded microgrid, the conventional battery energy storage system (BESS) is replaced by the hybrid energy storage system (HESS). Based on the proposed model, the operation failure models for the power electronic modules in microgrid are built and tested, and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

In order to cooperate the wind farm operators with grasping the operation status of wind power converter, a novel reliability assessment strategy is proposed based on supervisory control and data acquisition (SCADA) multistate parameters prediction of permanent magnet synchronous generator (PMSG) wind turbine. The strategy considers “off-line training, on-line matching and assessment”. The operation reliability of wind power converter is obtained via the analysis and weight computing of confidence level, prediction value and actual value of SCADA multistate parameters. In the “off-line training” part, first, the FP-Growth association algorithm is employed to analyze the confidence levels of SCADA variables to the faults of wind power converter. The variables with high confidence level are defined as SCADA multistate parameters. Afterwards, wavelet packet transform (WPT) and K-means algorithm are employed to decompose, reconstruct, normalize and cluster the time series (off-line data) of multistate parameters under normal operation of wind turbine, to improve the generalization capability of long short term memory (LSTM) prediction model. In the part of “on-line matching and assessment”, the actual value time series (on-line data) of multistate parameters are decomposed and reconstructed by WPT. Each normalized sample is matched to the closest cluster centroid, which is generated in the part of “off-line training”. Then the corresponding LSTM model is conducted to predict based on the each sample. The final prediction value is sum of prediction results of entire samples in clusters. Finally, the reliability of wind power converter is assessed by the proposed strategy. The effectiveness of proposed assessment strategy is verified by the experimental results on a PMSG wind power converter.

The integration of large-scale renewable energy significantly increases the computation time of reliability assessment and tracing. To solve this problem, the traditional methods cluster the scenarios directly based on renewable energy data. However, it could lead to errors in reliability assessment due to scenarios with similar risks. In this paper, a multi-scenario risk-oriented clustering algorithm considering renewable energy is proposed. The enumeration method is used to calculate the risk for different scenarios. According to the risk of each scenario, the Fuzzy C-means clustering method is adopted to cluster the scenarios, which maximizes the similarity of scenarios in the same cluster. The high-risk scenarios that contribute more to the reliability index are retained. The system reliability assessment and tracing are conducted based on the clustered scenarios. Case studies on the IEEE RBTS-6 and RTS-79 systems verify the accuracy and efficiency of the proposed method.

To address the problem that high-reliability and long-life complex products are susceptible to coupled fault mechanisms, and to address the challenge of constructing a macro fault behavior model for reliability evaluation, the reliability of a smart meter based on a Phase-Type (PH) distribution under a degradation-shock load is examined. This reliability assessment method considers the impacts of environmental stress and lightning shock on a smart meter. The performance degradation process caused by environmental stress is described by a Wiener model, which is converted into a representation of a PH distribution in a subsequent step. A lightning impulse can cause performance degradation or direct failure of a smart meter. The arrival time of the lightning, the magnitude of the lightning, and the damage caused by the lightning are analyzed separately, and then, the obtained model is expressed in the form of a PH distribution. Finally, a reliability evaluation model of a smart meter based on the PH model under a degradation-shock load is established. The accuracy of the model is verified by comparing the model results with Monte Carlo simulation results.

This work deals with the reliability assessment of a tanker ship hull structure subjected to a vertical bending moment and corrosion degradation. The progressive collapse and ultimate load carrying capacity are estimated based on experimentally tested scaled box-shaped-specimens. The translation of the strength estimate of the scaled specimen to the real tanker ship hull structure is performed based on the dimensional theory developing a step-wise linear stress-strain relationship. The load-carrying capacity is considered as a stochastic variable, and the uncertainties resulted from the scaled-specimen to the real-structure strength translation, and the subjected load of the real ship are also accounted for. A sensitivity analysis concerning the stochastic variables, included in the ultimate limit state function is performed. The partial safety factors, in the case of a scaled specimen and real structure, are also identified, and conclusions are derived.

One of the most important technical features of a power system is its ability to deliver electricity reliably to the customers. Based on interviews with 12 energy service companies (ESCO) currently operating solar micro-grids in northern rural India, this study identified important factors related to technical design, customer behaviour and operations and management (O&M) that may result in contingencies in service. In addition, the study presents companies' innovative solutions to overcome these problems. Initially, the interview method allowed only a rough qualitative comparison of different reliability levels as the availability of comparable data was limited. We found that a more descriptive method for reliability assessment would create equally valuable information on renewable off-grid energy projects. We propose a simple framework for assessing reliability that highlights the particular features of off-grid areas in developing countries.

With the ever-increasing penetration of renewable resources, more complexities and uncertainties are introduced in power system reliability assessment. This entails an enormous number of contingency states to represent the characteristics of renewable energy. As a result, the unbearable computation burden is the main challenge toward the efficiency of the state enumeration (SE) method. To address that, this paper proposes an improved reliability evaluation approach that can not only increase the accuracy but also accelerate the analysis. In detail, the impact increment method is first employed to decrease the proportion of higher-order contingency states, leading to accuracy improvement. Then, the shadow price is used to solve the optimal power flow (OPF) problem in a faster manner. This shadow price (SP) method allows us to obtain the optimal load curtailment directly from linear functions rather than the time-consuming OPF algorithms. In addition, one hundred percent criterion is used to match shadow-price-based linear functions with system states. Case studies are performed on the RTS-79 system and IEEE 118-bus system, in which test scenarios include loads, photovoltaics (PV), and wind turbines (WT). Results indicate that the proposed method can significantly ease the computation burden and outperform traditional reliability assessment methods in terms of both computing time and accuracy.

This paper proposes a comprehensive methodological framework to investigate the potential role of the grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) capability in improving the reliability of supply in future distribution networks. For this aim, we first develop an empirical model for describing the energy demand of electric vehicles (EVs) and their resultant available generation capacity (AGC) that can be utilized for BSS operation. Then, on this basis, a quantitative method to quantify the effect of grid-connected BSS on distribution system reliability is proposed. In order to capture the uncertainties associated with EV users&rsquo, behaviors, Latin Hypercube Sampling (LHS) methods were utilized to obtain the time series of the BSS traffic flow and initial State of Charge (SOC) of each EV battery, according to the probability distribution of corresponding uncertain factors whose statistics are obtained from real-world historical data. Compared with existing works in this research field, the main contributions of this paper are threefold. (i) A comprehensive and efficient method to assess the reliability benefits of BSS with an explicit consideration of BSS characteristics (including physical structure, charging strategy, and swapping model) is proposed, which is in contrast to most of the extant studies that only focus on the EV fast-charging paradigm and thus provide a practical tool to analyze the potential value of BSS resources in future distribution systems. (ii) The randomness of EV user behaviors in BSS operation is explicitly modeled and considered. (iii) The LHS-based sequential simulation is used to improve the accuracy and convergence performance of the evaluation, as compared to the traditional Sequential Monte Carlo Simulation (SMCS) method. To verify the effectiveness of the proposed approach, numerical studies are conducted based on a modified IEEE 33-bus distribution network. The simulation results show that with V2G capabilities, BSS can improve reliability to a certain extent and reduce the adverse impact on the reliability of the distribution network. In addition, EV resources should be orderly managed and exploited, otherwise, uncoordinated charging activities could impose a negative impact on the reliability performance of distribution networks. Finally, it is also shown that under the same sampling time, LHS-based sequential simulation could be better than SMCS in the accuracy and convergence speed of the procedure.

The estimation of lithium-ion batteries degradation plays an important role for the correct operation of smart grid and electric vehicle applications. In fact, only healthy battery energy storage systems meet minimum performance in terms of supplied voltage and power. Health prognostic is mandatory to ensure safe and reliable operation of batteries, as unsuccessful operation may cause technical/economic detriments or complete failures. The battery remaining useful life (RUL) depends on several random factors and thus it should be probabilistically characterized to allow the application of decision-making processes. In this article, a hybrid methodology is developed to characterize the RUL even with small amount of data, in view of the typical scarcity of data for adequately fitting the RUL probability distributions. Several distributions are considered in the methodology and compared for such purpose: the inverse Burr (IB) distribution, and mixtures of IB with inverse Gaussian and with inverse Weibull distributions. Expectation-maximization algorithms are specifically developed for the parameter estimation of the considered mixture distributions in the proposed methodology. The IB-based distributions are applied on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model fitted upon given charge/discharge cycles. Numerical experiments are reported to assess the proposal with respect to benchmark distributions.

Recently, the AC/DC hybrid distribution network has become a general trend in energy research. To accommodate the addition of renewable energy, this paper proposes a reliability assessment approach for AC/DC hybrid distribution network considering the new features introduced by the DC distribution technique. First, the probability models of renewable generations and loads are established. Second, the control modes of the voltage source converter (VSC) yielding to different fault scenarios are modeled based on its operating characteristics. Thereafter, an optimal load shedding model that considers the control modes of the VSC is developed, and it can be solved using a second-order cone algorithm. Finally, a reliability assessment method for the AC/DC hybrid distribution network is proposed that is based on the nonsequential Monte Carlo method. Case studies based on a modified AC/DC distribution network are applied to verify the effectiveness of the proposed method. The operational characteristics and reliability performance of the AC/DC distribution network with high integration of renewable energy are demonstrated by comparing it to a corresponding AC distribution network.

As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.

The Hoek–Brown empirical formulas are widely used to estimate the mechanical parameters of a rock mass. However, there exists a problem of variability and uncertainty in the mechanical parameters of a rock mass estimated by the Hoek–Brown empirical formulas. To do this, we present a method to implement a reliability analysis of the rock mass stability directly starting with the basic variables of the Hoek–Brown empirical formulas. First, a quantitative assessment of the disturbance factor is recommended to overcome the subjectivity and limitation of estimating the disturbance factor according to the guidelines by Hoek et al. Second, a performance function is built up together with the safety factor of a micro-unit. Third, the Rosenblueth point estimate method is chosen to estimate the mean and standard deviation of the factor of safety. Finally, the stability reliability of a cutting slope in Laohuzui Hydropower Station in Tibet of China is analyzed. The results of the cutting slope show good agreement with the rock mass failure that occurred.

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

Bridges are critical for transportation networks. Temporary closures due to poor maintenance may trigger adverse cascading events, affecting economic and societal well-being. Two main factors play a key role in bridge health conditions: ageing and wear-and-tear due to the increasing traffic. This paper proposes a comprehensive framework to quantify the combined phenomena in a holistic approach. The reliability assessment of an existing reinforced concrete bridge subjected to increasing traffic demand and spatially-variable pitting corrosion is investigated. Empirical data are used to develop probabilistic models for cracking initiation, pitting factor, severe cracking, and cover spalling. Statistical distributions of temperatures from a local meteorological station are used to investigate environmental effects on corrosion initiation. For the traffic, national highway databases are used to model the vehicular flow. Ductile and brittle failure mechanics are considered for the structural capacity assessment. Coupled biaxial bending-axial loading domain is adopted for the ductile structural checks. The shear capacity is assessed through the response limit surface from the modified compression field theory results. Finally, Monte Carlo simulations are performed at intervals of 10 years to derive a time-dependent reliability profile compared against standard thresholds to determine the health conditions of the bridge.

The representation of the strong wind climate and the reliability calibration of wind loading are the main components of the provisions for local conditions in the South African wind load standard. The reliability implications of new probability models for wind load and an updated map of the characteristic wind speed are assessed in this paper. Wind load probability distributions are based on a combination of new models for free-field wind pressure, pressure coefficients and terrain factors with existing information. The results from both assessments show that the wind load partial factor of 1.3 does not result in adequate reliability performance for typical design situations. A partial factor of 1.6 is recommended. Where an increased partial factor for wind loading will result in a corresponding increase in the design wind load, the introduction of an updated map of the characteristic wind speed for South Africa is shown to result in an overall reduction of wind load. Regional analysis, however, indicates that there are regions in the Western and Eastern Cape that will have increased design wind loads. Combined with an updated partial factor of 1.6, the aggregate increase in design wind loads across South Africa is shown to be 11%.

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man–machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors. Keywords: human factors, human error probability, maintenance operation, marine system, reliability assessment

Cloud manufacturing (CMfg) is an innovative manufacturing paradigm in which geographically distributed various manufacturing resources are encapsulated into manufacturing services (MSs), and also these MSs can provide users with various services. Users can find and choose qualified MSs to form cliques to help them complete their manufacturing tasks via Internet. Trust makes cooperative endeavors happen. Hence, it is an important problem to evaluate the trustworthiness of MSs in CMfg. In this paper, we present a comprehensive assessment approach to evaluate the trustworthiness of a MS in CMfg. The approach evaluates a MS from both objective and subjective perspectives, named as credit assessment and reliability assessment, respectively. Multinomial logistic regression analysis is presented to assess the credit of a MS, while fuzzy analytic hierarchy process is used to assess its reliability. Finally, a case study is presented and the results show the comprehensive assessment approach is efficacious at the trustworthiness evaluation of a MS.

The effect of dynamic soil properties and frequency content of harmonic excitation on the internal stability of reinforced soil retaining structure is investigated. Arc of a log-spiral is considered as the failure surface in the present limit equilibrium analysis. Backfill and reinforced soil is modeled as a viscoelastic material. The whole structure is considered to be resting on a rigid stratum. Backfill soil and the reinforced soil retaining structure are subjected to harmonic shaking at the base. Present methodology satisfies the stress boundary condition at the ground surface. In the present study, amplitude and phase of the horizontal and vertical seismic accelerations change with depth and the variation of accelerations along the depth is found to be time dependent and nonlinear. All the four possible combinations of horizontal and vertical seismic inertia force directions are considered to determine the total reinforcement force and critical length of the reinforcement. In the present study, amplification of accelerations towards the ground surface depends on the dynamic soil properties and frequency content of input excitation. Detailed parametric study is done to understand their implications on the solution. An algorithm is proposed at the end of this paper which uses strain dependent equivalent linear values of shear wave velocity (V-s) and damping ratio (xi) to compute the total reinforcement force and critical length of the reinforcement. The limitation of equivalent linear based approach is that it only considers vertically propagating shear wave. Comparison of present method with other theories is also presented showing the merit of the present study. (C) 2017 Elsevier Ltd. All rights, reserved.

U radu se prikazuje primjena kriterija temeljenog na svojstvu entropije za ocjenjivanje pouzdanosti vodoopskrbne mreže. Pri tome se u obzir uzimaju projektni i hidraulički parametri te vjerojatnost potpunog ispada iz rada (otkazivanja) cjevovoda. Na temelju odnosa ukupnog izlaznog protoka iz pojedinog čvora i ukupnog gubitka snage, u čitavoj mreži definiran je novi ponderski faktor. Definiran je i dodatni ponderski faktor koji se temelji na odnosu protoka u pojedinoj dionici i ukupnom protoku u mreži, pri čemu se u obzir uzima protočni kapacitet pojedine dionice u odnosu na cjelokupnu mrežu., A modified entropy-based criterion for evaluating reliability of water distribution networks is presented in the paper. Design and hydraulic parameters, as well as failure probabilities for individual links, are considered. For this purpose, a new weighting factor is defined based on the proportion of total outflow at each node to the total network dissipated power. Another weighting factor is defined based on the relation between the flow rate of a link and the total flow rates in the network, to take into account the capacity of individual links, as compared to that of the overall network.

The contribution is focused on reliability of balcony girders of a Czech national heritage monument. As preliminary reliability assessment suggests insufficient resistance, a series of nondestructive tests supplemented by a single tensile test are performed and evaluated by the statistical methods. Values of material properties, recommended in standards for historic materials, seem to be overly conservative and it is advised to specify properties of historic metallic materials by tests.

Active Network Management (ANM) technologies represent an effective tool for increasing the penetration of Distributed Generators (DGs) in electrical distribution systems. Under normal operating conditions, these technologies allow a massive integration of renewable generation without exceeding rated values of electrical equipment. In addition to that, these technologies can be used to restore the supply when there is a network outage, yet this additional use has been commonly neglected in reliability studies. In this paper a novel methodology to assess the contribution of ANM technologies to reliability of distribution networks is proposed. Technologies like renewable DGs with controllable output power, on-load tap-changers (OLTC) transformers, demand control solutions and soft open points (SOPs) are all included in the reliability study. Coordinated operation of these technologies during outages and performance of multi-terminal SOPs are also modelled and evaluated. The proposed methodology was tested on two distribution networks. The contribution of different ANM technologies to reliability was assessed and their benefits confirmed. This research was funded by Iberdrola Foundation, research program "Energy and Environment 2018-2019".

Metropolitan cities have hundreds of parking lots (PLs), where each PL may have a capacity to hold hundreds of cars. Large-scale use of electric vehicles coupled with technological advancements in battery storage systems provide several technical and economic benefits of integrating PLs into the grid. This study presents the impacts of electric vehicle PLs on the distribution system reliability. The reliability indices of the distribution feeder are determined using recently proposed novel storage capacity model of a PL that accounts for the load and component outage data of the distribution feeder. The paper uses average interruption duration, average interruption frequency, and energy not served as feeder reliability indices. For load point reliability indices interruption frequency, interruption duration and energy not served are used. Numerical results, thus attained, show that for the backup operation mode of the PL; the feeder reliability indices and load point reliability indices improve up to 26% and 44% respectively.

This paper covers a reliability analysis as a qualitative method, especially focused on axial piston units. The method is based on Fault Tree Analysis (FTA) and results in risk and reliability assessment at the components level. Especially, the development of the reliability assessment as a methodical tool is the core of the paper. Moreover, the FTA is combined with the industrial standard method known as Design Failure Mode Effects Analysis (DFMEA) which is typically used in the development phase of the design. The evaluation and the usability of the FTA methodology is analyzed in connection with field data. Thus, the deviation of the theoretical valuation from the field data was utilized as a success indicator of the method. The analysis of the fault spreading covers the assessment of component faults and links failure states with unit effects. The analysis of the axial piston unit as a system is made on idealized/theoretical design and functional behavior only. Hence, the failure rating and the effect is subsequently applied to determine the fault risk in form of the Risk Priority Number (RPN). The failure modes and effects are based on engineering experience of past decades, supported by existing DFMEAs of axial piston units. Thus, the assessment of the risk priority number is based on previous data, yielding the given severity, occurrence and detection quantification. This approach opens new opportunities of design assessment and the results show a good agreement to the damage accumulation seen in real field data. Furthermore, the connection between theoretical design assessment and field data do support the failure ranking improvement of the DFMEA.

Assessment of the oxygen and carbon dioxide content of expired air during exercise is critical for determining cardiorespiratory status. The purpose of this study was to compare the new portable metabolic analyzer PNOE with COSMED – Quark CPET, a previously validated stationary metabolic cart. Methods: A total of 22 subjects (17 male and 5 female) aged 32.3 ± 11.1 years took part in the study. Breath by breath gas exchange was measured by both devices during a four-stage incremental protocol on a cycle ergometer. On a separate day, 10 participants repeated the trial to assess the reliability of the PNOE metabolic cart. Results: Strong correlations were obtained in VO2 (r = 0.98, p < 0.001), VCO2 (r = 0.98, p < 0.001), VE (r = 0.98, p < 0.001), and RQ (r = 0.91, p < 0.001), between the two devices. Bland-Altman plots revealed a mean difference of 34.0 ± 118 ml/min and 36.4 ± 110 ml/min in VO2 and VCO2 analysis, respectively. There were no significant differences in VO2, VCO2, VE, or RQ between the two devices. Intraclass correlation coefficient was high between the two trials for VO2 (r = 0.98, p < 0.001), VCO2 (r = 0.98, p < 0.001), VE (r = 0.99, p < 0.001), and RQ (r = 0.93, p < 0.001). Conclusions: Our data indicate that the portable metabolic cart PNOE can accurately determine respiratory gases over a wide range of exercise intensities, in healthy individuals, in a controlled laboratory setting.

Modern power distribution networks assume the connection of Distributed Generators (DGs) and energy storage systems as well as the application of advanced demand management techniques. After a network fault these technologies and techniques can contribute individually to the supply restoration of the interrupted areas and help improve the network reliability. However, the optimal coordination of control actions between these resources will lead to their most efficient use, maximizing the network reliability improvement. Until now, the effect of such networks with optimal coordination has not been considered in reliability studies. In this paper, DGs, energy storage and demand management techniques are jointly modelled and evaluated for reliability assessment. A novel methodology is proposed for the calculation of the reliability indices. It evaluates the optimal coordination of energy storage and demand management in order to reduce the energy-not-supplied during outages. The formulation proposed for the calculation of the reliability indices (including the modelling of optimal coordination) is described in detail. The methodology is applied to two distribution systems combining DGs, energy storage and demand management. Results demonstrate the capability of the proposed method to assess the reliability of such type of networks and emphasise the impact of the optimal coordination on reliability.

The process of shaping distribution network structures is one of the most fundamental design tasks, and determines the delivery certainty of media transported by them. It is especially crucial with reference to network elements of a critical character, such as roads or water supply, sewage, or electrical networks. In urban conditions, the geometric shaping of these structures has a quasi-chaotic character that is individual for each network and city. The complexity of these networks increases significantly with the size of a city, and therefore the evaluation of water delivery certainty is also a difficult issue. Despite many years of research, there is no universal method to evaluate this certainty. The objective of this paper is to present two original approaches: the number of minimal efficiency paths from a water source to reference consumption nodes, and the relation of this number to the fractal dimension of a network&rsquo, s geometric structure. The developed methods were tested in the conditions of a few real water supply networks. The obtained results indicate that the analyzed methods can be used for the preliminary and relatively rapid evaluation of water delivery certainty.

Operational flexibility of power system at critical conditions becomes a worthy goal to all network operators (NOs). Such goal is challenging because of the high penetration of Renewable energy resources (RES) as well as the rapid demand growth that add more stresses to the network lines reducing the network reliability levels. To that extent, this paper proposes a reliability evaluation framework incorporating energy storage systems (ESS). The method shows the effectiveness of utilizing the available energy storage (ESS) in the network during contingencies as a secondary application for the ESS beside its main application at normal conditions. In addition, the benefits from considering time varying thermal ratings (TVTR) with the ESS is investigated showing the impact of TVTR on both ESS sizing and ESS effectiveness at contingencies as well as the network reliability. The impact of the proposed model is evaluated through metrics based on nodal reliability indices and the Sequential Monte Carlo simulation (SMC) approach on the 24-Bus IEEE Reliability Test System. The results from the proposed method significantly reduced the expected interruption costs (EIC) by 32%.

Ultra-deep oil and gas wells have become a new development trend in onshore oil and gas exploitation. However, Ultra-deep oil and gas wellbore casing is with high failure risk due to the harsh environment. It is essential to evaluate the reliability of wellbore casing. This paper assesses the operational reliability of wellbore casing using data statistics and numerical simulation. Firstly, the theoretical model for reliability analysis of wellbore casing is established, and the variables in the model are determined, including rock mechanics, cement ring, and casing string strength factors. Subsequently, considering the random distribution of model variables, many statistics and analyses are performed to determine the distribution parameters of the model variables. Eventually, Monte Carlo based numerical simulations are carried out to obtain the residual strength distribution and the reliability of wellbore casing. The production casing in the ultra-deep well with a depth of 6.5 km in China as an industrial case is used to illustrate the present study. It is observed that this study can be useful to guide a more accurate assessment of the reliability of ultra-deep wellbore casing.

This paper formulates a block-layer method for the reliability assessment of distribution systems. The characteristics of the method include: 1) identifying the impact of distribution component reliability on the system and load points using a block-layer structured assessment; 2) incorporating time-dependent failure parameters; and 3) taking account of the topological, seasonal, and meteorological features of the distribution systems under analysis. The proposed method first identifies the three critical parts of the distribution system: main supply, feeder, and secondary substation. It can also include reserve connections and distributed generation. Second, the method frames these parts into the layered structure, each corresponding to the zone of total load curtailment. To verify this reliability technique, this paper simulates distribution feeders assembled in a number of topologies and compares them with the state sampling technique. Data provided by the local utility company are processed to model the equipment and load for the base year. The results show that the proposed method can provide a wide range of partial and system indices. These values assist in the identification of parts of the distribution system and scenarios with low reliability and to determine possible remedial actions.

Construction of a cavern in close proximity to an existing cavern modifies the state of stresses and movements in a zone around the existing cavern, as some degree of interaction between these two caverns generally takes place. This study investigates the interaction of two parallel caverns and the influence of such interaction on stress-induced global stability in terms of a global factor of safety. A series of finite difference analyses were performed to derive the global factor of safety of a system of two parallel and adjacent caverns. A mathematical response surface model was then built using the multivariate adaptive regression splines (MARS) approach and a series of charts based on this surrogate model were developed to relate the global factor of safety to the critical parameters. The built MARS model is of high accuracy and is simple to interpret and can be used to perform probabilistic assessment of ultimate limit state of twin caverns. Accepted version

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

An efficient procedure for defining the boundaries of microgrids in smart distribution systems during distributed generation expansion planning becomes an important consideration in constructing smart grids. A novel approach named "Modified Reverse Current Flow Method" is proposed in this paper to split a large radial distribution system into a required number of self-adequate microgrids. This cluster of microgrids will be capable of utilizing maximum power output of the distributed renewable energy generators and will act as highly reliable zones, during both islanded and grid-connected modes. This method is based on the trend reversal of the flow of current in the various parts of the circuit. This paper uses the practical machine operating curves of the distributed generators to calculate their reactive power output. For the considered distribution system, the proposed method is applied to find the optimal point of operation and the boundaries of microgrids. To bring out the superiority of this novel method, the improvement in reliability indices and economic savings of this method are compared with the results obtained using a similar method available in the literature. This method has several notable merits, namely, increased accuracy in the calculation of annual energy losses and the voltage profile.

Modern power systems must provide efficient, reliable, and environmentally responsible energy. Recently, the inclusion of Microgrids (MGs) has allowed us to overcome some difficulties and face important challenges in this direction, especially related to the use of alternative energy sources. Increased and probabilistic demand, as well as limited energy supply, pose the need to evaluate the reliability of any distribution system (DS) when MGs are introduced. Here we reviewed and classified the state-of-the-art of reliability assessment (RA) in MGs. Initially, we contextualize RA in distribution systems. Next, each of the MGs subsystems components are introduced and the questions (1) why is it important to evaluate the reliability in Microgrids? (2) how do Microgrids influence the reliability of distribution systems? and (3) how does each of its subsystems influence the reliability of the Microgrids? are addressed. A total of 1395 research studies were published between 2002 and 2020. Using the PRISMA model, 147 met the inclusion criteria (71% correspond to research papers and 29% to reviews; 62% were published in journals, 34% were conference papers and 4% were books). The first study dates from 1971. Despite immense advances in MGs, we identified that (1) real test systems constitute an emerging trend; (2) although new MG configurations sound promising, the development and application of new RA techniques are a necessary step towards the identification of potential pitfalls of such architectures; (3) new RA methods or variations of the existing ones, whether analytical or simulation-based, are constantly being proposed, but their comparison for a particular DS, including MGs, are yet to be performed; and (4) more research studies are needed to assess how new control strategies and information and communications technology impact MGs reliability. Future lines of research could build upon these gaps to enhance reliability, especially when alternative energy resources are available.

In general, studies of the seismic reliability of water supply systems through hydraulic analysis models fall into three categories: (1) model development through hydraulic analysis, (2) establishment of post-earthquake recovery strategies, and (3) robust design in preparation for earthquakes and the development of improvement techniques. In this study, with regard to the design of earthquake resistance techniques, which is the third category, a seismic reliability assessment was conducted that considers the water supply system, and a plan to maximize the seismic reliability was determined as a result of the analysis. The degree of improvement in seismic reliability was quantified for water supply systems with various sources and water supply routes via modeling of seismic protection for each water supply route. Korea&rsquo, s A industrial water system, which has a history of large-scale earthquakes, was divided into nine paths, and the reliability of the water supply system and the cost of earthquake resistance were calculated and compared between the different paths. A water supply path consists of a combination of pipes of various sizes and lengths, and the amount of water to be supplied differs between paths. Quantifying the effect of the water supply system and the seismic reliability of a route is intended to provide a basic methodology for identifying the critical path that should be the target of seismic protection when money and resources available for construction are limited. This methodology was used to confirm that the reliability of the water supply, the demand for water, and the cost of earthquake resistance should be considered together to establish an earthquake resistance strategy for the critical path in a water supply system. We expect that the results of this study will be used to establish detailed strategies for preliminary reinforcement against earthquake damage based on critical paths rather than all or individual pipelines in a water supply network.

Safety by passive systems is a key design feature for new generation Nuclear Power Plants (NPPs). The Passive Containment Cooling System (PCCS) of the AP1000 NPP is a typical passive safety system, by which the heat produced in the containment is transferred to the environment through natural circulation and atmosphere is used as ultimate heat sink. Then, the climate conditions at the plant location influence the system performance. Monte Carlo (MC) simulation of random scenarios of embedding Thermal-Hydraulic (T-H) response of the passive system is commonly used for passive safety systems reliability assessment. However, T-H codes are usually computationally burdensome, in this paper, an effective way to overcome this issue and estimate passive safety system reliability is proposed and applied to the PCCS of the AP1000 NPP, showing that the T-H model runs can be reduced for efficient reliability assessment.

We present a framework for assessing operational reliability and performance of Web Services. The framework, named WS-REPAS, is hybrid in that it combines a modelling approach, based on Discrete Time Markov Chains (DTMC), with monitoring and in vivo testing of the service under assessment. Through the passive observation of the software in operation, field data are gathered and used to continuously update values of the parameters of the service DTMC model; changes in the service provisioning, or in the way it is used, trigger an active strategy, which executes proper testing sessions, ultimately yielding faithful estimates of the current service reliability and/or performance. We illustrate the framework and the automated support it provides. We show how it works describing experiments with a Web service publicly available in the Github repository.

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data., This work was partially supported by the project Power2Power: Providing next-generation silicon-based power solutions in transport and machinery for significant decarbonisation in the next decade, funded by the Electronic Component Systems for European Leadership (ECSEL-JU) Joint Undertaking and the Ministry of Science, Innovation and Universities (MICINN), under grant agreement No 826417. In addition, this work was also funded by the Spanish Ministry of Science, Innovation and Universities through the project COGDRIVE (DPI2017-86915-C3-1-R). Preparation of this publication was also partially co-financed by the Polish National Agency for Academic Exchange (NAWA) through the project: “Industry 4.0 in Production and Aeronautical Engineering (IPAE)”.

International audience; Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a modified Goodman curve of titanium-alloy. Giving consideration to the uncertainty of the resonance frequencies and material properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the turbo-fan blade under the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important influence on the fatigue performance of turbo-fan blades.

Using clean energy sources has contributed to various industries, especially the automotive industry. In this regard, large companies have taken a significant step in the field of renewable energy by introducing Electric Vehicles (EVs). One of the main challenges in the EVs area is evaluating the reliability and safety of these vehicles. Thus, the concept of the reliability and safety of EV's components is considered a significant issue. In general, reliability and safety assessment of important electrical components of EVs, i.e. i) the battery pack, ii) power electronic converters, and iii) the electric motor, plays a significant role in the lifetime performance of EVs’ electrical system. This paper presents a comprehensive review of the reliability of EVs’ components from different points of view. Also, the challenges and future perspective of EVs relating to the reliability and safety, which need to be considered have been investigated.

International audience; Reliability assessment Multi-state multivalued decision diagram (MMDD) Phased mission system (PMS) Multi-state system Semi-Markov process Monte Carlo simulation Attitude and Orbit Control System a b s t r a c t Phased mission systems (PMSs) like satellites and spacecraft perform their functions over non-overlapping mission periods, called phases. One of the challenges in assessing reliability of PMSs comes from considering the s-dependence among phases, and the consideration on the multi-state behavior of components and systems makes the reliability analysis even more difficult. To effectively address this problem, a multi-state multival-ued decision diagram algorithm for PMS and a multi-state multi-valued decision diagram model for phased mission system (PMS-MMDD) method is developed for the reliability modelling of non-repairable multi-state components. Based on the Semi-Markov process, a Markov renewal equation-based method is developed to deal with non-exponential multi-state components and a numerical method, the trapezoidal integration method, is used to compute the complex integrals in the path probability evaluation. A case study of a multi-state attitude and orbit control system in a spacecraft is analyzed to illustrate the proposed PMS-MMDD model and the Markov renewal equation-based evaluation method. The accuracy and computation efficiency of the proposed method are verified by the Monte Carlo simulation method.

A daily operation of distribution network with the integration of wind power generation (WPG) is presented in this paper. In this study is considered reliability assessment of distribution network, which is one of the key indices to measure the impact of the renewable units' outage such as WPG. The proposed framework maximizes the distribution network operator (DNO) benefits in short-term scheduling model. A Markov model is proposed to a comprehensive reliability assessment of WPG and battery storage system (BSS). The properties of the main parts of the BSS and WPG and their effects on the distribution system reliability is also provided by the Markov model. A scenario-based method is employed to model the uncertainties of WPG and load value. The suggested model is successfully examined on the 9-bus standard system.

The growing number of large container vessels is the reason of port infrastructure modernization necessity and changes of local bylaws in ports. The accessibility studies based on ship motion simulation allow for the assessment of manoeuvring operations safety for the new port designs and reconstructions of the existed infrastructure. Several factors can be used to determine safety of manoeuvring operations. The paper presents an approach to determine the operational reliability which can be included in an accessibility study to determine the best of the possible solutions with respect to the ship entrance into the harbour. The general reliability model has been proposed and an example of reliability of ship entrance into the Port of Gdynia is presented.

Nowadays the network resilience and flexibility to uncertainties becomes one of the main aim of all network operators (NOs). However, achieving the previous aim faces many challenges related to the rapid demand growth and the high penetration of renewable energy resources (RES) that add more stresses on network components and hence reducing the network reliability levels. To that extent, this paper proposes a reliability evaluation framework incorporating energy storage at network contingencies. The method shows the implementation steps for the utilizing the available energy storage system (ESS) in the network during contingencies as a secondary application for the ESS beside its main application. Moreover, it addresses the impact of the ESS operating parameters from energy, power capacities and efficiency individually on the reliability levels. The impact of the proposed model is evaluated through metrics based on nodal reliability indices and the Sequential Monte Carlo simulation (SMC) approach on the 24-Bus IEEE Reliability Test System. The results from the proposed weighing method significantly reduced the expected interruption costs (EIC) by 32%.

The most popular system for contracting construction works in the Polish market is the tender procedure. The selection of tenders in which a company will take part is very important as it plays a vital role in building its position on the market. Making a bid/no bid decision is influenced by a number of factors. The research carried out so far has been concerned mainly with identification of various kinds of factors influencing contractors’ bidding decisions. The present study describes a group of factors – identified using a survey method – that may influence the contractor's decision to participate in a tender procedure. An important problem revealed by the survey is assessment of experts’ opinions consistency. The method used in this paper for the consistency evaluation is Kendall's coefficient of concordance.