Reliability Of Engineering Systems Research Articles

Analysis of the Computing Methods of Structural Reliability of Process Flow Diagrams for the Power Generating Units of Nuclear Power Plants

This paper gives consideration to the methods used for the computation of the reliability of complicated engineering systems that allow us to specify the probability of failure-free operation of the equipment used by the power generating units of NPP, in particular the enumerating technique of states, the methods of minimal ways and minimal cross-sections, the reliability computing method based on the expansion of a relatively ²special² elements, and the ²fault tree² construction method. The reliability computing method based on the enumerating technique of states is grounded on the logic algebra tool and uses the computing methods of the reliability indices of series, parallel and combined connections in bridge circuits. When implementing the reliability computing method based on minimum ways and cross-sections minimum ways should be determined first. Then minimum cross-sections are determined, i.e. such options of element ways that in case of the failure of one element cause failure of the entire diagram in spite of the functionality of other ways. The reliability computing method based on the expansion of a relatively ²special² element provides for the isolation of one (²special²) element in the calculable system that is unconditionally faultless (the probability of the operable condition is equal to the unit) or unconditionally fault (the probability of the operable condition is equal to the zero). The analysis of the ²fault tree² consists in the determination of such a combination of elements whose simultaneous failure results in the knot of tree, i.e. in the failure of the entire calculable system. In particular cases the set of elements is possible whose failure results in the failure of entire system. The knots of tree are the logic operations of an ²AND² or ²OR² type (that define the product or the sum of events).

ВЛИЯНИЕ ПОЛНОТЫ ИСПОЛЬЗУЕМОЙ МОДЕЛИ ТЕХНИЧЕСКОЙ СИСТЕМЫ НА ТОЧНОСТЬ И ДОСТОВЕРНОСТЬ ОЦЕНКИ НАДЕЖНОСТИ

An overall purpose of researches – improvement of assessment methods of engineering systems reliability at the stage of design.
 To achieve the end specified within the bounds of the investigation there was established that one of the most significant factors defining the effectiveness of the reliability assessment at the stage of designing is the completeness of a model used. For the assessment of model completeness by the example of a failure tree it was offered to determine a correlation between levels of a logical model of reliability and kinds of engineering consistency regulated by RSS 30709-2002 “Engineering Consistency. Terms and Definitions”. Within the bounds of the paper there is analyzed a minimum set of the sorts engineering consistency (dimensional consistency of system elements, compatibility of ele-ments according to reliability, interoperability) the account of which requires various degrees of detailed elaboration of a model and supposes the existence of corresponding source data including those established at the identification of logic connections between failures and the analysis of possible failures caused by a common reason.
 For the systematization of research results there is developed a matrix of correlation of analyzed kinds of engineering consistency with the levels of a failure tree and the values of the assessment of a quadratic means of deviation (QMD) of expected results. The mathematical dependences allowing the definition of QMD values at every level of the logic model of relia-bility are developed. To account for the progressive-ness of a QMD value at the decrease of assessment reliability by analogy with Taguchi function of losses it is offered to use a parabolic dependence. The approach offered is particularly urgent for methods where the accuracy of results obtained determines the degree of a risk of manufacturers caused by wrong or untimely management or technical decision-makings. The trend of risk changes for manufacturers depending on QMD of a resultant value allows explaining diagrams presented in the paper. 
 The results obtained explain the dependence of accuracy and reliability of a reliability assessment on the completeness of use of the model and show a trend of the influence of parameters pointed out upon risk probability of manufacturers at the decisionmaking at the stage of designing.

Technological reliability of chemical engineering systems

A significant part of the gross domestic product is lost because of hitches followed by long downtime periods in industrial systems. This is a common problem in the industry of developed nations. Analysis of causes of this phenomenon allows developing a conception of solving this problem and suggesting a method of studying the reliability (working capacity) of chemical-engineering systems (CES). In this article we prove the need for technological reliability analysis tools in prefeasibility study to estimate the potential working capacity of the technology and to avoid the large costs of starts and stops.

Dynamic safety analysis of process systems using nonlinear and non-sequential accident model

Analysis of the safety and reliability of complex engineering systems is becoming challenging and highly demanding. In complex engineering systems, accident causation is a function of nonlinear interactions of several accident contributory factors. Traditional accident models normally use a fault and event trees sequential approach to predict cause–consequence relationships, which unable to capture real interaction thus have limited predictability of accident.This paper presents a new non-sequential barrier-based process accident model. The conditional dependencies among accident contributory factors within prevention barriers are modelled using the Bayesian network with various relaxation strategies, and non-sequential failure of prevention (safety) barriers. The modelling of non-linear interactions in the model led to significant improvement of the predicted probability of an accident when compared with that of sequential technique. This renders valuable information for process safety management. The proposed accident model is tested on a real life case study from the U.S. Chemical Safety Board.

Analysis of complex system reliability with correlated random vectors

The analysis of reliability of complex engineering systems remains a challenge in the field of reliability. It will be even more difficult if correlated random vectors are involved, which is generally the case as practical engineering systems invariably contain parameters that are mutually correlated. A new method for transforming correlated distributions, involving the Nataf transformation, is proposed that avoids the solution of integral equations; the method is based on the Taylor series expansion of the probability density function (PDF) of a bivariate normal distribution resulting in an explicit polynomial equation of the equivalent correlation coefficient. The required numerical results can be obtained efficiently and accurately.The proposed method for transformation of correlated random vectors is useful for developing a method for system reliability including complex systems with correlated random vectors. Based on the complete system failure process (originally defined as the development process of nonlinearity) and the fourth-moment method, the analysis of system reliability for elastic-plastic material avoids the identification of the potential failure modes of the system and their mutual correlations which are required in the traditional methods. Finally, four examples are presented – two examples to illustrate the potential of the new method for transformation of correlated random vectors, and two examples to illustrate the application of the proposed more effective method for system reliability.

Time-domain multi-state markov model for engine system reliability analysis

A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuous-time Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

Some criteria of critical infrastructures stability

Critical Infrastructures (CIs) are complex systems with a set of individual elements, such as buildings and facilities, technological equipment and energy supply networks, engineering, technology and other communications. The system criteria of Critical Infrastructures stability and their threshold are shown in the paper. The main criterion of CIs protection is engineering and functional stability. And the special criteria are the next: the criterion of work safety; the criterion of environmental safety; the criterion of energy safety; the criterion of resource safety (resource using in CIs); the criterion of CIs’ automation level; the criterion of technological equipment safety; the criterion of reliability of project design solution; the criterion of engineering systems reliability; the criterion of engineering safety; the criterion of explosion and fire safety. The algorithm for evaluation of Critical Infrastructures stability is formulated in this paper.

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presence of mixed (aleatory and multiple sources of epistemic) uncertainty to the reliability and performance assessment of complex engineering systems through the use of quantification of margins and uncertainties (QMU) methodology. This study focuses on quantifying the simulation uncertainties, both in the design condition and the performance boundaries along with the determination of margins. To address the possibility of multiple sources and intervals for epistemic uncertainty characterization, DSTE is used for uncertainty quantification. An approach to incorporate aleatory uncertainty in Dempster–Shafer structures is presented by discretizing the aleatory variable distributions into sets of intervals. In view of excessive computational costs for large scale applications and repetitive simulations needed for DSTE analysis, a stochastic response surface based on point-collocation non-intrusive polynomial chaos (NIPC) has been implemented as the surrogate for the model response. The technique is demonstrated on a model problem with non-linear analytical functions representing the outputs and performance boundaries of two coupled systems. Finally, the QMU approach is demonstrated on a multi-disciplinary analysis of a high speed civil transport (HSCT).

Parametric Sensitivity Analysis for Importance Measure on Failure Probability and Its Efficient Kriging Solution

The moment-independent importance measure (IM) on the failure probability is important in system reliability engineering, and it is always influenced by the distribution parameters of inputs. For the purpose of identifying the influential distribution parameters, the parametric sensitivity of IM on the failure probability based on local and global sensitivity analysis technology is proposed. Then the definitions of the parametric sensitivities of IM on the failure probability are given, and their computational formulae are derived. The parametric sensitivity finds out how the IM can be changed by varying the distribution parameters, which provides an important reference to improve or modify the reliability properties. When the sensitivity indicator is larger, the basic distribution parameter becomes more important to the IM. Meanwhile, for the issue that the computational effort of the IM and its parametric sensitivity is usually too expensive, an active learning Kriging (ALK) solution is established in this study. Two numerical examples and two engineering examples are examined to demonstrate the significance of the proposed parametric sensitivity index, as well as the efficiency and precision of the calculation method.

Mathematical Applications to Reliability and Maintenance Problems in Engineering Systems

Mathematical Applications to Reliability and Maintenance Problems in Engineering Systems

Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station

A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process.

Stochastic uncertainty modelling for ship design loads and operational guidance

Understanding and quantification of uncertainties are important attributes for the assessment of the performance, reliability and risk of complex engineering structures and systems. From a naval architecture perspective the consideration of the uncertainties related to ship’s seakeeping responses and wave induced loads is necessary for the assessment of ship design as well as for safe and efficient ship operations. To this end, the efficient processing of large amounts of data and the stochastic load combination by use of available numerical models may assist with the rationalisation of modelling assumptions and support the validation and verification of design and decision making criteria in the context of risk based ship design. This paper presents recent advances in (a) modelling the combined hydrodynamic responses of ship structures using cross-spectral combination methods and (b) in implementing uncertainty models used for the development of modern decision support systems as guidance to ship’s master.

Transient analysis of hardware and software systems with warm standbys and switching failures

Redundancy or standby is a provision which plays an important role in improving the reliability of engineering systems. This investigation deals with the reliability and sensitivity analysis of a repairable system (consisting of hardware and software components) with warm standbys and switching failures. The failure and repair rates of the components are assumed to follow exponential distributions. By using Markovian property, the transient state model is developed to establish the system reliability and other performance measures. Numerical technique-based matrix method is used to compute various system performance indices. A numerical example is provided to illustrate the tractability of the proposed method.

Value of information analysis with structural reliability methods

When designing monitoring systems and planning inspections, engineers must assess the benefits of the additional information that can be obtained and weigh them against the cost of these measures. The value of information (VoI) concept of the Bayesian statistical decision analysis provides a formal framework to quantify these benefits. This paper presents the determination of the VoI when information is collected to increase the reliability of engineering systems. It is demonstrated how structural reliability methods can be used to effectively model the VoI and an efficient algorithm for its computation is proposed. The theory and the algorithm are demonstrated by an illustrative application to monitoring of a structural system subjected to fatigue deterioration.

Predicting reliability and failures of engine systems by single multiplicative neuron model with iterated nonlinear filters

Failures and reliability prediction in engine systems have attracted much attention over the past decades. Soft computing techniques have been widely studied and successfully applied to engine system reliability prediction with the ability to deal with high non-linearity. However, the development of conventional soft computing techniques involves many difficulties such as the selection of inputs to the network, the selection of network structure and the calculation of model parameters. The single multiplicative neuron (SMN) model is found to be a viable alternative due to its advantages of better approximation capabilities, simpler network structures and faster learning algorithms. Furthermore, nonlinear filters can deal with additive noises and can update model parameters when a new observation data arrives due to their iterative algorithm structure. In this study, novel techniques based on the SMN model with iterated nonlinear filtering online training algorithms for engine systems reliability prediction are proposed. Illustrative examples taken from the previous literature are used to demonstrate the detailed implementation procedures and the corresponding results are compared with the existing developed models. The experimental results reveal that the proposed models result in better prediction results than the existing methods.

A fuzzy reliability assessment of basic events of fault trees through qualitative data processing

Probabilistic approaches are common in the analysis of reliability of complex engineering systems. However, they require quantitative historical failure data for determining reliability characteristics. In many real-world areas, such as e.g., nuclear engineering, quantitative historical failure data are unavailable or become inadequate and only qualitative data such as expert opinions, which are described in linguistic terms, can be collected and then used to assess system reliability. Moreover, experts are more comfortable justifying event failure likelihood using linguistic terms, which capture uncertainties rather than by expressing judgments in a quantitative manner. New techniques are therefore needed that will help construct models of reliability of complex engineering system without being confined to quantitative historical failure data. The objective of this study is to develop a fuzzy reliability algorithm to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing. The originality of this study comes with an introduction of linguistic values articulated in terms of component failure possibilities in order to qualitatively assess basic event failure possibilities treated as inputs of the proposed model and generate basic event failure probabilities as its outputs. To demonstrate the feasibility and effectiveness of the proposed algorithm, actual basic event failure probabilities collected from nuclear power plant operating experiences are compared with the failure probabilities generated by the algorithm. The results demonstrate that the proposed fuzzy reliability algorithm arises as a suitable alternative for the probabilistic reliability approach when quantitative historical failure data are unavailable.

Application of Support Vector Machine to Reliability Analysis of Engine Systems

Reliability analysis plays a very important role for assessing the performance and making maintenance plans of engine systems. This research presents a comparative study of the predictive performances of support vector machines (SVM) , least square support vector machine (LSSVM) and neural network time series models for forecasting failures and reliability in engine systems. Further, the reliability indexes of engine systems are computed by the weibull probability paper programmed with Matlab. The results shows that the probability distribution of the forecasting outcomes is consistent to the distribution of the actual data, which all follow weibull distribution and the predictions by SVM and LSSVM can provide accurate predictions of the characteristic life. So SVM and LSSVM are both another choice of engine system reliability analysis. Moreover, the predictive precise of the method based on LSSVM is higher than that of SVM. In small samples, the prediction by LSSVM will be more popular, because its compution cost is lower and the precise can be more satisfied. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2624 Full Text: PDF

Imprecise probabilities in engineering analyses

Probabilistic uncertainty and imprecision in structural parameters and in environmental conditions and loads are challenging phenomena in engineering analyses. They require appropriate mathematical modeling and quantification to obtain realistic results when predicting the behavior and reliability of engineering structures and systems. But the modeling and quantification is complicated by the characteristics of the available information, which involves, for example, sparse data, poor measurements and subjective information. This raises the question whether the available information is sufficient for probabilistic modeling or rather suggests a set-theoretical approach. The framework of imprecise probabilities provides a mathematical basis to deal with these problems which involve both probabilistic and non-probabilistic information. A common feature of the various concepts of imprecise probabilities is the consideration of an entire set of probabilistic models in one analysis. The theoretical differences between the concepts mainly concern the mathematical description of the set of probabilistic models and the connection to the probabilistic models involved. This paper provides an overview on developments which involve imprecise probabilities for the solution of engineering problems. Evidence theory, probability bounds analysis with p-boxes, and fuzzy probabilities are discussed with emphasis on their key features and on their relationships to one another.This paper was especially prepared for this special issue and reflects, in various ways, the thinking and presentation preferences of the authors, who are also the guest editors for this special issue.

Research on Hidden Failure Reliability Modeling of Electric Power System Protection

Aiming at digital relay protection system, a novel hidden failure Markov reliability model is presented for a single main protection and double main protection systems according to hidden failure and protection function under Condition-Based Maintenance (CBM) circumstance and reliability indices such as probability of protection system hidden failure state are calculated. Impacts of different parameters (containing impacts of human errors) to hidden failure state probability and the optimal measures to improve reliability by variable parameter method are also analyzed. It’s demonstrated here that: Compared to a single main protection, double main protection system has an increased hidden failure probability, thus the real good state probability decreases, two main protections’ reliability must be improved at the same time, so configuration of the whole protection system for the component being protected can’t be complicated. Through improving means of on-line self-checking and monitoring system in digital protection system and human reliability, the real application of CBM can decrease hidden failure state probability. Only through this way can we assure that the protection systems work in good state. It has a certain reference value to protection system reliability engineering.

A Novel Concept for the Reliability Evaluation of Large Systems

A general purpose novel reliability analysis method to estimate low probability of failure events for large complicated real nonlinear structures excited by dynamic loadings including seismic loading applied in time domain is presented. In this approach, only tens instead of hundreds or thousands of deterministic evaluations at intelligently selected points are used to extract the required reliability information. It is a significant improvement over other methods currently being developed in other disciplines. Several areas of response surface method are improved and advanced factorial design concepts are presented. The method is elaborated with the help of two informative examples. The proposed method found to be robust, efficient, and accurate. It can be used to estimate reliability of large complicated civil engineering systems.