System Reliability Model Research Articles

Reliability analysis of a steam boiler system by expert judgment method and best-fit failure model method: a new approach

PurposeIndia's textile industries play a vital role in the Indian economy. These industries consume the highest thermal energy (steam power). The demand of the steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. The purpose of this paper is to present a new approach for reliability analysis by expert judgment method.Design/methodology/approachA lack of adequate life data is one of the biggest challenge in the reliability analysis of mechanical systems. This research provides an expert judgment approach for assessing the boiler's reliability characteristics. For this purpose, opinions of experts on time to failure and time to repair data were elicited in the form of statistical distributions. In this work, reliability analysis of the boiler system is carried out by expert judgment method and by using best-fit failure model. The system reliability along with preventive maintenance intervals of all components is also evaluated.FindingsIt is observed that the reliability analysis results obtained by expert judgment method and best-fit failure model method indicate that there are no significant differences. Therefore, in case when insufficient data are available, the expert judgment method can be effectively used. The analysis shows that the feedwater tank, feedwater pump, supply water temperature sensor, strainer, return water temperature sensor, condensate filter, mechanical dust collector, coal crusher and fusible plug are identified as critical components from a reliability perspective, and preventive maintenance strategy is suggested for these components.Originality/valueIn this research paper, a system reliability model by the expert judgment method is developed, and it can be effectively used where insufficient failure data are available. This paper is useful for the comparative evaluation of reliability characteristics of a boiler system by expert judgment method and best-fit failure model method.

Reliability analysis of systems considering clusters of dependent degrading components

A new system reliability model is developed for complex multi-component systems when each component experiences multiple failure processes, and component degradation paths are stochastically dependent. The components degrade in clusters that behave similarly. Gamma process models are used to model the stochastic processes of individual component deterioration. Degradation paths of different components are clustered into different classes corresponding to different operational conditions or stresses. In this new model, hard and soft failure processes for each component are dependent due to simultaneous shared exposure to a shock process, and furthermore, degradation paths among components are also considered to be probabilistically dependent. Degradation paths of components, not considering shock exposure, are often assumed to be independent, which is a common assumption, and it is often a realistic assumption. However, this new model is for the cases when pure degradation paths of different components are dependent in such a way that they can be classified into groups. One reason degradation paths could be dependent is because the components can be physically in contact or close to each other, and the degradation of one component impacts the other ones. Alternatively, random factors like temperature, wind speed, exposure to a polluted environment, etc., can affect degradation paths of all the components at the same time when components exist in a shared environment, causing another form of dependence. The new reliability model, considering stochastically dependent degradation paths among components, can be more realistic and practical for some applications. This is a new model when stochastically dependent component degradation paths are used in reliability models for systems subject to dependent competing failure processes.

A Cybersecurity Insurance Model for Power System Reliability Considering Optimal Defense Resource Allocation

With the increasing application of Information and Communication Technologies (ICTs), cyberattacks have become more prevalent against Cyber-Physical Systems (CPSs) such as the modern power grids. Various methods have been proposed to model the cybersecurity threats, but so far limited studies have been focused on the defensive strategies subject to the limited security budget. In this paper, the power supply reliability is evaluated considering the strategic allocation of defense resources. Specifically, the optimal mixed strategies are formulated by the Stackelberg Security Game (SSG) to allocate the defense resources on multiple targets subject to cyberattacks. The cyberattacks against the intrusion-tolerant Supervisory Control and Data Acquisition (SCADA) system are mathematically modeled by Semi-Markov Process (SMP) kernel. The intrusion tolerance capability of the SCADA system provides buffered residence time before the substation failure to enhance the network robustness against cyberattacks. Case studies of the cyberattack scenarios are carried out to demonstrate the intrusion tolerance capability. Depending on the defense resource allocation scheme, the intrusion-tolerant SCADA system possesses varying degrees of self-healing capability to restore to the good state and prevent the substations from failure. If more defense resources are invested on the substations, the intrusion tolerant capability can be further enhanced for protecting the substations. Finally, the actuarial insurance principle is designed to estimate transmission companies’ individual premiums considering correlated cybersecurity risks. The proposed insurance premium principle is designed to provide incentive for investments on enhancing the intrusion tolerance capability, which is verified by the results of case studies.

An End-to-End Reliability Framework of the Internet of Things.

The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.

Reliability assessment of multi-state phased mission systems with common bus performance sharing considering transmission loss and performance storage

Multi-state phased mission systems (MS-PMSs) with common bus performance sharing widely exist in practical engineering, such as intelligent transmission systems and power supply systems. The challenges in assessing the reliability of these systems come from the dependence among phases and the complexity of multi-state behaviors of components. Besides, the performance transfer between different phases and the performance transfer with transmission loss have never been studied in such systems. To effectively address these problems, this paper proposes a reliability model for MS-PMSs with common bus performance sharing considering transmission loss and performance storage. First, a novel Markov model for multi-state components is established to solve the dependence problem among phases. Then, considering the performance sharing among subsystems within each phase through the common bus and the performance transferring between phases by an energy storage device, an iteration method combined with transmission loss function is adopted to establish the system reliability model, where the performance transmission loss is proportional to transmission distance and transmission performance. Furthermore, a universal generating function (UGF) based method is used for evaluating the system reliability. Finally, an analytical example and a case study of the IEEE-24 bus system are utilized to verify the availability of the proposed method.

An adaptive simulation analysis of reliability model for the system of supply chain based on partial differential equations

The system of supply chain is a complex dynamic system. Due to the existence of many influencing factors, the system of supply chain has strong randomness, which directly affects the reliability for the system of supply chain. How to analyse and improve its reliability has become more and more urgent, and has received more and more attention. As a result, this paper analyses transition relationship between states of the system of supply chain, introduces the supplementary variable method, and establishes a reliability model for system of supply chain using partial differential equations. The Repast S simulation is used to study reliability performance for the system of supply chain under different interferences, get intentional interference effects on the reliability for the system of supply chain, than in the same condition of the reliability of the impact of unintentional interference, and the characteristics of the system supply chain has a scale-free, which has better reliability for unintentional interference.

Reliability modeling for consecutive k‐out‐of‐n: F systems with local load‐sharing components subject to dependent degradation and shock processes

AbstractTraditional k‐out‐of‐n models assume that the components are independent, while recent research studies assume that the components are dependent caused by global load‐sharing characteristic. In this paper, we investigate the consecutive k‐out‐of‐n systems with dependent components by local load‐sharing characteristic. The work load and shock load on failed components will be equally shared by adjacent components, so the components tend to fail consecutively. Consequently, the components degradation processes may be diverse, since their degradation rate (dependent on work load) and abrupt degradation (dependent on shock load) become unequal because of local load‐sharing effect. Furthermore, the system failure will be path‐dependent on the failure sequences of components, which results in that the same system states may have different system failure probabilities. This new dependence makes the system reliability model more complex. In this work, an analytical model that can be solved numerically is derived to compute the reliability with this complex dependence. The developed model is demonstrated by a cable‐strut system in the suspension bridge. The results show that the reliability decreases significantly when the new dependence is considered.

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

Reliability analysis for blockchain oracles

Blockchain is an emerging technology that is increasingly supporting economic-ally-critical systems. The execution environment of blockchain is isolated from the external world and thus requires “blockchain oracles”: agents that fetch information from the external world. Blockchain is known to be highly reliable, but oracles are off-chain components that could be points of failure in whole blockchain-based systems. The reliability of blockchain oracles has yet to be investigated. In this paper, we propose a framework to compare and characterize existing blockchain oracles mechanisms from industry. Our approach for reliability modelling and architecture analysis of blockchain oracle systems uses Fault Tree Analysis. By calculating the reliability of oracles mechanisms, we can identify weak links that affect the overall reliability of a blockchain-based system.

Reliability Analysis and Imprecise Component Importance Measure of Redundant Systems of OWTs Based on Component Swapping

Due to the high cost of failures of wind turbines, redundancy designs are commonly applied in wind turbines for improving the reliability and availability of systems. For this reason, replacing failed components with other working components of the same type in redundant systems is becoming an attractive option of maintenance strategies towards more resilient systems. To quantitatively evaluate system’s reliability, this paper focuses on the reliability analysis of redundant systems of offshore wind turbines based on swapping existing components. The survival signature-based component swapping method is introduced to describe the new structure-function of the system upon swapping. Furthermore, the reliability model of redundant systems is established using the fault tree and survival signature. Following this, the influences of component swapping on component reliability importance measure (marginal reliability importance and joint reliability importance) without and with considerations of the imprecision of failure rates are explored. Finally, a 5MW offshore wind turbine is presented to show the applicability of the proposed approach for redundant systems, and the results show that the proposed approach can obtain realistic reliability assessment of redundant systems and considering component swapping can significantly improve system reliability.

Reliability evaluation of a k-out-of-n(G)-subsystem based multi-state system with common bus performance sharing

In this paper, a new reliability model for a complex multi-state system (MSS) with performance sharing is proposed. The MSS consists of m different k-out-of-n(G) subsystems which are connected by a transmission system. Subsystem i is composed of ni non-identical components, in which at least ki working components are demanded for the operating of the subsystem. The performances of the components in subsystem i are accumulated to meet its demand. Subsystem i fails if its performance demand is not satisfied or the number of working components is insufficient. The surplus performance of any subsystem can be shared by other subsystems experiencing performance deficiency via the transmission system. The entire system fails if at least one subsystem fails after performance sharing. An algorithm based on the universal generating function (UGF) is developed to evaluate the reliability of the entire system. Analytical and numerical examples are provided to show the effectiveness of the proposed method. Finally, a structure optimization problem of the performance sharing system with given transmission capacity C is discussed.

A new combinatorial model for deterministic competing failure analysis

AbstractComponents in many engineering and industrial systems can experience propagated failures, which not only cause the failure of the component itself but also affect other components, causing extensive damage to the entire system. However, in systems with functional dependence behavior where failure of a trigger component may cause other components (referred to as dependent components) to become unusable or inaccessible, failure propagation originating from a dependent component could be isolated if the corresponding trigger component fails first. Thus, a time‐domain competition exists between the failure propagation effect and the failure isolation effect, which poses a great challenge to the system reliability modeling and analysis. In this work, a new combinatorial model called competing binary decision diagram (CBDD) is proposed for the reliability analysis of systems subject to the competing failure behavior. In particular, special Boolean algebra rules and logic manipulation rules are developed for system CBDD model generation. The corresponding evaluation algorithm for the constructed CBDD model is also proposed. The proposed CBDD modeling method has no limitation on the type of component time‐to‐failure distributions. A memory system example and a network example are provided to demonstrate the application of the proposed model and algorithms. Correctness of the proposed method is verified using the Markov method.

Reliability modeling and optimal random preventive maintenance policy for parallel systems with damage self-healing

Materials with intrinsic self-healing phenomenon possess the ability to heal in response to external random shocks. Introducing a recovery factor to quantitatively measure the damage self-recovery efficiency, this paper designs a self-healing mechanism corresponding to both damage load and shock arrival numbers for a parallel redundant system consisting of multiple non-identical components. From the actual engineering perspective, each shock arriving on the system selectively affects one component or more but not necessarily all units in parallel, and consequently, random shocks are categorized according to their sizes, attributes and affected components. This study investigates novel reliability models and schedules optimal preventive maintenance policies, in which the closed-form reliability quantities are derived analytically and the optimum preventive replacement interval is demonstrated theoretically. In addition, a Nelder-Mead downhill simplex method is introduced to seek the optimal replacement age in minimizing the long-run average maintenance cost rate for the condition system failure distribution is rather complex. A micro-electro-mechanical system (MEMS) whose constitutional materials are integrated by microcrystalline silicon, where polymer binders with self-healing capability are always synthesized, is designed to verify the results we obtained numerically, illustrating the significance of considering damage self-healing phenomena.

Optimal switching policy for warm standby systems subjected to standby failure mode

Standby is used extensively in mission-critical and safety-critical systems to improve reliability and availability. The dormant period during standby may introduce additional failure modes to the standby components. This is commonly observed in many real systems, yet it has been overlooked in existing research. This study is motivated by a two-motor standby system used in a power plant, in which periodic switching between the two motors is used to mitigate standby failure. We propose a generic system reliability model that captures both the normal aging and standby failures. The long-run average cost of the system can be derived using the technique of semi-regenerative processes. Thereafter, the problem of the optimal switching policy is formulated with the objective of determining the optimal switching period that minimizes the long-run average cost. We further consider a special case where the component failures under normal-use conditions follow a Poisson process and the repair times are exponentially distributed. A numerical study is conducted to demonstrate the proposed methodologies.

Reliability and condition-based maintenance modeling for systems operating under performance-based contracting

Performance-based contracting motivates service providers to implement effective maintenance policies so as to boost profits and improve system performance at a lower cost. This paper deals with reliability and condition-based maintenance modeling for single-unit systems operating under performance-based contracting. For a system subject to degradation and sudden shocks, there involves three states: normal, degraded and failed. Once entered into the degraded state, the system deteriorates faster and becomes more susceptible to shocks. The degradation conforms to a two-stage inverse Gaussian process with random effects characterizing unit-specific heterogeneity in the population. Furthermore, the arrival of sudden shocks follows a doubly stochastic Poisson process. A reliability model is developed based on degradation-based and shock-based failures modeling. Afterwards, the long-run maintenance cost rate and the average system availability are evaluated. Optimal inspection-based preventive replacement policy is obtained by maximizing the expected profit rate to the service provider. Finally, a numerical example along with sensitivity analysis of model parameters is presented to demonstrate the applicability and solution procedure of the proposed models.

Levee System Reliability Modeling: The Length Effect and Bayesian Updating

In levee system reliability, the length effect is the term given to the phenomenon that the longer the levee, the higher the probability that it will have a weak spot and fail. Quantitatively, it is the ratio of the segment failure probability to the cross-sectional failure probability. The literature is lacking in methods to calculate the length effect in levees, and often over-simplified methods are used. An efficient (but approximate) method, which we refer to as the modified outcrossing (MO) method, was developed for the system reliability model used in Dutch national flood risk analysis and for the provision of levee assessment tools, but it is poorly documented and its accuracy has not been tested. In this paper, we propose a method to calculate the length effect in levees by sampling the joint spatial distribution of the resistance variables using a copula approach, and represented by a Bayesian Network (BN). We use the BN to verify the MO method, which is also described in detail in this paper. We describe how both methods can be used to update failure probabilities of (long) levees using survival observations (i.e., high water levels and no levee failure), which is important because we have such observations in abundance. We compared the methods via a numerical example, and found that the agreement between the segment failure probability estimates was nearly perfect in the prior case, and very good in the posterior case, for segments ranging from 500 m to 6000 m in length. These results provide a strong verification of both methods, either of which provide an attractive alternative to the more simplified approaches often encountered in the literature and in practice.

Modeling and analysis of system reliability using phase‐type distribution closure properties

AbstractPhase‐type distribution closure properties are utilized to devise algorithms for generating reliability functions of systems with basic structures. These structures include series, parallel, K‐out‐of‐N, and standby structures with perfect/imperfect switch. The algorithms form a method for system reliability modeling and analysis based on the relationship between the system lifetime and component lifetimes for general structures. The proposed method is suitable for functional system reliability analysis, which can produce reliability functions of systems with independent components instead of only system reliability values. Once the system reliability function is obtained, other reliability measures such as the system's hazard function and mean time to failure can be obtained efficiently using only matrix algebra. Dimensional and numerical comparisons with computerized symbolic processing are also presented to show the superiority of the proposed method.

Reliability analysis of k-out-of-n: F balanced systems with multiple functional sectors

In this paper, new concepts of balanced systems are proposed based on real engineering problems. The system under study consists of l groups and each group has n functional sectors. The conception of balance difference is proposed for the first time. It is assumed that unbalanced systems are rebalanced by either forcing down some working units into standby or resuming some standby units into operation. In addition, a case that the forced-down units are subject to failure during standby is studied in this paper. Based on different balanced cases and system failure criteria, two reliability models for balanced systems are developed. The proposed systems have widespread applications in aerospace and military industries, such as wing systems of airplane and unmanned aerial vehicles with balanced engine systems. Markov process imbedding method is used to analyze the number of working units in each sector for each model. Finite Markov chain imbedding approach and universal generating function technique are used to obtain the system reliability for different models. Several case studies are finally presented to demonstrate the new models.

Reliability Model and Sensitivity Analysis for General Electronic Systems with Failure Types based on Non-identical Correlated Components

Reliability is the most important qualitative properties of products and industrial electronic systems. Today, industrial electronic systems need to produce products with maximum reliability. In every industry, when a system fails, it becomes harmful in various aspects such as economic, human and political; therefore, accurate estimation of system reliability is very important. Previous methods to calculate system reliability assumed that a large number of components failures are statistically independent. Considering such a hypothesis makes it possible to calculate probability and mathematical computation, but it does not provide perfect system reliability. This paper presents a simple and new technique for reliability analysis by considering unequal reliability and non-identical distribution of correlated components for k-out-of-n and coherent systems. The efficiency of our proposed method is demonstrated by computing the reliability of Bridge system. The results show that the function of existing components correlation has a major impact, and that ignoring it has a significant effect on system safety.

Developing a Reliability Model of CNC System under Limited Sample Data Based on Multisource Information Fusion

The reliability of the computer numerical control (CNC) system affects its processing performance and is a major concern in the manufacturing industry today. However, existing reliability models to assess the reliability of the CNC system often exhibit relatively large errors due to inadequate treatment of small samples. In order to get around the constraint of limited lifetime failure data and take full advantage of existing reliability parameters in traditional reliability models, a multisource information fusion-based reliability model grounded on Bayesian inference is proposed to deal with the small sample size. The prior distributions are derived by using the probability encoding method and conjugate distribution based on the idea of multisource information fusion. Then, using the Jensen–Shannon divergence (JSD) to measure the similarity between prior information and field observation data, a constrained optimization problem is established to determine the respective weight of prior information and field observation data. Finally, by conducting the reliability analysis of repairable CNC systems, the validity of the proposed model and its prior distribution derivation method are verified.