scholarly journals Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status

2021 ◽  
Vol 13 (9) ◽  
pp. 5295
Author(s):  
Thirupathi Samala ◽  
Vijaya Kumar Manupati ◽  
Bethalam Brahma Sai Nikhilesh ◽  
Maria Leonilde Rocha Varela ◽  
Goran Putnik
Keyword(s):  
Health Status ◽  
Complex Systems ◽  
Failure Time ◽  
Residual Life ◽  
Slowing Down ◽  
Adjustment Strategy ◽  
Proposed Model ◽  
Lower Health ◽  
Machine Health ◽  
Multiple Machines

Complex systems consist of multiple machines that are designed with a certain extent of redundancy to control any unanticipated events. The productivity of complex systems is highly affected by unexpected simultaneous machine failures due to overrunning of machines, improper maintenance, and natural characteristics. We proposed realistic configurations with multiple machines having several flexibilities to handle the above issues. The objectives of the proposed model are to reduce simultaneous machine failures by slowing down the pace of degradation of machines, to improve the average occurrence of the first failure time of machines, and to decrease the loss of production. An approach has been developed using each machine’s degradation information to predict the machine’s residual life based on which the job adjustment strategy where machines with a lower health status will be given a high number of jobs to perform is proposed. This approach is validated by applying it in a fabric weaving industry as a real-world case study under different scenarios and the performance is compared with two other key benchmark strategies.

A FINITE RANGE DISCRETE LIFE DISTRIBUTION

1995 ◽  
Vol 02 (02) ◽  
pp. 147-160 ◽  
Author(s):  
C.D. LAI ◽  
D.Q. WANG
Keyword(s):  
Failure Time ◽  
Residual Life ◽  
Discrete Distribution ◽  
Lifetime Distribution ◽  
Mortality Data ◽  
Mean Residual Life ◽  
Life Distribution ◽  
Life Data ◽  
Proposed Model ◽  
Two Parameter

Discrete life data arise in many practical situations and even for continuous data we may find cases where the data are presented in grouped form, so that a discrete model can be used. In this paper, we propose a new two-parameter discrete lifetime distribution for modeling this type of data. The distribution under consideration has some interesting ageing properties; in particular, it is able to describe bathtub-shaped failure rate as well as upside-down bathtub-shaped mean residual life. We use this discrete distribution to model Halley’s mortality data and find it fits reasonably well. The proposed model, though quite simple in appearance, is flexible and potentially useful in describing various types of failure time. Some analytical results will also be presented.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Numerical Simulation of Decontamination of Airborne Fission Products during In-Vessel Release Phase by Containment Spray

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Khurram Mehboob
Keyword(s):  
Droplet Size ◽  
Failure Time ◽  
Ph Value ◽  
Removal Rate ◽  
Droplet Diameter ◽  
Fission Products ◽  
Product Release ◽  
Spray System ◽  
Spray Solution ◽  
Proposed Model

The containment spray system (CSS) has a significant role in limiting the risk of radioactive exposure to the environment. In this work, the optimal droplet size and pH value of spray water to prevent the fission product release have been evaluated to improve the performance of the spray system during in-vessel release phase. A semikinetic model has been developed and implemented in MATLAB. The sensitivity and removal rate of airborne isotopes with the spray system have been simulated versus the spray activation and failure time, droplet size, and pH value. The alkaline (Na2S2O3) spray solution and spray water with pH 9.5 have similar scrubbing properties for iodine. However, the removal rate from the CSS has been found to be an approximately inverse square of droplet diameter (1/d2) for Na2S2O3 and higher pH of spray water. The numerical results showed that 450 μm–850 μm droplet with 9.5 pH and higher or the alkaline (Na2S2O3) solution with 0.2 m3/s–0.35 m3/s flow rate is optimal for effective scrubbing of in-containment fission products. The proposed model has been validated with TOSQAN experimental data.

scholarly journals Model of the Process of Conducting Computer Attacks Using Special Information Influences

2021 ◽  
pp. 12-20
Author(s):  
Sergey Kondakov ◽  
◽  
Ilya Rud ◽  
Keyword(s):  
Information Systems ◽  
Comparative Analysis ◽  
Complex Systems ◽  
Research Method ◽  
System Analysis ◽  
Level Of Abstraction ◽  
Proposed Model ◽  
Analysis And Synthesis ◽  
Computer Attacks ◽  
Special Information

Purpose of work: development of a model of the process of conducting a computer attack. Research method: theory of complex systems, comparative analysis within the framework of system analysis and synthesis. Result: it is shown that the application of the proposed model of the process of conducting computer attacks allows you to fully describe the process, taking into account its inherent features and characteristics. The use in the model of information from the MITRE ATTACK database of Mitre, which contains a description of the tactics, techniques and methods used by cybercriminals, allows you to reduce the level of abstraction and describe specific scenarios for conducting complex targeted computer attacks with the maximum approximation to practice. The developed model is supposed to be used to form scenarios of computer attacks when assessing the security of information systems.

A stochastic covariate failure model for assessing system reliability

2001 ◽  
Vol 38 (03) ◽  
pp. 761-767 ◽  
Author(s):  
Nader Ebrahimi
Keyword(s):  
System Reliability ◽  
Alternative Model ◽  
Failure Time ◽  
Failure Mechanisms ◽  
Failure Model ◽  
Proposed Model ◽  
Deterioration Process

Many failure mechanisms can be traced to an underlying deterioration process, and stochastically changing covariates may influence this process. In this paper we propose an alternative model for assessing a system's reliability. The proposed model expresses the failure time of a system in terms of a deterioration process and covariates. When it is possible to measure deterioration as well as covariates, our model provides more information than failure time for the purpose of assessing and improving system reliability. We give several properties of our proposed model and also provide an example.

The influence of health on economic growth from the perspective of sustainable development: a case of OECD countries

2020 ◽  
Vol 16 (3) ◽  
pp. 181-194 ◽  
Author(s):  
Seda Yıldırım ◽  
Durmus Cagri Yildirim ◽  
Hande Calıskan
Keyword(s):  
Economic Growth ◽  
Cluster Analysis ◽  
Sustainable Development ◽  
Health Status ◽  
Life Expectancy ◽  
Oecd Countries ◽  
Content Type ◽  
Life Expectancy At Birth ◽  
Lower Health

PurposeThis study aims to explain the role of health on economic growth for OECD countries in the context of sustainable development. Accordingly, the study investigates the relationship between health and economic growth in OECD countries.Design/methodology/approachThis study employed cluster analysis and econometric methods. By cluster analysis, 12 OECD countries (France, Germany, Finland, Slovenia, Belgium, Portugal, Estonia, Czech Republic, Hungary, South Korea, Poland and Slovakia) were classified into two clusters as high and low health status through health indicators. For panel threshold analysis, the data included growth rates, life expectancy at birth, export rates, population data, fixed capital investments, inflation and foreign direct investment for the period of 1999–2016.FindingsThe study determined two main clusters as countries with high health status (level) and low health status (level), but there was no threshold effect in clusters. It was concluded that an increase in the life expectancy at birth of countries with higher health status had no significant impact on economic growth. However, the increase in the life expectancy at birth of countries with lower health status influenced economic growth positively.Research limitations/implicationsThis study used data that including period of 1999–2016 for OECD countries. In addition, the study used cluster analysis to determine health status of countries, and then panel threshold analysis was preferred to explain significant relations.Originality/valueThis study showed that the role of health on economic growth can change toward country groups as higher and lower health status. It was proved that higher life expectancy can influence economic growth positively in countries with worse or low health status. In this context, developing countries, which try to achieve sustainable development, should improve their health status to achieve economic and social development at the same time.

scholarly journals An Eigenvector Centrality for Multiplex Networks with Data

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 763 ◽  
Author(s):  
Francisco Pedroche ◽  
Leandro Tortosa ◽  
José F. Vicent
Keyword(s):  
Complex Systems ◽  
Network Theory ◽  
Essential Feature ◽  
Eigenvector Centrality ◽  
Centrality Measure ◽  
Multiplex Networks ◽  
Primitive Matrix ◽  
The Core ◽  
Proposed Model ◽  
Node Classification

Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may be modelled by more than one type of interaction. Multiplex networks are structures capable of describing networks in which the same nodes have different links. Characterizing the centrality of nodes in multiplex networks is a fundamental task in network theory. In this paper, we design and discuss a centrality measure for multiplex networks with data, extending the concept of eigenvector centrality. The essential feature that distinguishes this measure is that it calculates the centrality in multiplex networks where the layers show different relationships between nodes and where each layer has a dataset associated with the nodes. The proposed model is based on an eigenvector centrality for networks with data, which is adapted according to the idea behind the two-layer approach PageRank. The core of the centrality proposed is the construction of an irreducible, non-negative and primitive matrix, whose dominant eigenpair provides a node classification. Several examples show the characteristics and possibilities of the new centrality illustrating some applications.

BAYESIAN SOFTWARE RELIABILITY MODEL COMBINING TWO PRIORS AND PREDICTING TOTAL NUMBER OF FAILURES AND FAILURE TIME

2014 ◽  
Vol 21 (06) ◽  
pp. 1450031 ◽  
Author(s):  
D. DAMODARAN ◽  
B. RAVIKUMAR ◽  
VELIMUTHU RAMACHANDRAN
Keyword(s):  
Bayesian Approach ◽  
Software Reliability ◽  
Failure Time ◽  
Reliability Model ◽  
Software Failure ◽  
Failure Times ◽  
Failure Data ◽  
Time Between Failures ◽  
Software Reliability Model ◽  
Proposed Model

Reliability statistics is divided into two mutually exclusive camps and they are Bayesian and Classical. The classical statistician believes that all distribution parameters are fixed values whereas Bayesians believe that parameters are random variables and have a distribution of their own. Bayesian approach has been applied for the Software Failure data and as a result of that several Bayesian Software Reliability Models have been formulated for the last three decades. A Bayesian approach to software reliability measurement was taken by Littlewood and Verrall [A Bayesian reliability growth model for computer software, Appl. Stat. 22 (1973) 332–346] and they modeled hazard rate as a random variable. In this paper, a new Bayesian software reliability model is proposed by combining two prior distributions for predicting the total number of failures and the next failure time of the software. The popular and realistic Jelinski and Moranda (J&M) model is taken as a base for bringing out this model by applying Bayesian approach. It is assumed that one of the parameters of JM model N, number of faults in the software follows uniform prior distribution and another failure rate parameter Φi follows gama prior distribution. The joint prior p(N, Φi) is obtained by combining the above two prior distributions. In this Bayesian model, the time between failures follow exponential distribution with failure rate parameter with stochastically decreasing order on successive failure time intervals. The reasoning for the assumption on the parameter is that the intention of the software tester to improve the software quality by the correction of each failure. With Bayesian approach, the predictive distribution has been arrived at by combining exponential Time between Failures (TBFs) and joint prior p(N, Φi). For the parameter estimation, maximum likelihood estimation (MLE) method has been adopted. The proposed Bayesian software reliability model has been applied to two sets of act. The proposed model has been applied to two sets of actual software failure data and it has been observed that the predicted failure times as per the proposed model are closer to the actual failure times. The predicted failure times based on Littlewood–Verall (LV) model is also computed. Sum of square errors (SSE) criteria has been used for comparing the actual time between failures and predicted time between failures based on proposed model and LV model.

scholarly journals Multi-lane Changing Model with Coupling Driving Intention and Inclination

2017 ◽  
Vol 29 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Jiangfeng Wang ◽  
Chang Gao ◽  
Zhouyuan Zhu ◽  
Xuedong Yan
Keyword(s):  
Risk Factor ◽  
Lane Changing ◽  
Traffic Demand ◽  
Average Speed ◽  
Slowing Down ◽  
Behavioural Characteristics ◽  
Proposed Model ◽  
Density Flow ◽  
The Impact ◽  
The Relationship

Considering the impact of drivers’ psychology and behaviour, a multi-lane changing model coupling driving intention and inclination is proposed by introducing two quantitative indices of intention: strength of lane changing and risk factor. According to the psychological and behavioural characteristics of aggressive drivers and conservative drivers, the safety conditions for lane changing are designed respectively. The numerical simulations show that the proposed model is suitable for describing the traffic flow with frequent lane changing, which is more consistent with the driving behaviour of drivers in China. Compared with symmetric two-lane cellular automata (STCA) model, the proposed model can improve the average speed of vehicles by 1.04% under different traffic demands when aggressive drivers are in a higher proportion (the threshold of risk factor is 0.4). When the risk factor increases, the average speed shows the polarization phenomenon with the average speed slowing down in big traffic demand. The proposed model can reflect the relationship among density, flow, and speed, and the risk factor has a significant impact on density and flow.

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