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The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs.

The method is grounded on specific theorems used to design an algorithm for systematic construction of multi-state repairable systems models, whatever is their size.

Stop and constraint functions were derived from these theorems and allow to considering k-out-of-n structure systems and to identifying the minimal cut sets, useful to monitoring the states evolution of the system.

The properties of this model will be studied, and new investigations will help to demonstrate the feasibility of the approach in real world, and more complex structure will be considered.

The simulation models based on CSPN can be used as a tool by maintenance decision makers, for prediction of the effectiveness of maintenance strategies.

The proposed approach and model provide an efficient tool for advanced investigations on the development and implementation of maintenance policies and strategies in real life.

Renewable generation is a main component of most hybrid generation systems. However, randomness on its generation is a characteristic to be considered due to its direct impact on reliability and performance of these systems. For this reason, renewable generation usually is accompanied with other generation elements to improve their general performance. The purpose of this paper is to analyze the power generation system, composed of solar, wind and diesel generation and power outsourcing option from the grid as means of reserve source. A multi-objective optimization for the design of hybrid generation system is proposed, particularly using the cost of energy, two different reliability indexes and the percentage of renewable energy as objectives. Further, the uncertainty of renewable sources and demand is modeled with a new technique that permits to evaluate the reliability quickly.

The multi-state model of the generators and the load is modeled with the Universal Generating Function (UGF) to estimate the reliability indexes for the whole system. Then an evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm) is used to solve the multi-objective optimization model.

The use of UGF methodology reduces the computation time, providing effective results. The validation of reliability assessment of hybrid generation systems using the UGF is carried out taking as a benchmark the results obtained with the Monte Carlo simulation. The proposed multi-objective algorithm gives as a result different generators combinations that outline hybrid systems, where some of them could be preferred over others depending on its results for each independent objective. Also it allows us to observe the changes produced on the resulting solutions due to the impact of the power fluctuation of the renewable generators.

The main contributions of this paper are: an extended multi state model that includes different types of renewable energies, with emphasis on modeling of solar energy; demonstrate the performance improvement of UGF against SMC regarding the computational time required for this case; test the impact of different multi-states numbers for the representation of the elements; depict through multi-objective optimization, the impact of combining different energies on the cost and reliability of the resultant systems.

The reliability and maintainability of tunnel infrastructure and systems is an important factor in assuring normal operation of a tunnel. Evaluating availability of a large‐scale tunnel that includes civil, electrical, mechanical and electronic systems is a difficult task. The purpose of this paper is to present a methodology for performing such assessments, featuring the use of the Markov model.

The methodology involves application of failure mode, effect and criticality analysis (FMECA), state space diagram construction, formulation of state space equations, and development of transitional matrices. It also involves transformation of multi‐state models into two‐state models (each comprises of an “up” state and a “down” state) through the use of the frequency and duration method for determining the failure and repair rates, as well as the mean‐time‐between‐failures (MTBF) of the entire tunnel. By using the proposed bottom‐up approach, a MTBF tree linking the availability measures of individual equipment with those of sub‐systems, and ultimately the whole tunnel can be developed.

The tunnel availability measures obtained by this analysis can be used in making comparisons between different tunnel designs so as to determine the value for money of various options. Furthermore, weaknesses in a tunnel design can be identified in the analysis. The information obtained from this method can also be used to evaluate adequacy, security and maintainability of a tunnel.

The reliability and maintainability of tunnel infrastructure and systems are crucial factors for ensuring safety of tunnel operation. Unsafe conditions will cause closure of a tunnel. Efforts to improve availability of a tunnel often increase the tunnel's construction cost. Due to the complexity of tunnel systems, it is difficult to compare different tunnel designs, and trade‐off analyses to strike a balance between target availability and construction cost of a tunnel design are seldom performed. This paper presents a systematic methodology to address these issues. This methodology allows tunnel management to evaluate the adequacy, security and maintainability of a tunnel so that design weaknesses can be identified and the value of design improvements can be determined. The methodology can also be used to evaluate designs of other complex systems such as power generation or petrochemical processing plants.

A worked example demonstrating the application of the proposed methodology is presented in this paper.

The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs).

The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata.

Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified.

For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity.

For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation.

The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

The purpose of this paper is to focus on finding the optimal maintenance interval and the minimum maintenance cost for redundant system, considering environment factors.

The authors propose a decision model with environment-based preventive maintenance for the repairable redundant system. Referring to the k-out-of-n model and Proportional Hazard Model, the reliability analysis is completed for the redundant system affected by internal and external issues. Meanwhile, the maintenance cost for the redundant system is divided into two categories: the fixed maintenance cost involving whole system replacement at the time of system failure, and the cost to replace failure components when the system still functions.

Upon the required reliability analysis, an optimal maintenance interval that minimizes the average maintenance cost per unit time is identified. The simulation results indicate that the optimal maintenance interval with consideration of environmental factors is significantly shorter than that without consideration of these factors, with the maintenance cost increase within 10 percent.

The redundant systems have widely been used in industries including the aero craft control system and warship power system. The model could be applied in the more real case considering the types of components and the operation environment, and help production managers better maintain machines by increasing the safety and reliability of the redundant model with the more frequent inspection.

Previous research of redundant system always focuses on internal degradation, while ignoring the reliability analysis for a redundant system with various multiple components under the influence of environment. However, this work could fill the theoretical gap, i.e. simultaneously consider both environmental and internal factors for a redundant system with non-homogeneous components. Meanwhile, the proposed superior model increases the reliability and safety of the k-out-of-n model with reasonable cost. Production managers could benefit a lot from this as well.

Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC.

In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling.

Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively.

The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

The purpose of this paper is to evaluate the reliability indices such as reliability, mean time to failure and sensitivity analysis.

A non-repairable multi-state complex system with two subsystems, namely, S₁ and S₂, is studied. The subsystems S₁ and S₂ are multi-state weighted r-out-of-n: G and s-out-of-m: G systems, respectively. Every component of the sub-system S₁ consists of linear (f, l, k): G system, and the sub-system S₂ consists of circular (f, l, k): G system. The subsystems are connected in series arrangement and components of these subsystems are arranged in parallel.

Markov stochastic process has been applied to obtain probability of components of the subsystems and various results discuss such as reliability, mean time to failure and sensitivity analysis with the help of the universal generating function.

In this work, reliability measures of the purpose system can be enhanced under the high profit.

The FDIC Improvement Act of 1991 sets out five categories of capital and mandates corrective action for banks. Each bank based on its capital amount fall in the certain categories or states. The purpose of this paper is to consider the effect of banking regulations and supervisory practices on capital state transition.

First, the authors investigate how much the practices influence banks' capital adequacy using a dynamic panel data method, the generalized method of moments. Then, to scrutinize the results of the first phase, the authors estimate the effect of practices on some characteristics of capital state transition such as transition intensity, transition probability and state sojourn time using multi-state models for panel data in 107 developing countries over the period 2000 to 2012.

The dynamic regression results show that capital guidelines, supervisory power and supervisory structure can have significantly positive effects on the capital adequacy state. Moreover, the multi-state Markov panel data model estimation results show that the significantly positive-effect practices can change the capital state transition intensity considerably; for example, they can transmit the critical-under-capitalized (the lowest) capital state of banks directly to a well or the adequate-capitalized (the highest) capital state without passing through middle states (under-capitalized and significantly-undercapitalized). Moreover, the results present some new evidence on transition probability and state sojourn time.

The main contribution of this paper, unlike the existing literature, is to consider the power of banking regulations and supervisory practices to improve the capital state using a multi-state Markov panel data model.

A multi-state linear k-within-(r, s)-of-(m, n): F lattice system consists of m×n components arranged in m rows and n columns. The possible states of the system and its components are: 0, 1, 2, …, H. According to k values, the system can be categorized into three special cases: decreasing, increasing and constant. The system reliability of decreasing and constant cases exists. The purpose of this paper is to evaluate the system reliability in increasing case with i.i.d components, where there is no any algorithm for evaluating the system reliability in this case.

The Boole-Bonferroni bounds were applied for evaluating the reliability of many systems. In this paper, the author reformulated the second-order Boole-Bonferroni bounds to be suitable for the evaluation of the multi-state system reliability. And the author applied these bounds for deriving the lower bound and upper bound of increasing multi-state linear k-within-(r, s)-of-(m, n): F lattice system.

An illustrated example of the proposed bounds and many numerical examples are given. The author tested these examples and concluded the cases that make the new bounds are sharper.

In this paper, the author considered an important and complex system, the multi-state linear k-within-(r, s)-of-(m, n): F lattice system; it is a model for many applications, for example, telecommunication, radar detection, oil pipeline, mobile communications, inspection procedures and series of microwave towers systems.

This paper suggests a method for the computation of the bounds of increasing multi-state linear k-within-(r,s)-of-(m,n): F lattice system. Furthermore, the author concluded that the cases that make these bounds are sharper.

The purpose of this paper is to deal with a linear multi-state sliding window coherent system which generalizes the consecutive k-out-of-r-from-n:F system in the multi-state case. The system has n linearly ordered multi-state elements consisting of m parallel independent and identically distributed elements. Every element of the system can have two states: completely working or totally failed. The system fails if the sum of performance rate is lower than the given weight.

The authors proposed to compute the signature, MTTF and Barlow–Proschan index with the help of UGF technique of multi-state SWS which consists of m parallel i.i.d. components in each multi-state window.

In the present study, the authors have evaluated the signature reliability, expected lifetime, cost analysis and Barlow–Proschan index.

In this study, the authors have studied a linear multi-state sliding window system which consists of n ordered multi-state element, and each multi-state element also consists of m parallel windows. The focus of the present paper is to evaluate reliability metrices of the considered system with the help of signature from using the universal generating function.