System Reliability Model Research Articles

Proposal of quantification method of dynamic system reliability model of digital RPS using Markov state-transition model

ABSTRACT In safety systems of the latest nuclear power plants such as the Advanced Boiling Water Reactor (ABWR) and the latest Pressurized Water Reactor (PWR), digital circuits are used for start-up signals of a reactor protection system (RPS) and emergency core cooling systems (ECCS). And several studies have been conducted to develop the method to build a system reliability model of RPS to be combined with probabilistic risk assessment (PRA) so far. However, these studies have not been sufficiently progressed in terms of the quantitative evaluation and coupling with the conventional PRA method. Despite of the efforts of Task group of Digital system reliability failure mode taxonomy (DIGREL, later becoming Working Group on Digital Instrumentation and Control [WGDIC]) of Organization for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) in this field, the situation has not changed sufficiently. From these backgrounds, this paper presents a rational quantitative evaluation method of the dynamic system reliability model of the digital RPS which can represent time-dependency and is consistent with the conventional PRA method.

A stochastic time scale based framework for system reliability under a Markovian dynamic environment

AbstractMost engineering systems operate under stochastic dynamic environments. The variability and stochasticity of environmental conditions have a non‐negligible impact on the failure behavior of engineering systems. This article develops a reliability modeling and assessment framework for systems operating under a Markovian dynamic environment. The stochastic dynamic environment is characterized by a continuous‐time Markov chain. Using the cumulative exposure principle, the stochastic time scale, resulting from the cumulative effect of the Markovian dynamic environment, is computed via a Markov reward model. Based on the above settings, the system reliability model under the Markovian dynamic environment is developed. The maximum likelihood estimates and confidence intervals for the model parameters, including the transition rate matrix of the Markov chain, the reward rates of the Markov reward model, and the parameters of the baseline lifetime distribution, are obtained by utilizing the collected environment and lifetime data. The system reliability is then assessed with the estimated parameters. The effectiveness of the proposed framework are validated using simulation and through an application to a long‐term storage system. The results show that the unknown reliability model parameters can be accurately estimated, and the proposed model with the consideration of the cumulative effect of the Markovian dynamic environment can provide a more accurate reliability estimate than that without such a consideration.

Methodology for updating GO-FLOW model to handle scenario changes in nuclear power plants

The design of a nuclear power plant is proved to be safe enough in various hypothetical operation scenarios after strict safety assessment. One of the important tasks of operational risk management in a nuclear power plant is to evaluate whether any configuration change of the nuclear power plant can still achieve its expected safety and economic goals. This paper proposes a system reliability modeling and analysis method based on two-layers hierarchical GO-FLOW model. By flexibly adjusting the parameters of a GO-FLOW model, the model can adapt to the changes of success criteria and various configuration of the modeled system, thus avoiding the extra workload brought by re-modeling and improving the efficiency of risk management in nuclear power plants.

System Reliability of a Semi-Submersible Drilling Rig Considering the Effects of the Main Nodes

In this paper, structural systems reliability analysis was used to estimate the system reliability of a typical semi-submersible based on the characteristic structure failure mechanisms under extreme wave loads. First, a three-dimensional simulation model was created for the chosen semi-submersible platform using an elastic-perfectly plastic material model, neglecting the effect of the strain hardening assumption. Then, under design wave conditions, and characteristic structural failure mechanisms of a semi-submersible platform were examined, and a system reliability model was developed based on the failure mechanism of the chosen platform. Finally, system reliability methods were used to determine the platform’s system reliability. It was discovered that the main node’s system reliability makes a significant contribution to the overall structure reliability, so it is strongly advised that the reliability of the main nodes be taken into account when determining the overall structure’s reliability. This research aims to apply reliability theory in offshore engineering and, ideally, to encourage the reliability-based design of semi-submersible platforms and other similar structures.

Human reliability assessment of intelligent coal mine hoist system based on Bayesian network

The human reliability of intelligent coal mine hoist operation system is affected by many factors, in order to reduce the occurrence of human error in the hoist system and improve the reliability of the system. The characteristics of phased-mission task operation of hoists is combined, the phase dependence of human cognitive errors is considered and, a new human reliability evaluation method is proposed with the help of Bayesian network (BN) model in this paper. Firstly, the phase dependence of human cognitive errors was analyzed based on the cognitive behavior model. Then the human error analysis in the hoist system was carried out, and several main performance shaping factors are selected. Secondly, BN was used to build the human reliability model of the hoist system at each stage. Finally, it is found that the phase dependence of cognitive errors has a negative impact on the human reliability of the hoist system through the case analysis. At the same time, several main performance shaping factors (PSFs)were quantitatively analyzed by using the reverse reasoning ability of BN, which proves the effectiveness of the proposed method, and provides a scientific and reasonable theoretical basis for the development of effective human error prevention measures for the operation of intelligent coal mine hoists.

Generalized mixed shock model for multi-component systems in the shock environment with a change point

The reliability of various systems under shock models has been explored extensively in the literature, where the constant mechanism of shock impact has always been studied. Nevertheless, engineering systems operate in a more complicated shock environment due to developments, and it is worth studying the reliability of such multi-component systems in a variable shock environment. Driven by the research gaps and practical situations, this paper constructs a reliability model of multi-component systems under a generalized mixed shock model with a change point. The multi-state components operate in the shock environment I initially and it may continue to run in the shock environment II after the random change point of the environment. Two novel shock impact mechanisms of the variable environment are put forward, where a series of failure criteria based on shocks are included. Four different structures of multi-component systems are considered in this paper. It can be proved that the proposed mixed shock model is a generalization of some transformed models. A multi-stage finite Markov chain imbedding approach is established to derive the probabilistic indices of the components and entire systems. Based on the engineering applications, illustrative examples are provided to verify the effectiveness of the proposed model.

Impact of Reliability in Conceptual Design—An Illustrative Trade-Off Analysis

System reliability is treated as a parameter and not modeled in the early concept design stages. We illustrate a reliability model for system reliability in early concept design using knowledge from similar systems, technology readiness levels (TRL), and functional analysis methods using an unmanned ground vehicle. We integrate the reliability model with performance and cost models to demonstrate the impact of reliability in early concept design. The resultant tradespace comparison with and without early reliability assessment illustrates that reliability modeling can identify infeasible solutions in early system design. This will allow system designers to focus development on the most promising concept designs.

Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.

Reliability model of series systems with multiple shock sources subject to dependent competing failure processes using phase-type distribution

ABSTRACT In this paper, we introduce the concept of a series system with two components and three shock sources considering degradation to build a reliability model. Sources 1 and 2 affect components 1 and 2, respectively. Source 3 covers both components. Both components are subject to dependent competing failure processes (DCFPs). A general reliability model of the n-component series system with m-shock sources subject to DCFPs is derived. The phase-type distribution method is applied to calculate the reliability of the hard failure process. The time lag among shocks follows the continuous phase-type distribution (PH c ). The lifetime and system reliability properties are discussed based on the phase-type distribution. The dependence of shock sources is also considered according to the proposition of phase-type distribution (PH). Finally, an application example and sensitivity analysis of micro-electro-mechanical systems (MEMS) oscillators subject to various shock models are presented to illustrate the developed reliability models.

Application of external flood probabilistic safety assessment methodology to Prototype Fast Breeder Reactor

ABSTRACT This paper presents the methodology adopted, accident sequence analysis and salient results from the external flood probabilistic safety analysis exercise for Prototype Fast Breeder Reactor. The major steps performed are the probabilistic hazard estimation, fragility computation, system reliability modelling and risk quantification. The hazard analysis has been performed for rainfall, storm surge and tsunami as typically associated with a coastal site, from which correlation function relating flood level to the annual exceedance frequency is estimated. The fragility analysis of different safety systems is estimated by considering submergence mode of failure using step fragility functions. The secondary effects of flood such as debris impact, load falling and flood-induced sodium fires are not considered. The human reliability analysis of manual operations necessary for plant safety is done for the control room as well as onsite actions. The challenges encountered during the analysis, salient results and uncertainties involved are presented with inferences. Abbreviations: AHX: air heat exchanger; CDF: core damage frequency; EFPSA: external flood probabilistic safety assessment; EL: elevation levels; EOP: emergency operation procedure; ET: event tree; FT: fault tree; HRA: human reliability analysis; IE: initiating events; NPP: nuclear power plant; OGDHRS: operation grade decay heat removal system; PFBR: prototype fast breeder reactor; POT: peak over threshold; PSA: probabilistic safety assessment; PTHA: probabilistic tsunami hazard assessment; RY: reactor year; RCB: reactor containment building; RP: return period; SGDHRS: safety grade decay heat removal system; SSC: structures, systems and components

Belief reliability modeling for the two-phase degradation system with a change point under small sample conditions

The sparsity of the detection points and the small data size of samples have brought great difficulties in two-phase degradation modeling reliability analysis. This paper adopts belief reliability theory to cope with insufficient data and proposes a new reliability model for systems that display two-phase degradation. The uncertain process is used to model the degradation, thereby the first hitting time distribution and belief reliability of the two-phase degradation are derived. A uncertain variational inference algorithm based on the uncertain maximum likelihood method is also developed to discover the change-point more precisely and efficiently. The proposed method is verified to be more accurate and stable than the two-phase Wiener stochastic process model through the numerical simulation examples and a case of liquid coupling devices.

Reliability-centered prediction methods of clock battery low-output-voltage event for smart electricity meters

Smart electricity meter failures triggered by low-output-voltage accounted for 79 % of on-site meter failures. It is significant to predict the event of low-output-voltage of the clock battery. Still, it is also challengeable due to the complexity of the failure causes and the diversity of component reliability data. In this paper, multi-causes of battery low-output-voltage failure were analyzed and incorporated to develop a system reliability model for the clock battery circuit. The lifetime distribution of the clock battery circuit was computed using time-varying weights to integrate diverse forms of component data, including degradation data, lifetime distribution data, lifetime observation data, and failure rate data from datasheet. Finally, an engineering example is used to verify the effectiveness of the developed method. The developed reliability model of clock-battery systems can also be applied in other systems where power is supplied from batteries.

Reliability assessments for two types of balanced systems with multi-state protective devices

In the existing studies, the positive effects of a protective device on the reliability of balanced systems have not been attached importance, and the research on protective devices is limited and insufficient with respect to the triggering and protection mechanisms. Driven by practical engineering applications, this paper establishes two reliability models for balanced systems with multi-state protective devices from the perspectives of dynamic and static balanced concepts, respectively. The rebalanced mechanisms are considered in the proposed dynamic balanced system, and system imbalance is included in the failure criteria of the proposed static balanced system. In both models, new triggering mechanisms of protective devices are proposed based on the respective characteristics of the balanced systems. Moreover, variable protection mechanisms of the devices are proposed by introducing different reduction factors when the protective device operates in different stages. The protective devices reduce the integrated impact of shocks and internal degradation exerted on the units in the new models. The Markov process imbedding method combined with Monte-Carlo simulation is employed to derive the reliability of the proposed models. The applicability of proposed systems and correctness of the method are validated by practical engineering applications.

Analytical Method of Calculating Reliability Sensitivity for Space Capsule Life Support Systems

Several fields within the social and technical sciences have identified the importance of theoretical and practical research into complex systems. The sensitivity of these systems is one of the primary focuses. The capsule’s high-pressure oxygen supply system (HPOSS) prepares a complex system with complex interconnections. The reliability of HPOSS cannot be reduced to a single equivalent component or block by combining series and parallel reductions. The aim of this paper is to demonstrate the application of mathematical detection tests to HPOSS in order to assess the sensitivity of the reliability of complex systems. Using the matrix-based minimal cut methodology and the linear sensitivity model of system reliability, the critical system illustrates the suggested methodologies and their relevance to investigating the sensitivity of complex interconnected systems.

UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application.

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

Reliability modeling of uncertain random fractional differential systems with competitive failures

Typical degradation-shock failure processes have been widely investigated in current researches, and the failures caused by their dependence are described as competitive failure processes. This paper explores competitive failure modes for uncertain random fractional systems involving degradation and shock processes. We develop a wear degradation model explicitly by employing uncertain fractional differential equations in order to demonstrate the potential heredity and memorability of a system. External shocks are then considered to follow a Poisson process. Based on the classification of shock types, three definitions of reliability index for competitive failures are presented. The reliability index formulas are derived for systems with extreme shock, cumulative shock, and δ shock using chance measures. Finally, we introduce a numerical example where the results of the reliability analysis confirm the validity of proposed reliability evaluation methods.

EV Response Capability Assessment Considering User Travel Demand and Cyber System Reliability

With the increasing penetration rate of electric vehicles (EVs), EV demand response holds great significance for promoting the optimal and secure operation of the power system. This paper proposes an EV response capability assessment method that considers EV users’ travel demands and the reliability of the cyber systems integrated into both the power grid and the transportation network. A novel framework for an integrated cyber–power–transportation system is proposed for the first time, and a reliability model for the cyber system is provided. A method is further proposed to calculate the state of an EV when it is plugged in, considering the reliability of traffic guidance information and the reliability of the release of such information. The degree of relaxation in the EV charging demand is proposed to reflect the user’s travel demand, based on which the EV response capability can be assessed. Extensive test results on a cyber–power–transportation system containing RBTS BUS6 and the Beijing transportation network are conducted to show the efficiency of the proposed method. The impact of cyber reliability on the EV trip and response capability is analyzed.

Dynamic Fatigue Analysis of High-Speed Trains Gearbox Using Copula Function

As a key component of the transmission system of high-speed trains, the reliability of gearbox is crucial to the overall reliability and driving safety of high-speed trains. In this work, a comprehensive reliability model for the key parts of the gearbox including the driving and driven gears, bearings, and gearbox housing is developed, which combines the strength degradation parameters obtained by P-S-N curves of the corresponding material, and a stress-strength interference model based on the Poisson distribution of random stress and the Wiener strength degradation process. Further, a nested Copula function reliability model of the gearbox series system is developed using the binary Frank Copula function considering the correlation of failures of different parts and different failure modes. This model realizes the dynamic reliability analysis of the gearbox under different failure modes. The reliability analysis of the key parts of the gearbox and the gearbox series system of high-speed train achieved with this model are in line with the engineering practice. This method enables dynamic tracking and monitoring of gearbox reliability during the service life of high-speed train.

Modular model and algebraic phase algorithm for reliability modelling and evaluation of phased-mission systems with conflicting phase redundancy

Phased-mission systems (PMSs) have been widely used as mission reliability models for many complex systems whose missions can be divided into several phases. In practice, PMSs may have conflicting phase redundancy, that is, a phase task can be performed in more than one phase and redundancy phases can be shared by more than one failed phase. Such PMSs have the following characteristics: (1) the failure of a phase task with redundancy does not directly lead to the failure of the system mission; (2) they have more than one phase level path; and (3) redundancy phases of them have multiple states. This study investigates the reliability modelling and evaluation method of such PMSs with conflicting phase redundancy (PMS-PR). A modular model with new phase states notes was provided to depict the states of phases and deal with the redundant conflicts. A general phase algebraic algorithm is provided to produce all the disjoint phase level paths for the PMS-PR based on the presented model. The mission reliability PMS-PR can be estimated based on disjoint phase level paths. Finally, a simplified telemetry, tracking, and command system under different phase redundancy strategies is analysed to illustrate the effectiveness of the model and algorithm.

System Life and Reliability Modeling of a Multiple Power Takeoffs Accessory Gearbox Transmission

System Life and Reliability Modeling of a Multiple Power Takeoffs Accessory Gearbox Transmission