Accuracy Of Reliability Estimation Research Articles

Probabilistic assessment of seismic earth pressure against backfills with a nonlinear failure criterion

A novel approach for the probabilistic assessment of seismic earth pressure against nonlinear backfills is introduced in this study. To implement reliability-based design for the retaining structures under seismic loading condition, nonlinear upper bound analysis is adopted to obtain the seismic earth pressure through optimization procedure. Firstly, rigorous analytical function is formulated to reveal the normal-shear stress information along the failure surface in soil with nonlinear failure criterion. Subsequently, a kinematic equation is put forward to estimate the seismic earth pressures by balancing the external work rate and the internal energy dissipation rate. The solutions are verified by the Discontinuity Layout Optimization numerical modellings based on the derived stress data from the proposed method. To consider the probability analysis of nonlinear backfill properties, the moment method is presented by combining the adaptive dimension decomposition with the direct integral method. According to the estimated first four statistical moments, the approximated probability density function of the performance function is determined contently. Finally, the failure probability of seismic earth pressure is calculated based on the proposed moment method. Two numerical examples demonstrate that the moment method can be adapted to the characteristics of nonlinear backfills and further improve the accuracy of reliability estimation by introducing higher-order moments analytically.

Integrated Sensing and Communications System With Multiple Cyclic Prefixes

This letter proposes an integrated sensing and communications (ISAC) system with multiple cyclic prefixes (CPs) to achieve effectiveness of ISAC in high Doppler scenarios. Multiple CPs include sensing CP and communication CP, which are prepended at the beginning of the transmitted signal and before each modulation symbol, respectively. For transmission, the introduction of multiple CPs obtains abundant maximum unambiguous Doppler by decoupling sensing and communication requirements. For sensing reception, a sensing parameter estimation method is developed which achieves robustness to Doppler shifts. Moreover, the multiple CPs are utilized to enhance sensing processing gain and reduce the complexity of sensing parameter estimation. Theoretical and numerical analyses reveal that the proposed system possesses robustness to Doppler shifts, high processing gain, reliable estimation accuracy and affordable complexity.

Bivariate-Dependent Reliability Estimation Model Based on Inverse Gaussian Processes and Copulas Fusing Multisource Information

Reliability estimation for key components of a mechanical system is of great importance in prognosis and health management in aviation industry. Both degradation data and failure time data contain abundant reliability information from different sources. Considering multiple variable-dependent degradation performance indicators for mechanical components is also an effective approach to improve the accuracy of reliability estimation. This study develops a bivariate-dependent reliability estimation model based on inverse Gaussian process and copulas fusing degradation data and failure time data within one computation framework. The inverse Gaussian process model is used to describe the degradation process of each performance indicator. Copula functions are used to capture the dependent relationship between the two performance indicators. In order to improve the reliability estimation accuracy, both degradation data and failure time data are used simultaneously to estimate the unknown parameters in the degradation model based on the likelihood function transformed using the zeros-ones trick. A simulation study and a real application in the reliability estimation of mechanical seal used in airborne hydraulic pump are conducted to validate the effectiveness and accuracy of the proposed model compared with existing reliability models.

Recoverability effects on reliability assessment for accelerated degradation testing

Accelerated Degradation Testing (ADT) provides an efficient experimental approach to collect lifetime-related data for the reliability assessment of highly reliable products under normal use stress. Recoverability, which occurs in many typical failure modes when online in-chamber measurements have to be replaced by offline ex-chamber measurements with stress released, is an important factor that may affect the accuracy of reliability estimation from ADT data. Nonetheless, the presence of recoverability has not been adequately considered in traditional methods, leading to an over-optimistic estimation of lifetime and reliability. The linkage between recoverability and such inferential bias has not been theoretically studied systematically. In this study, recoverability is explicitly incorporated into the ADT modeling framework. Without loss of generality, the Wiener process is adopted as the basis for the proposed degradation model, superimposed with a cumulative recovery. Theoretical results show that the Mean Time To Failure (MTTF) will be overestimated with recoverability neglected, which leads to poor lifetime-centered decision-making. For finite and even small sample sizes, this conclusion is not intuitively certain but the chance is still high and the corresponding overestimation probability can be calculated explicitly. All theoretical conclusions are validated by simulation studies, in which the MTTFs are overestimated from 6% to 42% under different parameter settings. The real-world application of semiconductor products shows that even slight recoverability could lead to an obvious overestimation of MTTF and the proposed model provides a convenient way to derive more accurate assessment results.

Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems

This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep learning in tackling nonlinear mapping problems, the proposed approach only activates a part of RIS elements and utilizes the corresponding cascaded channel estimate to predict another part. Through a synthetic deep neural network (DNN), the direct channel and active cascaded channel are first estimated sequentially, followed by the channel prediction for the inactive RIS elements. A three-stage training strategy is developed for this synthetic DNN. From simulation results, the proposed deep learning based approach is effective in reducing the pilot overhead and guaranteeing the reliable estimation accuracy.

A quantile-based sequential approach to reliability-based design optimization via error-controlled adaptive Kriging with independent constraint boundary sampling

A significant challenge with reliability-based design optimization (RBDO) is the high computational cost associated with the double-loop structure that entails a large number of function calls for both the optimization process and reliability analysis. Several decoupling methods have been developed to improve the efficiency of RBDO. In addition, surrogate models have been used to replace the original time-consuming models and improve the computational efficiency. This paper proposes a novel quantile-based sequential RBDO method using Kriging surrogate models for problems with independent constraint functions. An error-controlled adaptive Kriging scheme is integrated to derive accuracy information of surrogate models and develop a strategy that facilitates independent training of the models for the performance function. The proposed independent training avoids unnecessary performance function evaluations while ensuring the accuracy of reliability estimates. Moreover, a new sampling approach is proposed that allows refinement of surrogate models for both deterministic and probabilistic constraints. Five numerical examples are carried out to demonstrate the performance of the proposed method. It is observed that the proposed method is able to converge to the optimum design with significantly fewer function evaluations than the state-of-the-art methods based on surrogate models given the constraint functions are independent.

An improved high order moment-based saddlepoint approximation method for reliability analysis

Moment-based methods use only statistical moments of random variables for reliability analysis. The cumulative distribution function (CDF) or probability density function (PDF) of a performance function can be constructed from the perspective of the first few statistical moments, and the failure probability can be evaluated accordingly. However, existing moment-based methods may lead to large errors or instability. As such, the present paper focuses on the high order moment method for higher accuracy of reliability estimation by combining the common saddlepoint approximation technique, and an improved high order moment-based saddlepoint approximation (SPA) method for reliability analysis is presented. The approximated cumulant generating function (CGF) and the CDF of the performance function in terms of its first four statistical-moments are constructed. The developed method can be used for reliability evaluation of uncertain structures follow any types of distribution. Several numerical examples are given to demonstrate the efficacy and accuracy of the proposed method. Comparisons of the new method and several existing high order moment methods are also made on the reliability assessment.

Micromechanics-based reliability analysis method for laminated composite structures

This paper proposes an improved reliability analysis method for composite structures. A new uncertainty model is introduced to quantify the uncertainty in the elastic properties and strength characteristics of the constituents. A multi-scale analysis procedure is developed to link scales in elastic properties, and solve the mechanical responses of all scales. A set of limit state functions are defined, corresponding to the failure modes of intralaminar constituents’ failure and interlaminar delamination. First-order reliability method presents good convergence and accuracy of reliability estimation. Numerical cases are studied to demonstrate the validity and improvements of the proposed method.

Reliability estimation from two types of accelerated testing data considering measurement error

Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.

Doppler Beam Sharpening Using Estimated Doppler Centroid Based on Edge Detection and Fitting

Doppler beam sharpening (DBS) technology is widely used in applications, such as helicopter rescue and early warning surveillance. To obtain the desired DBS images with high quality, accurate Doppler centroid estimation (DoCE) is necessary. Conventional methods for Doppler centroid estimation based on navigational devices are sensitive to the errors of the measured motion parameters. Hence, several alternative data-depended approaches have been developed to reduce the error. In this paper, a novel data-depended Doppler centroid estimation method is proposed to improve the image quality of DBS. We begin the method by analyzing the characteristics of range-Doppler distribution in different regions of interests. Then, the edge feature of range-Doppler distribution in forward-looking direction is extracted using morphological filtering and edge detection methods. We will show that the edge feature defines the required Doppler centroid parameters, which can be utilized to estimate the Doppler centroids of the full scene. At last, the estimation error is reduced through fitting the edge with the minimum mean square error (MMSE) algorithm. As compared with conventional Doppler centroid estimation methods, the proposed method can significantly provide reliable estimation accuracy under low echo signal to noise ratio, independent of conditions that strictly required by conventional methods. Simulations and experiments verify the proposed method.

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

AbstractSoftware development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed.This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.

Statistical Degradation Models for Reliability Analysis in Non-Destructive Testing

In this paper, we consider the application of the statistical degradation models for reliability analysis in non-destructive testing. Such models enable to estimate the reliability function (the dependence of non-failure probability on time) for the fixed critical level using the information of the degradation paths of tested items. The most widely used models are the gamma and Wiener degradation models, in which the gamma or normal distributions are assumed as the distribution of degradation increments, respectively. Using the computer simulation technique, we have analysed the accuracy of the reliability estimates, obtained for considered models. The number of increments can be enlarged by increasing the sample size (the number of tested items) or by increasing the frequency of measuring degradation. It has been shown, that the sample size has a greater influence on the accuracy of the reliability estimates in comparison with the measuring frequency. Moreover, it has been shown that another important factor, influencing the accuracy of reliability estimation, is the duration of observing degradation process.

Integrated multiobjective framework for reliability-based design optimization with discrete design variables

Abstract A Multiobjective Reliability-based Design Optimization (MO-RBDO) problem is of great interest as it can reveal the tradeoff between cost and reliability in the design of structures. The MO-RBDO problem, however, is computationally demanding and difficult to solve in practical situations. The present study proposes a new framework to solve the MO-RBDO problem by simultaneously minimizing the cost and associated failure probability. The proposed framework, dubbed as MO-PS2, extends and combines three methods: Multiobjective Particle Swarm Optimization (MOPSO), Support vector regression (SVR), and Subset simulation (SS). A unique retraining mechanism is developed not only to increase the accuracy of reliability estimation, but also to improve overall optimization performance. MO-PS2 relaxes restrictive assumptions required by existing methods to address practical concerns, such as discrete design variables, nonlinear and non-differentiable performance functions, and disjoint failure domains. A tower space truss example is used to illustrate the application of MO-PS2, whose performance is further validated by comparisons with conventional double-loop and single-loop approaches. The comparison results verify that MO-PS2 outperforms the conventional approaches, in terms of various criteria: solution quality, computational efficiency, performance consistency, and the accuracy of reliability estimation.

Optimal Design of Life Testing for High-Brightness White LEDs Using the Six Sigma DMAIC Approach

Life testing is an essential reliability assessment procedure to qualify a new product or technology before being released to the market. High-brightness white light-emitting diodes (HBWLEDs) with high efficiency, environmental benefits, and high reliability have attracted increasing interest in the field of lighting systems. However, owing to the long lifetime of LEDs, traditional life testing methods for LEDs, which record only failure time, are time consuming and expensive. Therefore, designing an optimal life testing process for HBWLEDs is desirable for accelerating LED technology innovation and development. This paper applies the Six Sigma define–measure–analyze–improve-control (DMAIC) approach to analyze the restrictions in the traditional life testing for HBWLEDs and optimize the life testing procedure. The goal is to shorten the testing time, reduce the testing operation cost, and maintain accurate reliability estimation. In this work, a general reliability estimation method with degradation data is developed by integrating a recursive unscented Kalman filter approach to estimate HBWLED reliability. The results show that, with the help of a recursive UKF, the accuracy of reliability estimation can be improved compared with the ordinary nonlinear least squares approach. Furthermore, the operation time and cost of LED life testing can be reduced by 57.75% and 71.51%, respectively, compared with traditional life testing.

Musical Fundamental Frequency Estimator Based on Harmonic Pattern Match

The fundamental frequency (F0) plays a vital role in music signal analysis and processing. However, due to non-stationary noise, undesired physical vibration from the musical instruments, the robust estimation of F0 remains a main challenge. In this paper, a F0 estimation algorithm of music signals based on harmonic pattern match (HPM) is described to achieve more reliable estimation accuracy. The algorithm utilizes the autocorrelation both in the time domain and in the frequency domain, exploiting the spectrum subset to guide the search of F0 candidates (FCs), and an efficient mechanism to evaluate the match between each FC and the harmonic pattern of the input signal. The harmonic pattern of the measured spectrum is presented by sub-pitch in each segmented sub-band. Finally, the estimated $$ \hat{F}_0$$ is selected to best match the sub-pitches under a weighting strategy. Evaluation experiments were performed over a musical instruments database consisting of single pitched notes and the viability of the HPM algorithm was demonstrated to be competitive with several other F0 estimators.

A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design

A maximum confidence enhancement (MCE)-based sequential sampling approach is developed for reliability-based design optimization (RBDO) using surrogate models. The developed approach employs the ordinary Kriging method for surrogate model development and defines a cumulative confidence level (CCL) measure to quantify the accuracy of reliability estimation when Monte Carlo simulation is used based on the developed surrogate model. To improve the computational efficiency, an MCE-based sequential sampling scheme is developed to successively select sample points for surrogate model updating based on the defined CCL measure, in which a sample point that produces the largest CCL improvement will be selected. To integrate the MCE-based sequential sampling approach with RBDO, a new sensitivity analysis approach is developed, enabling smooth design sensitivity information to be accurately estimated based upon the constructed surrogate model without incurring any extra computational costs, thus greatly enhancing the efficiency and robustness of the design process. Two case studies are used to demonstrate the efficacy of the developed approach.

Risk-based group decision making regarding renewable energy schemes using a stochastic graphical matrix model

Improvements to sustainability are generally measurable based on their environmental, economic, and socio-cultural effects. This study applied this concept by developing and empirically testing a risk-based method for assessing renewable energy policy. An integrated theoretical framework is proposed for analyzing group decision-making regarding renewable energy (RE) policy selection. The proposed graphical matrix approach combined with Monte Carlo simulation compares alternative RE schemes by identifying and measuring critical performance indicators with acceptable reliability. The mathematical model reliably prioritizes alternatives using majority voting to address uncertainty in multi-criteria decision making process. A case study using historical data from previous RE development projects to confirm the feasibility of this approach. Compared to the conventional deterministic method, the stochastic graphical matrix approach provides more reliable estimation accuracy, decision quality, and efficiency in selection of sustainable renewable energy. The systematic approach provides policy makers information for use in evaluation by synthesizing the judgments of a panel of experts.

미소한 신뢰도 감소율을 가지는 원샷 시스템의 가부반응 데이터를 이용한 저장 신뢰도 추정방법 개발

This paper proposes a new reliability estimation method for one-shot systems using quantal response data, which is based on a parametric estimation method. The proposed method considers the time-variant failure ratio of the quantal response data and it can overcome the problems in parametric estimation methods. Seven reliability estimation methods in the literature were compared with the proposed method in terms of the accuracy of reliability estimation in order to verify the proposed method. To compare the accuracy of reliability estimation, the SSEs (Sum of Squared Error) of the reliability estimation results for the different estimation methods were evaluated according to the various numbers of samples tested. The proposed method provided more accurate reliability estimation results than any of the other methods from the results of the accuracy comparison.

Generalizability theory in psychotherapy research: The impact of multiple sources of variance on the dependability of psychotherapy process ratings

A central task of psychotherapy process measurement development is the assessment of reliability and validity. The convention of reporting intraclass correlations (ICCs) for coding procedures assumes that variance in scores can be adequately explained by differences between individuals and error resulting from differences in coders. Given the complex coding procedures that are common in psychotherapy process studies (multiple sessions may be rated by multiple coders on one or more multi-item scales), an ICC may fail to account for all of the relevant sources of variability in obtained scores. If process studies are to provide useful information about the mutative agents in psychotherapy, assessment procedures must be developed that dependably assess the constructs of interest. Generalizability theory provides a framework within which multiple sources of error can be simultaneously evaluated, thus improving the accuracy of reliability estimates and providing critical information for modification and improvement of coding procedures. To illustrate the applicability of generalizability theory to psychotherapy process research, the authors present the design and findings of a study investigating the generalizability of technique scales in the Psychotherapy Process Rating Scale for Borderline Personality Disorder. Implications for measurement development and procedural modifications are discussed.

Interconnect Lifetime Prediction for Reliability-Aware Systems

Thermal effects are becoming a limiting factor in high-performance circuit design due to the strong temperature dependence of leakage power, circuit performance, IC package cost, and reliability. While many interconnect reliability models assume a constant temperature, this paper analyzes the effects of temporal and spatial thermal gradients on interconnect lifetime in terms of electromigration, and presents a physics-based dynamic reliability model which returns reliability equivalent temperature and current density that can be used in traditional reliability analysis tools. The model is verified with numerical simulations and reveals that blindly using the maximum temperature leads to too pessimistic lifetime estimation. Therefore, the proposed model not only increases the accuracy of reliability estimates, but also enables designers to reclaim design margin in reliability-aware design. In addition, the model is useful for improving the performance of temperature-aware runtime management by modeling system lifetime as a resource to be consumed at a stress-dependent rate