Accurate Reliability Evaluation Research Articles

W46. CRISPR/CAS9-MEDIATED CREATION OF A CELLULAR MODEL TO INVESTIGATE THE FUNCTION OF EGR1 TRANSCRIPTION FACTOR ASSOCIATED WITH SCHIZOPHRENIA

In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.

Fatigue reliability evaluation of Q460 welded joints using fracture fatigue entropy method

In this paper, the fracture fatigue entropy (FFE) was used to evaluate the fatigue reliability of Q460 welded joints during high-cycle fatigue. The Q460 butt welded joint fatigue tests were conducted under different loads. Meanwhile, the real-time temperature measurement, monitored by an infrared thermal scanner, was completed. The fatigue limit can be well estimated according to the cumulative damage entropy production rate in Phase II, and compared with the fatigue limit prediction value by the traditional staircase method, a good consistency is achieved. Furthermore, the P-S-N curves of 5%, 50%, and 95% survival probability rate are well predicted, which is consistent with the experimental data. Therefore, based on this, the accurate fatigue reliability evaluation based on the proposed method has been achieved.

Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains

In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.

Experimental Identification of Statistical Correlation Between Mechanical Properties of FRP Composite.

Recent prediction on the heavy statistical correlation between the mechanical properties of fiber reinforced composite (FRP) raises new concerns on the accurate reliability evaluation of composite structures, but such statistical correlation still lacks experimental verification. In this work, an experimental methodology is proposed to determine the statistical correlation between mechanical properties of unidirectional FRP composite. A rectangular shaped carbon fiber reinforced plastic (CFRP) specimen with a circular hole is loaded by tension, and 3D digital image correlation (DIC) is employed to characterize the heterogeneous strain field around the hole. Virtual field method (VFM) is used to derive E11, E22, ν12, and G12 of specimens. Specimen configuration considering fiber angle and hole diameter is optimized to achieve accurate determination of correlation coefficients. Experimental results on the linear correlation coefficients between E11, E22, ν12, and G12 agree well with previous theoretical predictions.

A modified reliability model for lithium-ion battery packs based on the stochastic capacity degradation and dynamic response impedance

The reliability assessment of battery packs is an important topic in the reliability design of electric vehicles. To improve the accuracy of the reliability analysis, a modified reliability model for lithium-ion battery packs is developed that couples multiphysics models, degradation models, and a multistate system reliability model. For the degradation models, a stochastic capacity degradation model and a dynamic response impedance (DRI) model are established based on experimental data: a) the stochastic capacity degradation characteristics of cells are investigated, and b) the dynamic response characteristics of the impedance with degradation and changing temperature are studied. It is found that Weibull distribution is slightly more appropriate than normal distribution when the temperature is 25 °C, then reversed with increasing temperature. The algorithm for distribution selection is proposed to describing this concept drift phenomenon. Then, the influence of DRI on the thermal performance is analyzed. Finally, reliability assessment of battery pack and several calculation examples are implemented for comparative analysis. The results show that the fading rate of cells in battery pack are accelerated when DRI is incorporated. Thus, multiphysics-based system reliability model with considering the stochastic capacity degradation and DRI enables more scientific and accurate reliability evaluation of battery pack.

A New Solar Radiation Model for a Power System Reliability Study

The dwindling number of conventional power resources and its environmental impact has motivated a transition to renewable energy sources, such as solar power. Evaluating the reliability of solar power integration into power networks can help decision makers gauge the feasibility of their solar power projects. However, the stochastic and non-stationary nature of solar radiation is difficult to be modeled and can even hinder an accurate evaluation of reliability. A good solar model for accurately assessing solar-power-integrated systems should be able to retain the original statistical properties of the sampled solar radiation data. Therefore, this paper aims to develop a new robust and easy-to-use methodology for simulating solar radiation. The proposed model was compared with four other models, including the clearness index, auto-regressive moving average, and two probability-distribution-based models. Five statistical tests, namely, F-test, diurnal distributions, partial auto-correlation function (PACF), mean, and standard deviation, were performed for the comparison. The comparison results indicate that the proposed method effectively retains the statistical properties of the original data and outperforms all other models in the tests. Therefore, the proposed model can be used for assessing solar-power-integrated power systems.

A Sequential Accelerated Life Testing Framework for System Reliability Assessment With Untestable Components

Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests, which are much more expensive than the component-level tests, the accelerated life testing (ALT) design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans is then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.

Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve

Estimation of earth-fill dam height and dimension of spillway are strongly depend on accurate evaluation of dam overtopping reliability. Ideally, whole random and effective variables on overtopping should be considered for dam overtopping reliability assessment. However, taking into account all affecting random variables as well as overtopping reliability assessment using algorithms such as Monte Carlo (MC) could be excessively time consuming and impossible in some cases. In this study, new approach has been introduced as conditional reliability method and it has been tried to reduce the simulations time significantly by using combination of rainfall threshold theory, SUFI (Sequential Uncertainty Fitting) and MC methods. According two defined indexes, the obtain results shown, the relative error of new approach in computation of Jamishan dam overtopping reliability is less than 0.23%. Also, time saving in new method against to regular MC method is more than 87%. So, the new introduced method has more efficiency with acceptable accuracy for assessment of dam overtopping reliability. In addition, among hydrological and hydraulic uncertainties, hydrological uncertainties have more effect on overtopping reliability.

Investigation on NBTI-induced dynamic variability in nanoscale CMOS devices: Modeling, experimental evidence, and impact on circuits

With the downscaling of CMOS devices, dynamic variability induced by negative bias temperature instability (NBTI) has become a critical issue. In addition to the time-dependent device-to-device variation (DDV) of NBTI degradation, the cycle-to-cycle variation (CCV) originated from random trap occupation is found non-negligible and should be added into the total dynamic variation. This paper summarizes our recent studies on NBTI-induced dynamic variability, focusing on the CCV effect, with more details on the statistical modeling, circuit reliability simulation methodologies and experimental results. By adding the random trap occupation into consideration, a statistical model for total dynamic variation (DDV+CCV) is proposed. The effective occupancy probability peff is introduced as a key parameter for modeling and circuit reliability simulation. With the statistical trap response (STR) method and modified on-the-fly method, the proposed model is validated by the experimental evidence under both DC and AC NBTI. According to the model and experimental results, circuit reliability simulation framework is proposed for both long-term quasi-static and short-term transient performance evaluation with the additional impact of CCV. Two representative digital circuit units, ring oscillator (RO) and SRAM cell, are simulated under different conditions, indicating it necessary to consider the evident influence of the CCV in accurate circuit reliability evaluation. The results are helpful for the reliability/variability-aware circuit design in nanoscale technology.

An Analytical Approach to Evaluate the Reliability of Offshore Wind Power Plants Considering Environmental Impact

The accurate quantitative reliability evaluation of offshore wind power plants (OWPPs) is an important part in planning and helps us to obtain economic optimization. However, loop structures in collector systems and large quantities of components with correlated failures caused by shared ambient influences are significant challenges in the reliability evaluation. This paper proposes an analytical approach to evaluate the reliability of OWPPs considering environmental impact on failures and solve the challenges by protection zone models, equivalent power unit models, and common cause failure (CCF) analysis. Based on the investigation of the characteristics of OWPP and related failures mechanisms, the components are divided into three CCF subsets. With the aid of the protection zone model and equivalent power unit model merged with CCF, the faulty collector system state evaluation is applied to reduce the computational burden. The case studies present the necessity and improved performance of merging CCF analysis into modeling via the comparison with other two simplified methods. A sensitivity analysis is also carried out to account for inaccuracy of failure data. The results show that the assumption of independent failures in the conventional method might lead to over-optimistic or over-pessimistic evaluation depending on the CCF style.

Complete monotonic expression of the fourth-moment normal transformation for structural reliability

Probability distributions of basic random variables are essential for the accurate evaluation of structural reliability. In engineering practice, the probability distributions of some random variables are often unknown and the only available information about these may be their statistical moments. To conduct structural reliability analysis without the exclusion of random variables with unknown probability distributions, the fourth-moment normal transformation (FMNT) has been proposed. However, the applicability of expression of the FMNT has not been sufficiently investigated. Furthermore, the monotonic regions of the FMNT are not defined without which the application of the transformation is inconvenient, or even unreliable in reliability analysis. In the present paper, a complete expression of the FMNT including six cases with different combinations of skewness and kurtosis is derived, and the monotonicity of each case of the FMNT expression is confirmed. Literature suggests that the complete monotonic expression of the fourth-moment normal transformation is the first time to be successfully accomplished up to date. Through the numerical examples, the FMNT is found to be quite efficient for normal transformation and to be sufficiently accurate to include random variables with unknown probability distributions in structural reliability analysis.

Seismic reliability evaluation of gas supply networks based on the probability density evolution method

An accurate and efficient reliability evaluation is necessary for network systems with uncertain conditions, especially for the lifeline networks subjected to earthquakes. This study takes gas supply networks as examples and establishes an approach to evaluate the seismic reliability of networks on the basis of the probability density evolution method. First, a nonlinear finite element model is established to simulate buried pipe networks that are subjected to earthquakes. Then, the seismic stresses of the pipes are derived, and the von Mises stresses of the pipes are calculated and used to judge pipe failure. Second, a connectivity index is defined to describe the connectivity between the source and the terminal. Third, on the basis of the probability density evolution method, network reliability is obtained after introducing a physically-based model to simulate ground motion field. Finally, two networks are used as examples to demonstrate the proposed approach, and the results are validated by the Monte Carlo simulation method and compared with a selective recursive decomposition algorithm.

Methodology to evaluate reliability of performance of second-order automatic control system

Reliability evaluation plays a more and more important role in nuclear engineering, aviation industry, and so on. Due to the special integrity of a control system, traditional reliability methods, such as the reliability block diagram and fault tree analysis, are not proper to make a reasonable and accurate reliability evaluation on a control system. However, a systematic methodology to predict performance reliability of a control system is rarely reported. In this article, a methodology of performance reliability based on the performance requirement for an automatic control system, not just limited to the stability reliability or probabilistic robustness evaluation, is first proposed. A solution to evaluate control performance reliability is introduced with analytical approximation approach called first-order second-moment method. Also, a robust iterative algorithm is adopted to overcome the bifurcation and chaos problems encountered when searching the most probable point for highly nonlinear limit state function. An elaborate example based on a servo motor control system is presented to calculate performance reliability of the settling time of the control system with first-order second-moment method. Its numerical approximation is demonstrated to be accurate through Monte Carlo simulation.

CLERECO, Cross-Layer Early Reliability Evaluation for the Computing cOntinuum, FP7

Advanced multi-functional computing systems realized in forthcoming manufacturing technologies hold the promise of a significant increase in device integration density complemented by an increase in system performance and functionality. However, a dramatic reduction in single device quality and reliability is also expected.CLERECO research project recognizes early accurate reliability evaluation as one of the most important and challenging tasks throughout the design cycle of computing systems across all domains. In order to continue harvesting the performance and functionality offerings of technology scaling, we need to dramatically improve current methodologies to evaluate the reliability of the system.On one hand, we need accurate methodologies that reduce the performance and energy tax paid to guarantee correct operation of systems. The rising energy costs needed to compensate for increasing unpredictability are rapidly becoming unacceptable in today's environment where energy consumption is often the limiting factor on integrated circuit performance. On the other hand, early budgeting for reliability has the potential to save significant design effort and resources and has a profound impact on the TTM of a product. CLERECO addresses early reliability evaluation with a cross-layer approach across different computing disciplines, across computing system layers and across computing market segments to address reliability for the emerging computing continuum. CLERECO methodology will consider low-level information such as raw failure rates as well as the entire set of hardware and software components of the system that eventually determine the reliability delivered to the end users.The CLERECO project methodology for early reliability evaluation will be comprehensively assessed and validated in advanced designs from different applications provided by the industrial partners for the full stack of hardware and software layers.

Markovian model for reliability assessment of microgrids considering load transfer restriction

Reliability is an indispensable factor in power system design and operation and has a significant impact on grid safety and economy. Future power distribution systems are expected to be more sophisticated, owing to the increasing penetration of renewable resources and adoption of advanced information and communication technologies. Extant studies in this field tend to focus on the modeling and assessment of the reliability of future microgrid distribution systems, including distributed generation, without considering networked configuration and limited transfer capacity. In the work presented in this paper, a Markov model is implemented to perform a practical and accurate reliability evaluation of networked electric microgrids under load transfer restriction conditions. The Markov model is used to model the microgrid based on the connectivity between the source and the loads and to compute load and system reliability indices. Moreover, the distribution load flow (DLF) method is adopted when reclassifying the Markov model states based on the system's transfer capability during interruptions. The obtained results confirm that the proposed model is efficient and that the DLF provides a more accurate reliability analysis due to the computing of the voltage profile during the system outage restoration process. This model can also be used to optimally integrate distributed generators into the power system at proper locations and with proper capacities to enhance the system's reliability.

Reliability Evaluation of a Microgrid Considering Its Operating Condition

Microgrids offer several reliability benefits, such as the improvement of load-point reliability and the opportunity for reliability-differentiated services. The primary goal of this work is to investigate the impacts of operating condition on the reliability index for microgrid system. It relies on a component failure rate model which quantifies the relationship between component failure rate and state variables. Some parameters involved are characterized by subjective uncertainty. Thus, fuzzy numbers are introduced to represent such parameters, and an optimization model based on Fuzzy Chance Constrained Programming (FCCP) is established for reliability index calculation. In addition, we present a hybrid algorithm which combines scenario enumeration and fuzzy simulation as a solution tool. The simulations in a microgrid test system show that reliability indices without considering operating condition can often prove to be optimistic. We also investigate two groups of situations, which include the different penetration levels of microsource and different confidence levels. The results support the necessity of considering operating condition for achieving accurate reliability evaluation.

A Stochastic Computational Approach for Accurate and Efficient Reliability Evaluation

Reliability is fast becoming a major concern due to the nanometric scaling of CMOS technology. Accurate analytical approaches for the reliability evaluation of logic circuits, however, have a computational complexity that generally increases exponentially with circuit size. This makes intractable the reliability analysis of large circuits. This paper initially presents novel computational models based on stochastic computation; using these stochastic computational models (SCMs), a simulation-based analytical approach is then proposed for the reliability evaluation of logic circuits. In this approach, signal probabilities are encoded in the statistics of random binary bit streams and non-Bernoulli sequences of random permutations of binary bits are used for initial input and gate error probabilities. By leveraging the bit-wise dependencies of random binary streams, the proposed approach takes into account signal correlations and evaluates the joint reliability of multiple outputs. Therefore, it accurately determines the reliability of a circuit; its precision is only limited by the random fluctuations inherent in the stochastic sequences. Based on both simulation and analysis, the SCM approach takes advantages of ease in implementation and accuracy in evaluation. The use of non-Bernoulli sequences as initial inputs further increases the evaluation efficiency and accuracy compared to the conventional use of Bernoulli sequences, so the proposed stochastic approach is scalable for analyzing large circuits. It can further account for various fault models as well as calculating the soft error rate (SER). These results are supported by extensive simulations and detailed comparison with existing approaches.

Accurate reliability evaluation using quantum‐dot cellular automata probabilistic transfer matrix

The probabilistic transfer matrix (PTM) plays an important role in analysing the transient faults and the reliability of digital logic circuits. Proposed a modified PTM (QPTM) for quantum-dot cellular automata (QCA). It has two features different from the previous PTM model: (i) finding more accurate matrices for the wires; and (ii) dividing the QCA circuits more reasonably. Simultaneously, the wire's correct probability is curved by a Gaussian function to enable it to emerge successfully. By using this QPTM, the detailed and extensive simulation results gained reveal that the reliabilities obtained from the PTM analysis of the same circuit could differ at 9%. As for the reliabilities achieved from the PTM and the QPTM, the difference rises to 30%. The simulations obtained have proved that the PTM has weaknesses when presenting the effects of the wire's length and division on the circuit reliabilities.

Combined Reliability Testing Profile Model and Application for Embedded System

Embedded system is applied more and more extensively in the fields of national defense weapon equipment. With the increasing complexity of embedded systems, the combination of the hardware with software is more and closer. Because of the overall characteristic of hardware and software combination in embedded system, traditional reliability test can hardly accurately identify the reliability of embedded systems. The proposed combined reliability testing method, which combines software reliability test with system integrated environmental stress test to simulate the state of system running, provides the basis for the accurate validation and evaluation of reliability of embedded system.

Modeling and simulation of fluctuating wind speeds using evolutionary phasespectrum

According to the characteristics of vortexes with different frequencies in atmospheric turbulence, a rational hypothesis is proposed in the present paper that the time history of fluctuating wind speeds can be viewed as the integration of a series of harmonic waves with the same initial zero-phase. A univariate model of phase spectrum is then developed which relies upon a single argument associated with the concept of starting-time of phase evolution. The identification procedure of starting-time of phase evolution is detailed and its probabilistic structure is investigated through the estimation of the measured data of wind speeds. The univariate phase spectrum model is proved to be valid, bypassing the need of the classical spectral representation techniques in modeling the phase spectrum where hundreds of variables are required. In conjunction with the Fourier amplitude spectrum, a new simulation scheme, based on the stochastic Fourier functions, for fluctuating wind speeds is developed. Numerical and experimental investigations indicate that the proposed scheme operates the accurate simulation of fluctuating wind speeds efficiently that matches well with the measured data of wind fields by revealing the essential relationship among the individual harmonic waves. The univariate phase spectrum model exhibits the potential application for the accurate analysis and reliability evaluation of random wind-induced responses of engineering structures.