Markov-based Approach Research Articles

Markov-Based Reliability Assessment for Distribution Systems Considering Failure Rates

Switches (SWs) and renewable distributed generations (RDG) are subject to failure as well as other components of a system that are usually presumed fully reliable in the literature. It can lead to an overestimation of their contribution to reliability enhancement disregarding their dysfunction. This study considers the failure rate of SWs and RDGs to assess their impact on the reliability cost of the system and their effect on the optimal placement using a Markov-based approach. The optimal placement of SWs and RDGs in radial distribution systems is carried out simultaneously aim at minimizing the overall cost including investment, maintenance of SWs, and RDGs alongside the interruption cost. The performance of the model is verified through different scenarios and tested on RBTS bus 2. The results show that high values of the failure rates lead to fewer allocations to prevent plummeting the reliability of the system and increasing the capacity of the RDG results in fewer SWs allocation.

A Comprehensive Life-Cycle Cost Analysis Approach Developed for Steel Bridge Deck Pavement Schemes

This study aims to evaluate the economy of a steel bridge deck pavement scheme (SBDPS) using a comprehensive life-cycle cost (LCC) analysis approach. The SBDPS are divided into the “epoxy asphalt concrete system” (EA system) and “Gussasphalt concrete system” (GA system) according to the difference in the material in the lower layer of the SBDPS. A targeted LCC checklist, including manager cost and user cost was proposed, and a Markov-based approach was applied to establish a life-cycle performance model with clear probability characteristics for SBDPS. Representative traffic conditions were designed using a uniform design method, and the LCC of SBDPS under representative traffic conditions and different credibility (construction quality as a random factor) was compared. The reliability of the LCC analysis approach was verified based on the uncertainty analysis method. Based on an expert-scoring approach, a user cost weight was obtained to ensure it is considered reasonably in the LCC analysis. Compared with the cumulative traffic volume, the cumulative equivalent single axle loads (CESAL) have a closer relationship with the LCC. The GA system has better LCC when the CESAL is less, while the EA system is just the opposite. The breaking point of CESAL for the LCC of the EA system and the GA system is 15 million times. The LCC analysis of SBDPS should consider the influence of random factors such as construction quality. The comprehensive LCC analysis approach in this paper can provide suggestions for bridge-management departments to make a reasonable selection on SBDPS.

Comparative performance of regression and the Markov based approach in the prediction of the future condition of a water distribution pipe network amidst data scarce situations: a case study of Kampala water, Uganda

AbstractGlobally, water utilities are grappling with the challenge of predicting the condition of deteriorating pipe network infrastructure amidst financial constraints and data-scarce scenarios. As a result, new innovative approaches such as statistical regression and Markov-based approaches have been introduced to aid water distribution pipe renewal decision making. However, comparison of the performance of these models under limited data has not been undertaken so far. In addition, the models have been applied elsewhere, in different environments and data availability scenarios. This paper addresses therefore the mentioned research gap and compares the performance of statistical regression and Markov models in the prediction of a condition of a pipe in a developing country. In addition, the criticality analysis of a block is studied. The data used for assessment is from Kampala water, the largest area in the National Water and Sewerage Corporation, Uganda. The results show that 78.26% of the prediction of the regression model is accurate in comparison to 88.4% for the Markov model. This means that the Markov-based approach is more superior than a regression model in a data scarce scenario. The approach will go a long way in helping water utilities in development of water decision pipe renewal plan amidst a limited budget and in data scarce scenarios.

Multi-touch attribution: A case study in automotive media optimisation

Multi-touch attribution is a foundation of proper reporting and media optimisation. In the absence of e-commerce, an alternative to direct sales must be developed in order to properly value the credit of each visitor touched by media. This paper uses an automotive marketing case study to illustrate how a Markov-based approach to attribution can be applied to customer digital interactions to form a causal relationship with sales. Once the relationship between consumer digital behavioural data is combined with the brand’s media executions, media optimisation including saturation analysis is enabled.

Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures

The understanding of resilience is an emerging topic within the study of risks affecting distributed infrastructure systems. Although recent studies have explored the quantification of system resilience, there has been limited research aimed at understanding the role of multiple performance measures, spatiotemporal heterogeneities, and modeling uncertainties within the assessment of resilience and associated decision-making. Under real-world conditions, there is an increased burden on analysts for translating observed system data (including human and electronic sensor observations) into system performance estimates that may not be directly observable. This paper addresses these issues using a scenario-based risk modeling approach to understand: (1) resilience of complex systems, often in cases of hidden (not readily observable) measures of performance, (2) resilience sensitivity to modeling uncertainties in event and system characteristics, and (3) resilience sensitivity to the measurement of performance across multiple operational perspectives. The methods in this paper integrate uncertainty-driven risk and probabilistic modeling within a multi-state Markov-based approach. This study contributes to the state-of-the-art by developing methodologies for assessing community perceptions of infrastructure system resilience using observable factors and inferring possibly hidden performance measures for facilitating adaptive decision-support. The methods are demonstrated with hypothetical spatiotemporal data across multiple system performance dimensions. The analysis results are useful for infrastructure security analysts, system decision-makers, and the general public.

Multi-objective optimization of reliability–redundancy allocation problem with cold-standby strategy using NSGA-II

The reliability–redundancy allocation problem (RRAP) involves the selection of components reliability and redundancy levels, with the aim of maximizing system reliability. Generally, RRAPs are formulated with the assumption of active redundancy strategy. In this paper, the problem is formulated by considering a cold-standby strategy. In most practical cases, complicated decision-making problems are encountered due to the presence of several conflicting objectives such as maximizing system reliability and simultaneously minimizing the total cost, weight or volume of the systems. Therefore, it is essential to consider these conflicting criteria as the main objectives of the optimization problem. Hence, in this paper, a bi-objective RRAP is developed. The proposed mathematical model is developed for three benchmark problems and solved by using a multi-objective evolutionary algorithm (NSGA-II). In order to calculate the exact reliability values for the problems, a recently introduced Markov-based approach is used. The results indicate that, in comparison with recent studies, the proposed NSGA-II shows superior performance and leads to better results in all benchmark problems. In other words, for each problem, a variety of structures are presented which outperform the best active structure and at least one structure is obtained that outperforms the best standby one in the literature.

Mining Object Similarity for Predicting Next Locations

Next location prediction is of great importance for many location-based applications. With the virtue of solid theoretical foundations, Markov-based approaches have gained success along this direction. In this paper, we seek to enhance the prediction performance by understanding the similarity between objects. In particular, we propose a novel method, called weighted Markov model (weighted-MM), which exploits both the sequence of just-passed locations and the object similarity in mining the mobility patterns. To this end, we first train a Markov model for each object with its own trajectory records, and then quantify the similarities between different objects from two aspects: spatial locality similarity and trajectory similarity. Finally, we incorporate the object similarity into the Markov model by considering the similarity as the weight of the probability of reaching each possible next location, and return the top-rankings as results. We have conducted extensive experiments on a real dataset, and the results demonstrate significant improvements in prediction accuracy over existing solutions.

A simplified Markov-based approach for safety integrity level verification

Safety integrity level (SIL) verification is a critical step in the life cycle of safety instrumented systems (SIS). For the chemical process industry (CPI), SIL verification often means calculation of average probability of failure on demand (PFDavg) of a SIS. Markov analysis covers most aspects of the quantitative safety evaluation and shows great flexibility. However, for a complex SIS, the model building is so cumbersome that industrial users always try to avoid using it. A simplified approach by conducting Markov analysis on each channel of a SIS with any type of architecture and combining the results together is proposed in this paper. A case study is performed and the results prove that the simplified approach can simplify Markov modeling without loss of accuracy if a proper common cause failure (CCF) model is adopted.

Combinatorial Reliability Analysis of Imperfect Coverage Systems Subject to Functional Dependence

Functional dependence occurs when the failure of one component causes other components within the same system to become inaccessible or unusable. It is one of the dynamic behaviors that have been recognized in the dynamic fault tree analysis, where a dynamic gate called FDEP was designed to model such behavior. Traditional approaches to handling functional dependence in the reliability analysis of fault-tolerant systems with imperfect fault coverage are mainly based on Markov models, which are often computationally intensive, and even intractable due to the well-known state space explosion problem. In addition, the Markov-based approaches are typically restricted to exponential time-to-failure distributions for system components. In this paper, a combinatorial, separable method based on the divide-and-conquer paradigm and total probability theorem is proposed for addressing the above problems. The proposed method obviates the use of inefficient Markov models, offering exact, computationally-efficient solutions to the reliability analysis of imperfect coverage systems subject to functional dependencies. The proposed method is applicable to the analysis of large systems with any arbitrary time-to-failure distributions. Several case studies are given to illustrate the application and advantages of the proposed method.

Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach

Fatigue influences the way a training exercise is performed and alters the kinematics of the movement. Monitoring the increase of fatigue during rehabilitation and sport exercises is beneficial to avoid the risk of injuries. This study investigates the use of a parametric hidden Markov model (PHMM) to estimate fatigue from observing kinematic changes in the way the exercise is performed. The PHMM is compared to linear regression. A top-level hidden Markov model with variable state transitions incorporates knowledge about the progress of fatigue during the exercise and the initial condition of a subject. The approach is tested on a squat database recorded with optical motion capture. The estimates of fatigue for a single squat, a set of squats, and an entire exercise correlate highly with subjective ratings.

Discrete-time GI/G/1 retrial queues with time-controlled vacation policies

A discrete-time GI/G/1 retrial queue with Bernoulli retrials and time-controlled vacation policies is investigated in this paper. By representing the inter-arrival, service and vacation times using a Markov-based approach, we are able to analyze this model as a level-dependent quasi-birth-and-death (LDQBD) process which makes the model algorithmically tractable. Several performance measures such as the stationary probability distribution and the expected number of customers in the orbit have been discussed with two different policies: deterministic time-controlled system and random time-controlled system. To give a comparison with the known vacation policy in the literature, we present the exhaustive vacation policy as a contrast between these policies under the early arrival system (EAS) and the late arrival system with delayed access (LAS-DA). Significant difference between EAS and LAS-DA is illustrated by some numerical examples.

Competing failure analysis in phased-mission systems with functional dependence in one of phases

This paper proposes an algorithm for the reliability analysis of non-repairable phased-mission systems (PMS) subject to competing failure propagation and isolation effects. A failure originating from a system component which causes extensive damage to other system components is a propagated failure. When the propagated failure affects all the system components, causing the entire system failure, a propagated failure with global effect (PFGE) is said to occur. However, the failure propagation can be isolated in systems subject to functional dependence (FDEP) behavior, where the failure of a component (referred to as trigger component) causes some other components (referred to as dependent components) to become inaccessible or unusable (isolated from the system), and thus further failures from these dependent components have no effect on the system failure behavior. On the other hand, if any PFGE from dependent components occurs before the trigger failure, the failure propagation effect takes place, causing the overall system failure. In summary, there are two distinct consequences of a PFGE due to the competition between the failure isolation and failure propagation effects in the time domain. Existing works on such competing failures focus only on single-phase systems. However, many real-world systems are phased-mission systems (PMS), which involve multiple, consecutive and non-overlapping phases of operations or tasks. Consideration of competing failures for PMS is a challenging and difficult task because PMS exhibit dynamics in the system configuration and component behavior as well as statistical dependencies across phases for a given component. This paper proposes a combinatorial method to address the competing failure effects in the reliability analysis of binary non-repairable PMS. The proposed method is verified using a Markov-based method through a numerical example. Different from the Markov-based approach that is limited to exponential distribution, the proposed approach has no limitation on the type of time-to-failure distributions for the system components. A case study is given to illustrate such advantage of the proposed method.

Behavioural modelling of career progression in the European Commission

Though manpower planning models have been part of OR for many years, and simulation has always been acknowledged as a potential approach, there are few reported applications of its use. In this case-based study we report on a micro-simulation model that exploits the structure of the European Commission’s appraisal and promotion rules, and includes regression-based sampling schemes which allow for non-Normal error terms to represent behavioural factors that led to the need for a new system. With a suitably parsimonious formulation the 20,000 person model runs very effectively, and the transparency associated with simulation proves an important factor in the successful use of the model as the basis for designing a promotion box system that was implemented across the Commission in 2009. The simulation modelling incorporates many Markov-type elements, and we reflect on important lessons learned from this combined use of micro-simulation and Markov-based approaches to manpower modelling.

Discovery of high-level tasks in the operating room

Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a framework for recognizing high-level tasks from low-level noisy sensor data. Specifically, we present a Markov-based approach for inferring high-level tasks from a set of low-level sensor data. We also propose to clean the noisy sensor data using a Bayesian approach. Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%. Introducing missed and ghost errors to the sensor data results in a significant decrease of the recognition accuracy. This supports our claim to use a cleaning algorithm before the training step. Finally, we highlight exciting research directions in this area.

Markov-Based Lane Positioning Using Intervehicle Communication

The majority of today's navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle's position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm's effectiveness is shown in both simulations and experiments with real data.

Adaptive multiresolution search: How to beat brute force?

Multiresolution and wavelet-based search methods are suited to problems for which acceptable solutions are in regions of high average local fitness. In this paper, two different approaches are presented. In the Markov-based approach, the sampling resolution is chosen adaptively depending on the fitness of the last sample(s). The advantage of this method, behind its simplicity, is that it allows the computation of the discovery probability of a target sample for quite large search spaces. This permits to “reverse-engineer” search-and-optimization problems. Starting from some prototypic examples of fitness functions the discovery rate can be computed as a function of the free parameters. The second approach is a wavelet-based multiresolution search using a memory to store local average values of the fitness functions. The sampling density probability is chosen per design proportional to a low-resolution approximation of the fitness function. High average fitness regions are sampled more often, and at a higher resolution, than low average fitness regions. If splines are used as scaling mother functions, a fuzzy description of the search strategy can be given within the framework of the Takagi–Sugeno model.

Theoretical analysis of bus-invert coding

The closed-form formulas derived from the Markov chains depicting the bus-invert coding processes for even and odd bus widths are employed to compute switching activity and weight per bus transfer, to explain the diminishing returns with increasing bus width and to show the nonviability of partitioning a bus into smaller buses of odd number bits, etc. Probing into the codewords generated by the coding process, we obtain the same closed-form formulas as that obtained by the Markov-based approach. Our contributions form a superset over the previously published theoretical results and provide comprehensive background information for exploiting the bus-invert method for low-power applications.