•The hierarchical and modular method is applied to the kinematic reliability analysis of industrial robots.•A kinematic reliability calculation and evaluation method based on MFMs is proposed.•The ...influence of intermediate MFM error on kinematic reliability is studied.•Error sum function is proposed to integrate uncertain variables in the reliability calculation model.
The robot kinematic reliability analysis methods based on parts or the whole machine have defects in accuracy and efficiency. Therefore, the paper proposes an efficient method to calculate and evaluate the kinematic reliability of industrial robots based on motion function modules (MFMs). Taking MFMs as the basic analysis units, the paper focuses on the influence of the uncertainty of intermediate MFMs on robot kinematic reliability. Based on the differential linearization error model and multivariate Gaussian distribution method, the transfer and accumulation of errors in the joint motion layer and the functional layer are described. And then, the summation method is proposed to calculate and evaluate the kinematic reliability of industrial robots. Through the error sum functions, the error variables are integrated into the appropriate motion module layer. It can effectively reduce the number of uncertain parameters in the kinematic reliability model, and then reduce the operation cost. In the case study, a TA6R robot is taken as an example to verify the accuracy and practicability of the proposed method.
The robustness and efficiency of performance measure approach (PMA) depend on the reliability loop in reliability-based design optimization (RBDO). For the reliability loop in the PMA using the ...minimum performance target point (MPTP) search, existing approaches can obtain stable results but may converge to inaccurate results, and higher computational efforts are required to achieve the optimum results for highly nonlinear problems. In this paper, a hybrid descent mean value (HDMV) approach is proposed based on a novel merit function, which is applied to combine the MPTP search formulas of the descent mean value (DMV) and advanced mean value (AMV). The merit function is used to adaptively control the numerical instability of the inverse reliability analysis for RBDO-based PMA. The accuracy, robustness and efficiency of the proposed DMV and HDMV methods are compared with existing methods through four nonlinear performance functions, two structural RBDO problems and a complex aircraft panel problem. The results illustrate that the DMV and HDMV methods are more robust, efficient and accurate than existing reliability methods. For the aircraft panel problem, a simultaneous buckling pattern is finally achieved by the proposed methods with better performance in terms of both convergence rate and computational efficiency.
•Hybrid descent mean value (HDMV) approach is proposed for reliability analysis and RBDO using PMA.•A merit function is applied to combine the descent mean value (DMV) and advanced mean value (AMV).•The accuracy, robustness and efficiency of HDMV are compared with several PMA-based reliability methods.•An aircraft panel problem is optimized using buckling probabilistic constraint to illustrate the performances of HDMV.•HDMV method provides robust and efficient results and shows a simultaneous buckling pattern for aircraft problem.
Software-defined networking (SDN) is a softwarization technology of networks that can optimize processes and operation costs and bring new values to infrastructures. The issue of reliability, ...however, becomes more complex in SDN due to new and multi-lateral network domains, and poses many critical challenges on the existing network reliability mechanisms in order to achieve the same reliability services. In this paper, we first identify and illustrate reliability challenges in a control path network that lies between a control plane network and a data plane network to connect them through either an in-band SDN or an out-of-band traditional network. We then observe a number of distinctive control path reliability problems. Accordingly, we propose and develop a control path management framework to enhance SDN reliability addressing the observed issues. It includes several control path reliability algorithms that enhance performance, network protocols that simplify management of control path reliability, as well as a novel control message classification and prioritization system that serves as a fundamental approach to improve scalability and then reliability for SDN. Recognizing the control path as a network and understanding its potential and practical reliability problems enable us to provide effective solutions that prior approaches fall short of. We validate our proposed management framework through extensive experiments through a real network system as well as numerical analyses.
•A method to reduce the randomness degree of stochastic vector processes is proposed.•A sample set of complete probability is obtained by executing one random permutation.•The proposed approach ...applies to PDEA of wind-induced structures.•The proposed approach has good robustness for different random functions.
This paper develops a hybrid approach of spectral representation and random function for simulating stationary stochastic vector processes. In the proposed approach, the high-dimensional random variables, included in the original spectral representation (OSR) formula, could be effectively reduced to only two elementary random variables by introducing the random functions that serve as random constraints. Based on this, a satisfactory simulation accuracy can be guaranteed by selecting a small representative point set of the elementary random variables. The probability information of the stochastic excitations can be fully emerged through just several hundred of sample functions generated by the proposed approach. Therefore, combined with the probability density evolution method (PDEM), it could be able to implement dynamic response analysis and reliability assessment of engineering structures. For illustrative purposes, a stochastic turbulence wind velocity field acting on a frame-shear-wall structure is simulated by constructing three types of random functions to demonstrate the accuracy and efficiency of the proposed approach. Careful and in-depth studies concerning the probability density evolution analysis of the wind-induced structure have been conducted so as to better illustrate the application prospects of the proposed approach. Numerical examples also show that the proposed approach possesses a good robustness.
•A hybrid methodology for the prediction of system reliability considering multiple failure modes’ is proposed•The methodology integrates the Bayesian Network with Copula-based Monte Carlo ...simulation.•The methodology analyzes dynamic interactions of the parameters and their failure modes.•The results reveal that the system's failure probability changes as the degree of dependencies increases.•The proposed methodology will aid integrity management of offshore operation experiencing microbial corrosion.
The stochastic nature of microbial corrosion creates spatial interdependencies among random corrosion parameters and their failure modes. These interdependencies need to be captured for robust offshore system reliability prediction considering complex multispecies biofilms.
This research paper presents a hybrid methodology for the prediction of system reliability, considering multiple failure modes’ interdependencies. The methodology integrates the Bayesian Network with Copula-based Monte Carlo (BN-CMC) simulation. The BN captures the dynamic interactions among physio-chemical parameters and microbes to predict the corrosion rate of an offshore system. The random corrosion parameters dependencies and the failure modes that define the performance functions under microbial corrosion are modeled using CMC. The methodology is assessed with an example, and the impact of dynamic interactions of the parameters and their failure modes on the system reliability is investigated. The results reveal that the system's probability of failure differs diversely as the degree of dependencies among the random corrosion parameters and their failure modes increases. The proposed methodology can predict the failure indexes that could aid system integrity management for a sustainable offshore operation experiencing microbial corrosion.
Regular and reliable access to energy is critical to the foundations of a stable and growing economy. The Nigerian transmission network generates more electricity than is consumed but, due to ...unpredicted outages, customers are often left without electrical power for several hours during the year. This paper aims to assess the present reliability indices of the Nigerian transmission network, and to determine the impact of HVDCs on system reliability. In the first part of this paper, the reliability of the Nigerian transmission system is quantified by building a model in DIgSILENT PowerFactory and carrying out a reliability study based on data provided by the Nigerian transmission-system operator. Both network indices and load-point indices are evaluated, and the weakest points in the network are identified. In the second part of the paper, an HVDC model is built and integrated into the existing network at the locations identified by the reliability study. A comparative study using two different HVDC connections is then carried out, to determine the critical impact of HVDC on system reliability. The reliability results indicate that the weakest points of the transmission system are the radial feeders, and the highest impact could be achieved by spanning an HVDC line between two busbars located at the two extremes of a radial feeder: Azura and Yola.
CMOS scaling has greatly increased concerns for both lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two ...reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the user's concern is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. This paper introduces a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we tackle the problem of maximizing availability for multicore real-time systems with consideration of permanent and transient faults. A framework is proposed to solve the system availability maximization problem. Experimental results on a hardware board and simulation results of synthetic tasks show that our scheme significantly improves system MTTF (and hence availability) compared with existing techniques.
The present study explores the plausibility of measuring personality indirectly through an artificial intelligence (AI) chatbot. This chatbot mines various textual features from users' free text ...responses collected during an online conversation/interview and then uses machine learning algorithms to infer personality scores. We comprehensively examine the psychometric properties of the machine-inferred personality scores, including reliability (internal consistency, split-half, and test-retest), factorial validity, convergent and discriminant validity, and criterion-related validity. Participants were undergraduate students (n = 1,444) enrolled in a large southeastern public university in the United States who completed a self-report Big Five personality measure (IPIP-300) and engaged with an AI chatbot for approximately 20-30 min. In a subsample (n = 407), we obtained participants' cumulative grade point averages from the University Registrar and had their peers rate their college adjustment. In an additional sample (n = 61), we obtained test-retest data. Results indicated that machine-inferred personality scores (a) had overall acceptable reliability at both the domain and facet levels, (b) yielded a comparable factor structure to self-reported questionnaire-derived personality scores, (c) displayed good convergent validity but relatively poor discriminant validity (averaged convergent correlations = .48 vs. averaged machine-score correlations = .35 in the test sample), (d) showed low criterion-related validity, and (e) exhibited incremental validity over self-reported questionnaire-derived personality scores in some analyses. In addition, there was strong evidence for cross-sample generalizability of psychometric properties of machine scores. Theoretical implications, future research directions, and practical considerations are discussed.
IoT-enabled consumer electronics (CE) communication networks, which involve billions of connected consumer devices, can be aptly modeled as Multistate Flow Networks (MFN). In such networks, the edges ...(transmission links) and nodes (CE devices/ access points) are characterized by multi-valued capacity states. Evaluating the reliability of these intricate networks presents an NP-hard computational challenge as the network grows. In this study, we propose a novel algorithm founded on the Sum of Disjoint Products (SDP) concept to compute the probability of successfully transmitting d units of data from a source node to a destination node of the MFN. To validate the correctness and robustness of our approach, we illustrate the algorithm's application using a benchmark network sourced from relevant literature. Additionally, we demonstrate the applicability and scalability of the proposed work through computational experiments, comparing it with two best-known existing MFN reliability evaluation methods. Our method surpasses both the methods by achieving a remarkable 84% and 70% reduction in the computations required to evaluate reliability.
Enhancing the efficiency and the reliability of the data center are the technical challenges for maintaining the quality of services for the end-users in the data center operation. The energy ...consumption models of the data center components are pivotal for ensuring the optimal design of the internal facilities and limiting the energy consumption of the data center. The reliability modeling of the data center is also important since the end-user's satisfaction depends on the availability of the data center services. In this review, the state-of-the-art and the research gaps of data center energy consumption and reliability modeling are identified, which could be beneficial for future research on data center design, planning, and operation. The energy consumption models of the data center components in major load sections i.e., information technology (IT), internal power conditioning system (IPCS), and cooling load section are systematically reviewed and classified, which reveals the advantages and disadvantages of the models for different applications. Based on this analysis and related findings it is concluded that the availability of the model parameters and variables are more important than the accuracy, and the energy consumption models are often necessary for data center reliability studies. Additionally, the lack of research on the IPCS consumption modeling is identified, while the IPCS power losses could cause reliability issues and should be considered with importance for designing the data center. The absence of a review on data center reliability analysis is identified that leads this paper to review the data center reliability assessment aspects, which is needed for ensuring the adaptation of new technologies and equipment in the data center. The state-of-the-art of the reliability indices, reliability models, and methodologies are systematically reviewed in this paper for the first time, where the methodologies are divided into two groups i.e., analytical and simulation-based approaches. There is a lack of research on the data center cooling section reliability analysis and the data center components' failure data, which are identified as research gaps. In addition, the dependency of different load sections for reliability analysis of the data center is also included that shows the service reliability of the data center is impacted by the IPCS and the cooling section.
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