System Reliability Model Research Articles

Precising the evaluation of radio information system reliability characteristics based on the empirical failure distribution

For highly reliable radio information systems the failures are quite rare phenomena leading to critical decrease in functional characteristics. At the same time, failure distribution may be a subject to rather complex laws, which together with a lack of real data makes it difficult to evaluate the reliability characteristics of the product. Achieving high accuracy of reliability characteristics evaluation is possible due to constant updating the parameters of the radio information system reliability model based on experimental data on the technical condition of the product components starting from the stage of its development. In this case, it is possible to determine the reliability characteristics based on current data on the performance of radio information system components based on the empirical failure distribution function. In this article we propose an algorithm for transferring to an empirical distribution and counting the characteristics (mean time between failures) of radio information system reliability based on it.

Reliability parameter estimation of repairable systems with imperfect maintenance, repair and overhaul

PurposeIndustries generally require good maintenance, repair and overhaul (MRO) facilities. Maintenance activities at MRO cover the normal scheduled check-ups known as scheduled preventive maintenance (SPM) whereas an overhaul reviews and rejuvenates the complete system at a scheduled time. The literature is reasonably stocked with reliability modelling of repairable systems considering both the corrective maintenance (CM) and SPM as imperfect. However, in all these situations the overhaul is modelled as perfect repair. Thus, the purpose of this research paper is to develop a mathematical model for the estimation of reliability parameters considering the complete MRO as imperfect.Design/methodology/approachThe paper proposes arithmetic reduction of age (Kijima I) based virtual age model to estimate reliability parameters by considering the complete MRO as imperfect and provides the likelihood and log-likelihood functions for parameter estimation of the proposed model and also presents the various extensions of the proposed model.FindingsFor analysis, two real-time data sets of two components, i.e. turbostarter and plunger pump are considered. The analysis mainly focuses on intensity function and availability of components. The availability analysis of the components directly affects the cost analysis. It is very important to analyze the realistic trend of availability, and the comparative analysis shows that the assumption of perfect overhaul overestimates and minimal overhaul underestimates the performance of the components whereas assumption of imperfect overhaul portraits more sensible deteriorating and availability trend of the components.Originality/valueThe proposed methodology in this paper is a novice and not available in the literature.

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Motivated by real-world complex engineering systems, this research proposes a multi-state system with two levels of performance sharing. In this system, there exists a unique performance transmitter between any two adjacent subsystems and any two adjacent elements within each subsystem. Surplus performance of a subsystem or an element can only be transmitted to its adjacent subsystems or elements. We build the reliability model of the proposed system and suggest a corresponding reliability evaluation algorithm by extending the existing universal generating function technique. Since the performance sharing is only allowed between adjacent subsystems and elements, the element allocation and sequencing will affect the system reliability. Due to the complexity of the combinatorial optimization problem, we use the genetic algorithm to find the optimal allocation and sequencing of the elements. An analytical example is provided to illustrate the reliability evaluation algorithm. Numerical experiments are carried out to demonstrate how the optimal allocation and sequencing as well as the capacities of the performance transmitters affect the system reliability.

Serial and parallel reliability models for robot arm reliability analysis

For reliability evaluation of a robot arm designed for tunnel boring machine, a serial system reliability model for arm strength and a summation (cumulative deformation) system reliability model for the fingers of the robot arm are developed. To reflect the effect of load uncertainty, total probability theorem is applied to develop mult-layer, multi-variate reliability models. With the total probability theorem and the multi-layer configuration of the reliability models, such models reflect the effect of load intensity uncertainty, load action number uncertainty, load peak distribution uncertainty, and component strength uncertainty hierarchically.

Reliability Evaluation of Multiple DCFP System subject to Shifting Threshold

This paper focuses on system reliability analysis with dependent competing failure process due to soft failure and hard failure. Some new probabilistic methods based on cumulative shock model and nonlinear Wiener process under different shifting thresholds situation are obtained. Considering that nonlinearity exists extensively in practice, the continuous soft failure process is governed by random effected nonlinear Wiener process. Firstly, reliability evaluation models for hard failure and soft failure are obtained under the cumulative shock, respectively. Furthermore, some system reliability models under different shifting thresholds situation are studied, in which failure threshold will decrease after a certain number of shocks. A real numerical example about fatigue crack growth dataset is carried out to demonstrate the proposed procedure. Numerical results indicate that both random shocks and shifting threshold have significant effect on system reliability. Finally, some sensitivity analysis are also been given.

Optimal Distribution of Renewable Energy Systems Considering Aging and Long-Term Weather Effect in Net-Zero Energy Building Design

Generation system interruptions in net-zero energy buildings (NZEBs) may result in missing the net-zero targets by a great margin. Consequently, it is significant to incorporate a realistic reliability model for renewable energy systems (RESs) that considers aging and long-term weather conditions. This study proposed a robust design optimization method that deals with the selection of RES to achieve NZEB. Different case studies were evaluated: 1. Deterministic approach; 2. Markov chain-based reliability without the aging effect; 3. Markov chain-based reliability with the aging effect. The results showed that the optimal sizes of RES, considering the aging effect, were much larger than the other two cases based on the annual energy balance. Moreover, the consideration of the aging effect on the reliability assessment of the generation system for NZEB opens a pathway for a more robust and economic design of RES.

Simulation of the FPT for Multi-Dimensional Wiener Process with Dynamic Thresholds

Reliability modeling and analysis has becoming an essential issue in complex system management. Degradation system reliability modeling is an important part of the reliability field due to that the failure data is more and more difficult to obtain. Degradation data can well describe the failure process of a system or product. In the stochastic process, the Wiener process has been widely used in literature due to its good properties. Multidimensional diffusion Wiener process describes the multidimensional degradation process of products. First passage time is the lifetime of a system to reach a threshold value. In this paper, a generalized model is developed for evaluating the reliability of the multidimensional degradation process. Meanwhile, we use the Monte Carlo method for the solution of the FPT with dynamic threshold value. The comparisons show that the results of the proposed model agree well with the Monte Carlo results. The research can support decision making in the reliability improving.

Component-based system reliability subject to positive and negative correlation

Traditional system reliability modeling methods assume statistical independence of component failures, while past research emphasizes methods to characterize positive correlation and dependence. However, negative dependence and correlation are also important for system reliability and resilience modeling. This paper develops a method to assess the reliability of a system subject to arbitrary combinations of positive and negative correlation between the failures of its components. The method enables derivation of expressions composed of a vector of unequal component reliabilities and unequal pairwise component correlations. The method is illustrated through a sequence of examples, which conduct sensitivity analysis on correlation parameters of the model as well as quantify the impact of component correlations on system reliability. Coverage experiments formally assess the flexibility and scalability of the approach.

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.