A simulation model for multi-platform phased mission system reliability analysis

The capabilities of smart meters and the potential vulnerabilities they introduce make the Advanced Metering Infrastructure (AMI) a critical components of the Smart Grid. A virus propagating among meters and massively issuing remote disconnect commands could have catastrophic consequences. To understand and assess the risk posed by a given set of meter devices getting compromised, we introduce a novel modeling approach based on fuzzy Markov chain. Markov modeling is one of the main approaches used to analyze the reliability of critical systems. Markov models are usually analyzed using mathematical approaches or Monte-Carlo Simulation. For large and complex systems, traditional Markov models suffer from the state explosion problem and quickly become intractable. The key idea of our approach to solve this issue in the context of the smart grid is to leverage the hierarchical topology of AMI to build a high-level model where components represent Neighborhood Area Networks (NAN) instead of individual meters. The fuzzy Markov formalism enables our solution to take into account gradual component degradation. We propose a Fuzzy Markov Model (FMM) for reliability analysis of a Smart Grid. The reliability of each NAN is represented by a number in [0, 1] which denotes the state of that component in the FMM.We define the transition probabilities between states of the fuzzy Markov model based on the fuzzy time-to-failure value for each NAN. We utilize the Fuzzy Monte Carlo Simulation (FMCS) approach to calculate the fuzzy time-to-failure value of each NAN. Our proposed method enables us to assess the reliability of the entire AMI at the meter level while keeping the model manageable.

An accurate method for the analysis of remaining life for pressure vessels already in service would certainly help operation managers facing the question of when to replace mayor pieces of processing equipment. Operating companies would also realize the consequent savings derived from knowing in advance the probability of failure, or from safely extend the operating life of facilities. In this paper the first steps are undertaken towards a probabilistic analysis model for predicting the reliability of cracked pressure vessels as a function of applied operating loads using fracture mechanics. The analysis takes into account the variability in material properties, uncertainty in operating loads, continuing degradation of structural residual strength due to crack initiation and propagation and the combined effect of existing crack and newly initiating crack. The information gathered from inspections and non-destructive tests can be incorporated, together with the stresses caused by the operating loads, into the calculations. A numerical example is provided to demonstrate the methodology. Introduction The development of cracks due to fatigue is one of the main causes of interruption of service for pressure vessels. Cracked pressure vessels would generally be out of service and receive appropriate repair. However, sometimes the vessels need to continue to service until the maintenance or repair condition becomes available. In addition, leaving the cracked pressure vessel in service, provided that it maintains acceptable reliability and safety level, has great economical benefit. Because of these reasons, analysis and assessment of the reliability and safety of cracked pressure vessels becomes an increasingly interesting topic for the petroleum and gas industries. Traditional analysis for the reliability of cracked pressure vessels are based on classical fracture mechanics methods, such as the application of Paris-Erdogan formulation[1,2], and focus generally on single crack initiation and propagation using deterministic analysis. This deterministic technique does not take into consideration the variability and uncertainty of material properties and working loads that, as measurements show, demonstrate significant variation. In this paper a new reliability analysis model is proposed for cracked pressure vessels based on probabilistic fracture mechanics technique. This new model takes into account an extended set of variables such as:variability in material properties;uncertainty in working loads;continuing degradation of structural residual strength due to crack initiation and propagation andthe combined effect of existing and newly initiating cracks. Compared to traditional analysis models based on deterministic analysis techniques, the probabilistic model it is expected to provide a more accurate prediction for the reliability and the safety of cracked pressure vessels. This increased accuracy will improve the decision making process and should translate into significant savings in inspection, repair and replacement expenses for cracked pressure vessels. Reliability Analysis Model The basic procedure for calculation of reliability of cracked pressure vessels in the proposed analysis model is as follows: 1) identify the probability of fracture failure at loading cycle N due to the crack initiating at loading cycle Nd (Nd < N);

The traditional reliability evaluation method does not consider the equipment use environment and multistage mission situation, and can not truly reflect the quality characteristics of equipment in different mission phase. Based on the use characteristics of warship equipment, this paper proposes the methods of considering mission multistage, mission duty cycle correction and maintainability correction when calculating the unit level product mission reliability. It can effectively reflect the use characteristics of ship unit level products, and provides a new method for evaluating the mission reliability of warship equipment.

Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined U learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined U learning function are verified.

Dynamic reliability analysis using the extended support vector regression (X-SVR)

This paper is devoted to the use of a proportional hazards model (PHM) for reliability analysis of solder joints through two case studies: a drop-impact test and a vibration test. The lifetime data collected under various load conditions can be integrated in establishing a PHM for reliability analysis of solder joints. PHM has the capability of evaluating the influence of load conditions (e.g. drop height, power spectrum density in random vibration) on the life span of solder joints. Using this capability, this paper focuses on the implementation of PHM for estimating the reliability characteristics (MTTF and probability density function (p.d.f.) of failure) of solder joints whose load conditions are not contained in the training dataset of the PHM. In comparison with the naive use of lifetime data by simply ignoring the effect of load conditions, the two case studies verify the effectiveness of PHM in providing consistent reasonable estimation of the reliability characteristics of solder joints. The perspectives and the procedures of the use of PHM in this paper are generic and can be directly adopted for other systems/products.

In some special engineering circumstances, it is likely that all parameters of an uncertain automotive structure can only be treated as interval variables due to limited knowledge, but meanwhile their lower and upper bounds can just be modeled as fuzzy variables rather than as deterministic values due to ambiguous information. To handle this dual uncertainties case, a reliability-based optimization method with fuzzy-boundary interval variables is developed in this study, and it is further extended to carry out squeal instability analysis and reduction of brake involving both limited and vague information. In the proposed method, fuzzy-boundary interval variables are utilized to cope with the above dual uncertainties of structure parameters and help to build up the structure response analysis model. First, the structure responses are derived on the basis of α-cut strategy, Taylor series expansion, subinterval analysis, and central difference method. Then, with the aid of fuzzy possibility theory, a reliability analysis model of structure response is developed, which can make use of extra reliability information and thus quantify the reliability more accurately. Next, a reliability-based optimization model involving fuzzy-boundary interval variables is established by integrating the uncertain response analysis model and the reliability analysis model. Finally, the proposed method is extended to carry out automotive brake squeal instability analysis and optimization. The numerical investigations demonstrate the applicability and effectiveness of the proposed method.

Time-dependent structural system reliability analysis model and its efficiency solution

Analysis of common cause failure effect on system reliability in seismic PSA

Modular multi‐level converter (MMC) with press‐packing IGBT devices is the popular and key equipment of VSC‐HVDC system, its reliability is directly related to the equipment safe operation. However, it is difficult to analyse the reliability using the failure statistical approach due to less fault data. In order to improve the reliability analyses accuracy, it is necessary to use the physical failure method by considering the main components fatigue failure mode. The reliability analysis models are presented for the MMC sub‐module with the press‐packing IGBT device and metallised film capacitors. First, based on the MMC‐HVDC simulation model, the losses and the junction temperatures of the IGBT devices were derived by the temperature field model, which the failure rates can be calculated. Furthermore, based the corrosion failure mode, the failure rate calculation of the metallised film capacitor was investigated using the voltage stress and thermal stress, the reliability calculation of the MMC sub‐module was presented by combining the main components failure rates. Finally, the reliability of the VSC‐HVDC based MMC was also presented with the different operation mode. The achievement is helpful to assess and improve the reliability of the VSC‐HVDC.

Multi-state homogeneous Markov models in reliability analysis

Fault trees are a key model in reliability analysis. Classical static fault trees (SFT) can best be analysed using binary decision diagrams (BDD). State-based techniques are favorable for the more expressive dynamic fault trees (DFT). This paper combines the best of both worlds by following Dugan’s approach: dynamic sub-trees are analysed via model checking Markov models and replaced by basic events capturing the obtained failure probabilities. The resulting SFT is then analysed via BDDs. We implemented this approach in the Storm model checker. Extensive experiments (a) compare our pure BDD-based analysis of SFTs to various existing SFT analysis tools, (b) indicate the benefits of our efficient calculations for multiple time points and the assessment of the mean-time-to-failure, and (c) show that our implementation of Dugan’s approach significantly outperforms pure Markovian analysis of DFTs. Our implementation Storm-dft is currently the only tool supporting efficient analysis for both SFTs and DFTs.

Chip kill correct is an advanced type of error correction used in memory sub-systems. Existing analytical approaches for modeling the reliability of memory sub-systems with chipkill correct are limited to those with chip kill-correct solutions that guarantee correction of errors in a single DRAM device. However, stronger chip kill correct solutions that are capable of guaranteeing the detection and even correction of errors in up to two DRAM devices have become common in existing HPC systems. Analytical reliability models are needed for such memory subsystems. This paper proposes analytical models for the reliability of double-chipkill detect and/or correct. Validation against Monte Carlo simulations shows that the output of our analytical models are within 3.9% of Monte Carlo simulations, on average. We used the analytical models to study various aspects of the reliability of memory sub-systems protected by double-chip kill detect and/or correct. Our studies provide several insights into the dependence of reliability of these systems on scale, device fault rate, memory organization, and memory-scrubbing policy.

Slope reliability analysis has always been a research focus for geotechnical engineers. Currently, most studies use stationary random fields to simulate the spatial variability of soil parameters. However, many field test data show that soil parameters such as cohesion and friction angle often exhibit obvious nonstationary characteristics along the burial depth direction. Moreover, the slope reliability analysis method based on Monte Carlo simulation requires a combination of the FEM and a large number of computational resources, resulting in extremely low computational efficiency. Therefore, this paper proposes a convolutional neural network (CNN)-based surrogate model for slope reliability analysis, emphasizing the nonstationary spatial variability of cohesion and friction angle. First, stationary and nonstationary random fields considering the spatial variability of shear strength parameters are generated, and sample data sets are collected using an automated computational program. Next, a CNN surrogate model is established to predict the factor of safety (FOS), which is trained using nine sample data sets containing stationary and nonstationary random fields. Finally, the model performance is validated using six slope models with different shear strength parameters. The results show that the CNN surrogate model can adapt to the data characteristics of different random fields and also shows good predictive performance in handling unseen sample data sets. The predicted FOS values show a high degree of consistency with the results calculated by the FEM, with the computation time being significantly reduced.

For structural reliability analysis with time-consuming performance functions, an innovative design of experiment (DoE) strategy of the Kriging model is proposed, which is named as the stepwise accuracy-improvement strategy. The epistemic randomness of the performance value at any point provided by the Kriging model is used to derive an accuracy measure of the Kriging model. The basic idea of the proposed strategy is to enhance the accuracy of the Kriging model with the best next point that has the largest improvement with regard to the accuracy measure. An optimization problem is developed to define the best next point. The objective function is the expectation that quantifies how much an untried point could enhance the accuracy of the Kriging model. Markov chain Monte Carlo sampling and Gauss–Hermite quadrature are employed to make several approximations to solve the optimization problem and get the best next point. A structural reliability analysis method is also constructed based on the proposed strategy and the accuracy measure employed. Several examples are studied. The results validate the advantages of the proposed DoE strategy.