397 research outputs found
A discrete MMAP for analysing the behaviour of a multi-state complex dynamic system subject to multiple events.
A complex multi-state system subject to different types of failures, repairable and/or nonrepairable, external shocks and preventive maintenance is modelled by considering a discrete
Markovian arrival process with marked arrivals (D-MMAP). The internal performance of the
system is composed of several degradation states partitioned into minor and major damage
states according to the risk of failure. Random external events can produce failures throughout
the system. If an external shock occurs, there may be an aggravation of the internal degradation, cumulative external damage or extreme external failure. The internal performance and the
cumulative external damage are observed by random inspection. If major degradation is
observed, the unit goes to the repair facility for preventive maintenance. If a repairable failure
occurs then the system goes to corrective repair with different time distributions depending on
the failure state. Time distributions for corrective repair and preventive maintenance depend on
the failure state. Rewards and costs depending on the state at which the device failed or was
inspected are introduced. The system is modelled and several measures of interest are built into
transient and stationary regimes. A preventive maintenance policy is shown to determine the
effectiveness of preventive maintenance and the optimum state of internal and cumulative
external damage at which preventive maintenance should be taken into account. A numerical
example is presented, revealing the efficacy of the model. Correlations between the numbers of
different events over time and in non-overlapping intervals are calculated. The results are
expressed in algorithmic-matrix form and are implemented computationally with Matlab.Junta de AndalucĂa, Spain, under the grant FQM307Ministerio de EconomĂa y Competitividad, España, MTM2017-88708-PEuropean Regional Development Fund (ERDF
Optimal Periodic Inspection of a Stochastically Degrading System
This thesis develops and analyzes a procedure to determine the optimal inspection interval that maximizes the limiting average availability of a stochastically degrading component operating in a randomly evolving environment. The component is inspected periodically, and if the total observed cumulative degradation exceeds a fixed threshold value, the component is instantly replaced with a new, statistically identical component. Degradation is due to a combination of continuous wear caused by the component\u27s random operating environment, as well as damage due to randomly occurring shocks of random magnitude. In order to compute an optimal inspection interval and corresponding limiting average availability, a nonlinear program is formulated and solved using a direct search algorithm in conjunction with numerical Laplace transform inversion. Techniques are developed to significantly decrease the time required to compute the approximate optimal solutions. The mathematical programming formulation and solution techniques are illustrated through a series of increasingly complex example problems
A complex multi-state k-out-of-n: G system with preventive maintenance and loss of units
In this study, a multi-state k-out-of-n: G system subject to multiple events is modeled through a Markovian Arrival Process with marked arrivals. The system is composed initially of n units and is active when at least k units are operational. Each unit is multi-state, each of which is classified as minor or major according to the level of degradation presented. Each operational unit may undergo internal repairable or non-repairable failures, external shocks and/or random inspections. An external shock can provoke extreme failure, while cumulative external damage can deteriorate internal performance. This situation can produce repairable and non-repairable failures. When a repairable failure occurs the unit is sent to a repair facility for corrective repair. If the failure is non-repairable, the unit is removed. When the system has insufficient units with which to operate, it is restarted. Preventive maintenance is employed in response to random inspection. The system is modeled in an algorithmic and computational form. Several interesting measures of performance are considered. Costs and rewards are included in the system. All measures are obtained for transient and stationary regimes. A numerical example is analyzed to determine whether preventive maintenance is profitable, financially and in terms of performance.Junta de AndalucĂa (Spain) FQM-307Ministerio de EconomĂa y Competitividad (España) MTM2017-88708-PEuropean Regional Development Fund (ERDF
MoMA Algorithm: A Bottom-Up Modeling Procedure for a Modular System under Environmental Conditions
The functioning of complex systems relies on subsystems (modules) that in turn are
composed of multiple units. In this paper, we focus on modular systems that might fail due to wear
on their units or environmental conditions (shocks). The lifetimes of the units follow a phase-type
distribution, while shocks follow a Markovian Arrival Process. The use of Matrix-Analytic methods
and a bottom-up approach for constructing the system generator is proposed. The use of modular
structures, as well as its implementation by the Modular Matrix-Analytic (MoMA) algorithm, make
our methodology flexible in adapting to physical changes in the system, e.g., incorporation of new
modules into the current model. After the model for the system is built, the modules are seen as a
âblack boxâ, i.e., only the contribution of the module as a whole to system performance is considered.
However, if required, our method is able to keep track of the events within the module, making
it possible to identify the state of individual units. Compact expressions for different reliability
measures are obtained with the proposed description, optimal maintenance strategies based on
critical operative states are suggested, and a numerical application based on a k-out-of-n structure
is developed.Spanish Ministry of Science and Innovation-State Research Agency PID2020-120217RB-I00
PID2021-123737NB-I00Junta de Andalucia B-FQM-284-UGR20
CEX2020-001105-/AEI/10.13039/50110001103
Analytical Results for a Single-Unit System Subject To Markovian Wear and Shocks
This thesis develops and analyzers a mathematical model for the reliability measures of a single-unit system subject to continuous wear due to its operating environment and randomly occurring shocks that inflict a random amount of damage to the unit. Assuming a Markovian operating environment and shock arrival mechanism, Laplace-Stieltjes transform expressions are obtained for the failure time distribution and all of its moments. Moreover, an analytical expression is derived for the long-run availability of the single-unit system when it is subject to an inspect-and-replace maintenance policy. The analytical results are illustrated, and their results compared with those of Monte Carlo-simulated failure data. The numerical results indicate that the reliability measures may be accurately computed via numerical inversion of the transform expressions in a straightforward manner when the input parameters are known a priori. In stark contrast to the simulation model which requires several hours to obtain the reliability measures, the analytical procedure computes the same measures in only a few seconds
Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model
For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models.
In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, ÎŽ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks.
Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o
Markovian arrivals in stochastic modelling: a survey and some new results
This paper aims to provide a comprehensive review on Markovian arrival processes (MAPs),
which constitute a rich class of point processes used extensively in stochastic modelling. Our
starting point is the versatile process introduced by Neuts (1979) which, under some simplified
notation, was coined as the batch Markovian arrival process (BMAP). On the one hand, a general
point process can be approximated by appropriate MAPs and, on the other hand, the MAPs
provide a versatile, yet tractable option for modelling a bursty flow by preserving the Markovian
formalism. While a number of well-known arrival processes are subsumed under a BMAP as
special cases, the literature also shows generalizations to model arrival streams with marks, nonhomogeneous
settings or even spatial arrivals. We survey on the main aspects of the BMAP,
discuss on some of its variants and generalizations, and give a few new results in the context of a
recent state-dependent extension.Peer Reviewe
Supporting Large Scale Collaboration and Crowd-Based Investigation in Economics: A Computational Representation for Description and Simulation of Financial Models
Finance should be studied as a hard science, where scientific methods apply. When a trading strategy is proposed, the underlying model should be transparent and defined robustly to allow other researchers to understand and examine it thoroughly. Any reports on experimental results must allow other researchers to trace back to the original data and models that produced them.
Like any hard sciences, results must be repeatable to allow researchers to collaborate and build upon each otherâs results. Large-scale collaboration, when applying the steps of scientific investigation, is an efficient way to leverage crowd science to accelerate research in finance.
Unfortunately, the current reality is far from that. Evidence shows that current methods of investigation in finance in most cases do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of financial decisions at all levels, from personal investment choices to overreaching global economic policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and true is blurred.
In this research, we advocate the use of a next-generation investigative approach leveraging forces of human diversity, micro-specialized crowds, and proper computer-assisted control methods associated with accessibility, reproducibility, communication, and collaboration. This thesis is structured in three distinctive parts.
The first part defines a set of very specific cognitive and non-cognitive enablers for crowd-based scientific investigation: methods of proof, large-scale collaboration, and a domain-specific computational representation. These enablers allow the application of procedures of structured scientific investigation powered by crowds, a âcollective brain in which neurons are human collaboratorsâ.
The second part defines a specialized computational representation to allow proper controls and collaboration in large-scale in the field of economics. A computational representation is a role-based representation system based on facets, contributions, and constraints of data, and used to define concepts related to a specific domain of knowledge for crowd-based investigation.
The third and last part performs an end-to-end investigation of a non-trivial problem in finance by measuring the actual performance of a momentum strategy in technical analysis, applying formal methods of investigation developed over the first and second part of this research
Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University
Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIsâ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the âCritical Infrastructure/Modelling, Simulation and Analysis â Dataâ. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance.
In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: âCritical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuityâ.
The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet
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