Traditional Reliability Research Articles

Reliability analysis of Aeroengine Blades Based on Fourier Transform

As a key part of aero-engine, blade is responsible for energy conversion. This paper summarizes the significance of traditional aero-engine blade reliability analysis. The basic principle of Fourier Transform is introduced. According to the Fourier transform, the decomposition method of the actual dynamic load curve of the blade in the working process in the frequency domain is deduced, and the dynamic load is decomposed in the frequency domain. The specific information of the sinusoidal harmonics which constitute the time domain signal is obtained, and then the theoretical method of restoring it to the time domain is deduced. The Fast Fourier Transform (FFT) is applied to the time domain signal by MATLAB. The dynamic load of aero-engine blade is analyzed by Fourier Transform, and the derivation process of Fourier Transform and inverse transform is completed. Fourier Transform can simulate the actual dynamic load curve of aero-engine blade, which can be used as the theoretical basis for the next step of aero-engine blade reliability test-bed loading.

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.

High Concurrent News Communication Strategy Based on Reliable Routing in Wireless Sensor Networks

Since information sensing transmission is an inherent requirement of wireless sensor networks, this paper analyzes the traditional reliability routing algorithm that treats all data groups indiscriminately and cannot make the data groups containing important information preferentially transmitted to the sink node and consume too much energy on unimportant data. The healthy development of news communication can be promoted from the three aspects of audience supervision, platform control, and government supervision, and a cross‐platform operation model can be established to enhance the sociality of the platform and improve new media technologies to achieve accurate push. This paper attempts to sort out the current news and information communication strategies comprehensively from the aspects of content, communication, operation, and profit in the process of news and information communication from production to profit. The communication strategy of news and information communication is highly adaptable to the 4I principle of network marketing. And from the audience supervision, platform control, and government supervision, these three aspects promote the healthy development of news communication news, establish cross‐platform operation mode to enhance the sociality of the platform, improve new media technology to achieve accurate push, innovate business model, enhance liquidity, and so on.

Reliability Engineering Design Method based on Generalized Margin Design theory

Traditional reliability indexes in engineering are statistic parameters of failure time or failure rates, these reliability parameters are not related to products’ design parameters directly. In fact, reliability design in engineering process is margin design, for instance, the safety factors approach, the derating design approach, and even the selection of the high quality components carried out by enhancing the margin of resisting the sense stress of failures. This paper proposes a reliability engineering design method based on the general margin theory, and the quantification relationships between the statistic reliability parameters and general margin parameters are established. The general margin proposed in this paper contains the stress margin and function margin. The margin design rules and requirements are defined according to the products(or concepts design) ‘s loads in the predicted lifecycle; and then the stress margin parameters that satisfying the reliability could be determined based on the stress-strength interference model, and these margin parameters could support the products concepts selection; if the stress margin parameters could not satisfy the requirements, then the function margin parameters are analyzed to guide the key function redundancy design until the reliability of the products are meet. This method could improve the suitability of reliability theory in engineering field. This paper demonstrates the availability of this design method with a case study.

Research on Reliability Prediction method of Complex Mechanical Product Based on Meta-action Unit

Reliability prediction of complex mechanical product is helpful to determine the reliability state of products and that how to optimize products. Traditional reliability prediction methods of complex mechanical products come from electronic products, which need a certain amount of reliability data as support. In addition, the reliability of system complex mechanical product is generally predicted by the parts or component that make up it. However, failure of one part may not cause failure of the whole product, that will affect the accuracy of reliability prediction. To solve these problems and in order to accurately predict the reliability of complex mechanical product, a methodology based on meta-action unit (MAU) which is the basic units affecting reliability is proposed in this paper. The mechanical product is decomposed into many MAUs, and the conception of “unit quality entropy” is introduced to predict the reliability index of MAU. Subsequently, according to the reliability mathematical model of complex mechanical product and MAU, the reliability is predicted by combining interval analytical hierarchy method and eigenvector method. This paper also verifies that the method is applicable and is more accurate with a case study of the reliability of spindle box.

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Aiming at the large error of traditional reliability prediction method, and the defects of BP neural network prediction method, a new method of optimized cascade feedforward neural network was proposed based on Adam algorithm to predict the reliability of CNC machine tool spindles. A three-layer optimized cascade feedforward neural network model for reliability prediction was established based on the first $\text{n}^{\mathrm {th}}$ reliability value and the mean time between failure corresponding to the ( $\text{n}+1$ ) $^{\mathrm {th}}$ reliability value as the input variables of the neural network. Besides, error comparison analysis was performed on the test set data using the existing relevant reliability data for simulation training. As per the research results, the absolute value of the maximum relative error of the reliability prediction value obtained by this method is 2.53% which is less than 3%, and the prediction method shows high accuracy. Compared with BP neural network, it has the advantages of faster learning speed and better nonlinear fitting ability. Therefore, this method is feasible to be utilized for the prediction of the reliability of CNC machine tool spindles, and provides a reference for improving the accuracy of CNC machine tool reliability prediction.

Development of a Comparison Framework for Evaluating Environmental Contours of Extreme Sea States

Environmental contours of extreme sea states are often utilized for the purposes of reliability-based offshore design. Many methods have been proposed to estimate environmental contours of extreme sea states, including, but not limited to, the traditional inverse first-order reliability method (I-FORM) and subsequent modifications, copula methods, and Monte Carlo methods. These methods differ in terms of both the methodology selected for defining the joint distribution of sea state parameters and in the method used to construct the environmental contour from the joint distribution. It is often difficult to compare the results of proposed methods to determine which method should be used for a particular application or geographical region. The comparison of the predictions from various contour methods at a single site and across many sites is important to making environmental contours of extreme sea states useful in practice. The goal of this paper is to develop a comparison framework for evaluating methods for developing environmental contours of extreme sea states. This paper develops generalized metrics for comparing the performance of contour methods to one another across a collection of study sites, and applies these metrics and methods to develop conclusions about trends in the wave resource across geographic locations, as demonstrated for a pilot dataset. These proposed metrics and methods are intended to judge the environmental contours themselves relative to other contour methods, and are thus agnostic to a specific device, structure, or field of application. The metrics developed and applied in this paper include measures of predictive accuracy, physical validity, and aggregated temporal performance that can be used to both assess contour methods and provide recommendations for the use of certain methods in various geographical regions. The application and aggregation of the metrics proposed in this paper outline a comparison framework for environmental contour methods that can be applied to support design analysis workflows for offshore structures. This comparison framework could be extended in future work to include additional metrics of interest, potentially including those to address issues pertinent to a specific application area or analysis discipline, such as metrics related to structural response across contour methods or additional physics-based metrics based on wave dynamics.

Component unavailability model for PSA Level I applications considering aging and startup degradation: An Application to an Emergency Diesel Generator System

The paper proposes an unavailability model, which considers standby component degradation due to repeated startups, including not only planned surveillance tests, but also additional startups due to real demands and, in cases when the component is detected failed in the planned test, extra tests related to corrective maintenance. The model is applied to evaluate the effect of additional tests on Emergency Diesel Generators from a Nuclear Power Plant’s Onsite Power System. These additional tests are not considered in traditional reliability models. The results show that excessive Diesel Generator testing during its working life may lead to a significant increase of component unavailability with reflections on the core damage frequency at the end of its overhaul maintenance period. This increment could justify anticipation of Diesel Generator overhaul maintenance in one or two working cycles.

Definitions and Reliability Evaluation of Multi-state Systems Considering State Transition Process and its Application for Gas Systems

Multi-state system (MSS) models have been widely used to represent the reliability of engineering systems that have a finite number of performance levels. The previous studies related to the reliability evaluation of the MSSs generally follow the assumption that the transition among different performance states is instantaneous with no time delay. However, there exist several engineering systems that transit slowly between different states, such as gas supply systems and heating distribution systems. When evaluating the operating reliability of such an engineering system, the neglect of the non-instant state transition process may lead to inaccurate or insufficient evaluation results. To fill the research gap, the traditional MSS model is extended to handle the situation where the non-instant state transition features should be considered. The concepts and features of multi-state systems considering state transition process (MSS-STP) are innovatively proposed. Considering the non-instant state transition process, the traditional reliability evaluation framework for the MSSs is extended to evaluate the operating reliability of MSS-STP. The procedure of transition curve discretization is innovatively proposed to divide the dynamic transition process into a series of discrete states. The developed general MSS-TSP model is applied to the natural gas system (NGS) to demonstrate the effectiveness of the proposed reliability evaluation framework. The proposed technique can provide a useful tool for system operators to accurately evaluate the operating reliability of MSS-STP

Reliability modeling and analysis for CNC machine tool based on meta‐action

AbstractTraditional machine tool reliability modeling methods take static parts as the research object, ignoring the characteristic that the function and performance of computer numerical control (CNC) machine tool are ultimately driven by its most basic actions, and the traditional reliability modeling methods do not fully consider the randomness of machine tool failure. In view of this, this paper gave an improved four‐parameter nonhomogeneous Poisson process reliability modeling method based on meta‐action (MA), and a comprehensive method of system reliability was presented. A CNC machine tool made in China was taken as an example, and the reliability model of MA and the whole machine were built by the proposed method. The proposed method was compared with other methods by Akaike information criterion and Bayesian information criterion, and the method proposed in this paper was proved to be better. The reliability indexes of the system were simulated, and then the general trend of system reliability indexes was obtained by least squares support vector machines. The results verify the applicability and validity of this method, and lay a foundation for further research on the machine tool reliability based on metamotion.

Research on Reliability Evaluation Method of Aerospace Pyrotechnic Devices Based on Energy Measurement

High reliability is the basic requirement of aerospace pyrotechnic devices. Traditional reliability evaluation methods require a lot of tests, which become too expensive; therefore, the small-sample evaluation method is needed to reduce the cost. Using energy as a performance parameter can better reflect the essence of the function of the pyrotechnic device compared to using force. Firstly, this article assumes that the strength obeys the normal distribution, and the stress is a constant; therefore, the reliability evaluation formula based on the t distribution is proposed. Then, taking the pin puller as the research object, four sets of energy measuring devices were developed so as to obtain its performance parameters. Finally, the evaluation results show that the pin puller has a high reliability of 0.9999999765 with a confidence level of 0.995. The reliability method proposed in this paper is a small-sample method for evaluating aerospace pyrotechnic devices, which can greatly reduce the cost of reliability evaluation. Moreover, the energy measuring devices developed in this paper can provide a new way of measuring performance parameters for piston-type pyrotechnic devices.

Accelerated mechanical low cycle fatigue in isothermal solder interconnects

Properly assessing the underlying physics of failure is critical in predicting the long term reliability of electronic packages in their intended field applications, yet traditional reliability demonstration methods are complicated by time and cost considerations as well as deterministic inadequacies when considering thermomechanical failures. In this work, an alternative reliability testing apparatus and associated protocol were utilized to provide clarity and insight to solder fatigue mechanisms at the device scale; targeting rapid testing times with minimal cost while preserving fatigue life prediction accuracy. A test stand was developed to allow for bi-directional application of shear stress at elevated steady-state temperatures. Utilizing the mechanical force of springs to apply shear loads to solder interconnects within the devices, the reliability of a given device to withstand repeated cycling was studied using in situ resistance monitoring techniques to detect the number of cycles-to-failure (CTF) based on a 30% resistance increase criterion. A mathematical method for quantifying the plastic work density (amount of damage) sustained by the solder interconnects prior to failure was developed relying on the relationship between Hooke's Law for springs and damage deflection to accurately assess the mechanical strength of tested devices.

Reliability Estimation for Zero-Failure Data Based on Confidence Limit Analysis Method

Due to the improvement of the quality of industrial products, zero-failure data often occurs during the reliability life test or in the service environment, and such problems cannot be handled using traditional reliability estimation methods. Regarding the processing and analysis of zero-failure data, the confidence limit assessment methods were proposed by some researchers. Based on the existing research, a confidence limit method set (CLMS) is established in the Weibull distribution for reliability estimation of zero-failure data. The method set includes the unilateral confidence limit method and optimal confidence limit method, so that almost all existing grouping types of zero-failure data can be quickly evaluated, and multiple methods can be used in parallel to deal with the same problem. The effectiveness and high efficiency of the CLMS combined with numerical simulation examples have been verified, and the possibility of analyzing multiple groups of zero-failure data with a confidence limit method suitable for processing single group of zero-failure data is expanded. Finally, the actual effect of the method set is verified by the single group of zero-failure data of rolling bearings and the multiple groups of zero-failure data of torque motors. The results of the example evaluation show that the CLMS has obvious advantages in practical engineering applications.

On the Advanced Methodology of Risk-Based System Resilience Analysis

The modern evolution of technological systems from Hierarchical branching structures purposed to centralized transfer and distribution of limited resources to multi-agent interconnected self-organized networks aimed to produce, transport and consumption of resources are considered. The model of multi-agent interconnected self-organized adaptive networking systems is proposed, the network topology is considered, a system functioning model including transient processes is analyzed. A substantial limitation of the traditional reliability paradigm for a novel type of systems is demonstrated. It was assumed, that optimization approaches in the context of “big data” utilization lead to create a quasi-infinite space of non-structured decisions, which can be characterized as “big decisions”. The modified approach based on the “equally defended networked system” paradigm and the corresponding quantitative risk measure is proposed.

Hybrid AC/DC microgrids flexible reliability index by using the axiomatic design concept

With the capacity increase of large-scale DC renewable energy and DC loads, the reliability issues of hybrid AC/DC networks become increasingly prominent. On the other hand, a modern distribution network should also deliver power to individual customers based on their priority levels and the traditional reliability indices are not suitable for modern networks. Flexible reliability (FR) is a new concept that considers the loads’ importance in the reliability evaluation of distribution grids. In this study, a novel comprehensive FR index is introduced for hybrid AC/DC networks. To determine the proposed index, power networks are modelled as a topological combination of edges and nodes. An axiomatic design concept which is a systematic binary matrix-based approach is then employed for topology analysis. The proposed index is based on the systematic counting of feasible paths from generators to loads, considering loads’ importance. The effectiveness of the proposed index is demonstrated through an illustrative example and IEEE 33-bus distribution system.

The Application of Improved Bayesian Method in Reliability Evaluation

Aiming at the problem that the traditional reliability method with small failure samples cannot accurately calculate the reliability, this paper makes full use of existing information and expert information, etc. Based on the combination of prior information and constrained no prior information, the improved Bayesian method is used to establish reliability. Sexual analysis model. First, a weighted fusion method is used to establish a joint prior distribution that combines prior information with constrained and no prior information according to the credibility of the prior information to the posterior information. At the same time, the robustness of the joint prior distribution is analyzed; The prior distribution is combined with the posterior information to calculate the posterior distribution form of the distribution parameter, and the distribution parameter value and the mean time between failures are calculated.

Improving early stage system design under the uncertainty in reliability-wise structure

This paper is concerned with system reliability improvement at as early as the system's embodiment design stage. Design decisions made at this stage, including component reliability specifications, will have long-term impacts on the entire lifecycle of the system, but they are also difficult to make because the knowledge of system and/or subsystem performance is most likely incomplete at this moment. Thus the system reliability function cannot be fully defined analytically. Because of this inherent uncertainty at early design stage, the traditional reliability evaluation methods, which assume deterministic reliability-wise structures and component reliability specifications, may not be appropriate in real-world applications. In this paper, a Nonparametric Bayesian Network (NPBN) approach is proposed to tackle such challenges at early design stage. The proposed method can represent the uncertainty in the structure of system reliability by combining any available information of component performance in some similar or past-generation systems. This approach can partially quantify the reliability function of the system under design and propose design change recommendations in order to achieve the targeted system reliability level.

360 DEGREE FEEDBACK: FACULTY PERSPECTIVE

The 360-degree feedback system has emerged as a suitable and valuable tool for solving many snags and biases related to other traditional assessment instruments, where validity and reliability have been traditionally questioned. The 360-degree feedback system is based on the hypothesis that the assessment must be carried out from multiple evaluations of various stakeholders that include several dimensions of employees work from varied perspectives. In this sense, the results of the evaluation would benefit from greater objectivity, reliability and validity. In the management education context, it was always a need for faculty to have a robust system of evaluation to identify their contribution of work. Generally, Management institutes have evaluation surveys filled in by students and these are usually the only tool administered for evaluating faculty performance. The outcome of this type of evaluation could have a significant bias as only one set of stakeholders participates in the assessment. It is important to understand that Faculty of management education not only contributes towards teaching but also towards research publications, consultancy and service to institution. On understanding the requirements, there is a need to implement a 360-degree feedback system, which addresses the gaps in evaluating faculty contribution. AICTE has made an attempt to introduce 360 degree feedback mechanism for faculty and laid down some parameters. The process comprises of creating a data framework that maps Faculty, Students and Subjects and this data gets captured through an online mechanism. Further, the system has also proposed to disburse rewards on real time basis. Keeping these developments in background, the aim of this paper is to understand the perception of faculty members on the parameters of 360-degree feedback mechanism as recommended by AICTE

As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI

BackgroundWe focus on the importance of interpreting the quality of the labeling used as the input of predictive models to understand the reliability of their output in support of human decision-making, especially in critical domains, such as medicine.MethodsAccordingly, we propose a framework distinguishing the reference labeling (or Gold Standard) from the set of annotations from which it is usually derived (the Diamond Standard). We define a set of quality dimensions and related metrics: representativeness (are the available data representative of its reference population?); reliability (do the raters agree with each other in their ratings?); and accuracy (are the raters’ annotations a true representation?). The metrics for these dimensions are, respectively, the degree of correspondence, Ψ, the degree of weighted concordanceϱ, and the degree of fineness, Φ. We apply and evaluate these metrics in a diagnostic user study involving 13 radiologists.ResultsWe evaluate Ψ against hypothesis-testing techniques, highlighting that our metrics can better evaluate distribution similarity in high-dimensional spaces. We discuss how Ψ could be used to assess the reliability of new predictions or for train-test selection. We report the value of ϱ for our case study and compare it with traditional reliability metrics, highlighting both their theoretical properties and the reasons that they differ. Then, we report the degree of fineness as an estimate of the accuracy of the collected annotations and discuss the relationship between this latter degree and the degree of weighted concordance, which we find to be moderately but significantly correlated. Finally, we discuss the implications of the proposed dimensions and metrics with respect to the context of Explainable Artificial Intelligence (XAI).ConclusionWe propose different dimensions and related metrics to assess the quality of the datasets used to build predictive models and Medical Artificial Intelligence (MAI). We argue that the proposed metrics are feasible for application in real-world settings for the continuous development of trustable and interpretable MAI systems.

Comparing Methods for Assessing Reliability.

The usual method for assessing the reliability of survey data has been to conduct reinterviews a short interval (such as one to two weeks) after an initial interview and to use these data to estimate relatively simple statistics, such as gross difference rates (GDRs). More sophisticated approaches have also been used to estimate reliability. These include estimates from multi-trait, multi-method experiments, models applied to longitudinal data, and latent class analyses. To our knowledge, no prior study has systematically compared these different methods for assessing reliability. The Population Assessment of Tobacco and Health Reliability and Validity (PATH-RV) Study, done on a national probability sample, assessed the reliability of answers to the Wave 4 questionnaire from the PATH Study. Respondents in the PATH-RV were interviewedtwice about two weeks apart. We examined whether the classic survey approach yielded different conclusions from the more sophisticatedmethods. We also examined two ex ante methods for assessing problems with survey questions and item nonresponse rates and response times to see how strongly these related to the different reliability estimates. We found that kappa was highly correlated with both GDRs and over-time correlations, but the latter two statistics were less highly correlated, particularly for adult respondents; estimates from longitudinal analyses of the same items in the main PATH study were also highly correlated with the traditional reliability estimates. The latent class analysis results, based on fewer items, also showed a high level of agreement with the traditional measures. The other methods and indicators had at best weak relationships with the reliability estimates derived from the reinterview data. Although the Question Understanding Aid seems to tap a different factor from the other measures, for adult respondents, it did predict item nonresponse and response latencies and thus may be a useful adjunct to the traditional measures.