System Reliability Model Research Articles

Analytic of VSC-HVDC Interconnected Power Systems from a Perspective of Frequency Security

In the interconnected power systems with VSC-HVDC (Voltage Source Converter-High Voltage Direct Current) transmission links, the failures of VSC-HVDC system can cause large power imbalance for both sending-end and receiving-end power systems. In this paper, the reliability model of the VSC-HVDC system is developed based on series-parallel structure recognition. The frequency dynamics of the power systems are characterized considering the frequency regulation of the power systems in response to the power imbalance arise from the component failures. Moreover, the Monte Carlo simulation method is applied to analyze the frequency dynamics in different contingencies. The indices measuring the frequency stability and the reliability of the interconnected power systems are introduced.

Chatter reliability prediction of side milling blisk

During the blisk side milling, the blade is thin and the tool suspension length is large, which is easy to occur milling system chatter. The wavy surface left by chatter has great effect on the surface roughness and service performance of the aero-engine blisk. To accurately predict the stability of the blisk side milling, the influence of random variables on the machining stability was considered. In this paper, the chatter reliability model of the blisk side milling system was established using the first-order second-moment method. The model takes structural parameters and spindle speed as random variables and chatter frequency as intermediate variables. The chatter reliability model was used to draw the reliability lobe diagram, which can be used to divide the machining reliability area. The effectiveness of this method was verified by experiments and compared with Monte Carlo method. The validation results showed that the reliability lobe diagram can be used to determine the stability probability prediction of blisk side milling and can replace the traditional stability lobe diagram.

Reliability evaluation of non-repairable systems with failure mechanism trigger effect

Trigger effect indicates that some events occur because of the trigger of other events. This kind of effect often appears in the operation of products and leads to failure. Due to the randomness of trigger events, it is difficult to predict the reliability of a product that embraces triggering processes. In this paper, we develop a new reliability model for non-repairable systems with the trigger effect. The effect is divided into two types: degradation-degradation trigger effect and degradation-overstress trigger effect. To model this effect, the degradation process is described by the continuous or discrete stochastic process, while the overstress process is described by the homogeneous Poisson process. Furthermore, a method integrating Petri Net and Monte Carlo is proposed to evaluate the complex system reliability. To demonstrate the proposed model, a conductive slip ring in an electromechanical orientation device is introduced as a case, where the trigger effect of one trigger mechanism and three dependent basic mechanisms are considered. It is shown that the trigger time model and system modeling method can describe the trigger failure behaviors and evaluate the reliability of complex systems with the trigger effect.

Reliability assessment of different HVDC transmission system configurations considering transmission lines capacity restrictions and the effect of load level

The ongoing increasing demand for electrical energy and the reliable access to remote renewable energy generation points such as off-shore wind power stations or solar power generation, have revived the interest in HVDC multi-terminal systems. With the expansion of HVDC systems, their reliability assessment has become continuously important. One of the most important aspects of the reliability assessment of electrical systems is to have an accurate understanding of the engineering implications of the system and its associated components. The next step is to appropriately model the system components to represent their characteristics and functions in the overall system. In order to deliver a specified amount of electrical power, different methods can be considered. There are two general methods: 1) using several electrical power sources with low capacity, or 2) using fewer electrical power sources with high capacity. The decision to choose a certain method for transmitting electrical power depends on various parameters, and one of the most important parameters is reliability studies. This paper presents reliability modeling and analysis of HVDC transmission systems incorporating hybrid HVDC breakers to interrupt HVDC line faults. In this paper, comprehensive detailed reliability models of point-to-point, three-terminal, and four-terminal configurations are developed. Using this equivalent reliability model, various reliability indices are calculated at the load point of the system. Also, the effects of line constraints and incorporating DC circuit breakers of the DC transmission lines on the load point reliability indices have been evaluated. In the end, the effect of the load level on the reliability indices of the load point of each configuration has been assessed.

A Deep Reinforcement Learning Approach to Dynamic Loading Strategy of Repairable Multistate Systems

As multistate system (MSS) reliability models can characterize the multistate deteriorating nature of engineering systems, they have received considerable attention in the past decade. The states of a multistate system/component can be distinguished by its performance capacity, which deteriorates over time and can be restored by maintenance activities. On the other hand, the deterioration of a system/component is, oftentimes, controllable by setting a loading strategy. In this article, a dynamic load optimization problem for repairable MSSs is investigated to achieve the maximum expected cumulative performance within a finite time horizon and limited maintenance resources. The degraded components in a system are dynamically maintained to recover to their better conditions, whereas the performance rate of each component can also be dynamically specified. The resulting sequential decision problem is formulated as a Markov decision process with a continuous action space and a mixed integer discrete continuous state space. The deep deterministic policy gradient algorithm, which is a specific deep reinforcement learning algorithm in the actor−critic framework, is customized to overcome the “curse of dimensionality” and mitigate the uncountable state and action spaces. The effectiveness of the proposed method is examined by two illustrative examples, and a set of comparative studies are conducted to demonstrate the advantage of the proposed dynamic loading strategy.

Reliability analysis for multi-component systems with interdependent competing failure processes

In this paper, two reliability models for multi-component systems suffering from dependent competing failure processes is studied. All components in the systems are subject to two types of failures, i.e., soft failure owing to internal degradation and hard failure due to external shocks. A soft failure happens when the total degradation amount of a component exceeds its soft failure threshold, and a hard failure of a component occurs when a single shock load or the accumulated shock load exceeds its hard failure threshold. A component fails when a soft failure or a hard failure happens, whichever occurs first. The external shock arriving according to a non-homogeneous Poisson process has impact on all components of the systems simultaneously. The soft failure and hard failure processes are dependent because the external shock can not only break down components stochastically, but also accelerate the degradation process of the components. By using the multivariate analysis technique, the analytic forms of the reliability functions for systems under cumulative shock model and extreme shock model are derived. The random point method in Monte Carlo simulation and vector program in Matlab are applied to calculate multiple integral in the expressions of the reliability functions. Finally, a case study on the Micro-Electro Mechanical Systems with multiple different components is provided to illustrate the proposed model.

Strength–redundancy allocation problem using artificial bee colony algorithm for multi-state systems

The system reliability optimization design is an indispensable part in the reliability engineering. The traditional reliability–redundancy allocation problem (RRAP) is widely studied and used to optimize the system reliability. However, researchers mainly focused on the optimal redundancy and subsystem reliability strategies, which is relatively blurry in the aspect of giving designers an explicit orientation to improve the system reliability. Therefore, this paper raises a newly-defined reliability-based strength–redundancy allocation problem (SRAP) for multi-state systems based on the load–strength interference model and formulates its corresponding reliability optimization design model with the proposed continuous multi-state system reliability model as the objective function. To obtain the more specific optimal component strength and redundancy strategies, we improve the basic artificial bee colony (ABC) algorithm to solve the reliability optimization design model since the SRAP is a mixed-integer nonlinear programming problem. An illustrative example about the reliability optimization design of the solar module is given to demonstrate the availability of the proposed model.

Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics

In practical engineering, the presence of dependent evidence is not rare due to various imperfections. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this paper, we propose a Bayesian reliability approach for complex systems with dependent life metrics. Notions such as explicit evidence and implicit evidence are established based on an identification of different roles of multiple dependent evidence in the likelihood construction. A likelihood decomposition method is developed to convert the overall likelihood into a product of Explicit Evidence-based Likelihood (EEL) function and Implicit Evidence-based Likelihood (IEL) function. An inferential diagram is developed to intuitively generate the required implicit evidence taking both outer-source information and the system configuration into consideration. An algorithm is then presented for implementation. The contribution of our work is a systematic investigation of the role of dependent evidence in system reliability evaluation and a full Bayesian approach that is applied to various system reliability models. Extensive numerical cases and a practical engineering case are demonstrated for validation and to illustrate the benefits of our approach.

Discrete time dynamic reliability modeling for systems with multistate components

This study proposes modeling techniques for the exact dynamic reliability analyses of systems with the lifetimes of all components following independent and nonidentical discrete phase-type (DPH) distributions. The systems may have series, parallel, standby, K-out-of-N, and bridge structures with any combinations of them. The models produce numerical formulas and algorithms for generating system reliability and hazard functions; thus, they are applicable to the dynamic reliability analysis of systems, including networks. The approach is by showing that the system lifetime follows a DPH distribution. For network reliability analysis, the DPH distribution is generalized into a matrix-geometric (MG) distribution. The use of the DPH distribution makes the models suitable for systems with multistate components and simplifies the calculations of the system reliability measures. Its effectiveness is illustrated using results from complex structure systems.

A new framework of complex system reliability with imperfect maintenance policy

The interactions and dependencies between software and hardware are often neglected in modeling system reliability in the past few decades due to the mathematical complexity. However, many system failures occurred from the interactions or simultaneous occurrences of software and hardware. This paper first proposes a new diagram of categorizing system-level failures and further incorporates such a diagram into the development of complex system reliability framework. System-level failures result from software subsystem, hardware subsystem, and the interactions of software and hardware subsystems. The focus of this study is on the investigation of the interactions failures generated from the interactions of software and hardware subsystems. In addition to the considerations of total hardware failures, software-induced hardware failures, and hardware-induced software failures introduced by Zhu and Pham (Mathematics 7(11):1049, 2019), we further introduce the partial hardware failures that can be respectively induced by hardware and software to explicitly demonstrate the dependencies and interactions between software and hardware. Hence, a new complex system reliability framework is developed based on such system-level failure categorization with the Markov process. Furthermore, the numerical examples are studied to illustrate the impacts on system reliability with the changes of state transition parameters that modeling the interactions of software and hardware subsystems. Finally, we have studied two maintenance policies of the proposed complex system reliability model.

A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver

The accurate and effective reliability prediction of light emitting diode (LED) drivers has emerged as a key issue in LED applications. However, previous studies have mainly focused on the reliability of electrolytic capacitors or other single components while ignoring circuit topology. In this study, universal generating function (UGF) and physics of failure (PoF) are integrated to predict the reliability of LED drivers. Utilizing PoF, lifetime data for each component are obtained. A system reliability model with multi-phase is established, and system reliability can be predicted using UGF. Illustrated by a two-channel LED driver, the beneficial effects of capacitors and MOSFETs for the reliability of LED drivers is verified. This study (i) provides a universal numerical approach to predict the lifetime of LED drivers considering circuit topology, (ii) enhances the modelling and reliability evaluation of circuits, and (iii) bridges the gap between component and circuit system levels.

System reliability modeling and life cycle research considering component characteristics and correlation

The accuracy and rationality of the system life depends not only on the detailed grasp of the system operation information, but also on the structural relationship between the internal components of the system and the correlation between the characteristics of each component and the degradation of the components. This article explicitly considers the different characteristics of component failure, and adopts different random process personalized modeling for the failure modes of different components; on this basis, the failure correlation between the components that work together to realize the system function is considered, and the synergistic effect of component failure is integrated into the reliability degradation model of components. In terms of system failure criteria, starting from an economic point of view, the overall working cycle of the system is obtained by comparing the relationship between the replacement cycle of lossy parts and the remaining life of the stable parts. Finally, an example is given to show the effectiveness and practicability of the proposed method.

Variable structure Copula model and its Comparison with static and time-varying Copula models

Copula model has been increasingly used in reliability modeling of correlation systems. In this paper, the static copula model, time-varying copula model and variable structure copula model are compared. Firstly, variable structure copula model is introduced. Secondly, the procedures of Bayes diagnosis method which is used to diagnose the variable structure points are introduced. Finally, the correctness of Bayes diagnosis method for finding variable structure points is verified by a simple numerical example. The comparison results illustrate that the staged correlation parameter estimated by variable structure Copula model is better than the constant correlation parameter estimated by static Copula model, and also it has a consistent trend with correlation parameter estimated by time-varying Copula model.

Прогнозування і управління надійністю в умовах застосування запобіжних пристроїв з детермінованим навантаженням спрацьовування

Reliability prediction of machinery parts being designed is primarily aimed at avoiding mechanical failures in service, caused by the influence of force mechanical interaction of parts among themselves and with the environment. Special attention should be given to sudden mechanical failures due to the fact that their development, unlike gradual failures, as a rule, does not lend itself to individual diagnosis and prevention. In addition, as practice shows, sudden failures can often occur in the initial operation period of the equipment, which adversely affects its competitiveness when it is introduced to the market. Most transport, agricultural, road-building and other mobile machines has operating conditions, use modes, and, therefore, modes of mechanical loads of elements with a wide range of variation. Most time, a machine normally operates under normal (nominal) load conditions, whereby long-term performance and durability should be provided. However, in some relatively rare cases, some parts of machines experience short-term and repeatedly repeated effects of extreme loads close to breaking or inelastic material deforming material details. The book is devoted to a brief review of the fundamentals of one of the many directions of science of reliability - predicting risks of sudden mechanical failures and associated reliability indicators. The safety factor is a generalized parameter which in many respects determines the future material consumption and the cost price of the designed product. Therefore, dependencies linking the safety factors of the machine parts with the predicted reliability indexes allows, at the design stage, to choose a rational and economically feasible option to ensure a sufficient level of reliability. The same concerns justification for tusing safety devices that provide mechanical reliability. Therefore, the use of a set of methods for forecasting and managing reliability stated in the book should improve the efficiency of work related to the design of engineering products. The above reliability models of elements and systems protected from repeated impact of overloads by means of a safeguard with a nearly constant actuation load can serve as a theoretical basis when being designed and during modernization of machinery elements to manage reliability rationally.

Economic analysis of system reliability model under operation in changing weather

Economic analysis of system reliability model under operation in changing weather

Модели надежности электроснабжения объектов нефтедобычи

This paper presents power supply systems reliability models of oil production facilities. There are two types of electrical complexes: an electrical network with a consumer sensitive to voltage dips and an electrical network with a consumer with technological redundancy. A comprehensive analysis of the power supply system and the power consumer allowed us to clarify the reliability indicators and determine the direction of improving the power supply reliability to oil and associated petroleum gas production facilities

LTE-U based Train to Train Communication System in CBTC: System Desin and Reliability Analysis

Communication-Based Train Control (CBTC) system is a critical signal system to ensure rail transit’s safe operation. Compared with the train-ground CBTC system, the train control system based on train-to-train (T2T) communication has the advantages of fast response speed, simple structure, and low operating cost. As the core part of the train control system based on T2T communication, the reliability of the data communication system (DCS) is of great significance to ensure the train’s safe and efficient operation. According to the T2T communication system requirements, this paper adopts Long-Term Evolution-Unlicensed (LTE-U) technology to design the DCS structure and establishes the reliability model of the communication system based on Deterministic and Stochastic Petri Nets (DSPN). Based on testing the real line’s communication performance parameters, the DSPN model is simulated and solved by π -tool, and the reliability index of the system is obtained. The research results show that the LTE-U-based T2T communication system designed in this paper meets the train control system’s needs for communication transmission. This paper’s reliability evaluation method can complete the reliability modeling of train control DCS based on T2T communication. The research in this paper will provide a strong practical and theoretical basis for the design and optimization of train control DCS based on T2T communication.

ALGORITHMS AND SOFTWARE SUITE FOR RELIABILITY ASSESSMENT OF COMPLEX TECHNICAL SYSTEMS

Context. One of the most essential properties of technical systems is their reliability, i.e. the ability of the system to perform intended functions, preserving with time the values of operation indicators within the predefined boundaries. The failure cost for modern complex technical system can be very high, which can result in events of different severity ranging from economic losses to harm to human life and health. Hence, the requirements for their reliability constantly increase. The reliability assessment of complex technical systems can be simplified by the combination of analytical research methods with computational capabilities of modern computers. The most widely used analytical methods are based on the theory of Markov processes which in turn provide the possibility to determine the time dependencies of probabilities of the system to be in defined states (operating, recovering, failure etc.), and thus the values and time dependencies of the reliability indices needed. These methods can be successfully used for the reliability analysis of different kinds of technical systems: both non-recovered and recovered; non-redundant and redundant of different redundancy types, maintenance priorities etc. However, the application of these methods for complex technical systems containing large number of elements meets the high dimensional calculation problem, which makes it impossible to perform these tasks manually. Hence the problem of automation of complex technical system reliability modeling using modern computational systems is very relevant research topic. To solve this problem, one can use specific algorithmic and software techniques described in this paper.Objective. The goal of this article is to develop the algorithms for automated RBD processing and reliability indices assessment of complex technical systems along with the software suite for automated reliability assessment.Method. To perform the reliability analysis the RBD approach is used which allows one to represent and visualize each element of the system in the form of a rectangle, joined by the lines in parallel or in series with other elements of the system. To obtain the reliability indices values the mathematical model of technical system reliability behavior using Markovian random process was suggested. The algorithm of RBD processing and automatic determination of operability conditions of a technical system was further considered. To calculate the minimum and maximum number of operational and failure states for the system of n elements and r recoveries the paper introduces a mathematical model based on combinatorial approach. To develop the software suite the objectoriented approach was used.Results. The algorithms and software suite allows us to easily construct RBD for a technical system, to automatically determine the operability condition with execution time of about 10 sec for 1,000 elements with mixed type of connection, to form automatically a state-and-transition matrix along with the corresponding differential equation system and solve it with total execution time of about 35 sec for 109 states and, thus to obtain the numerical values of reliability indices for the technical system studied. A case study of the reliability assessment for the system consisting of 22 elements using RBD shows that the total time of software execution is 36.712 sec. During executing of this test case the most time (35.168 sec) was spent for execution of the algorithm for construction of a state-and-transition graph consisting of 52,694 states.Conclusions. The algorithms and methods for automated reliability indices assessment of complex technical systems based on RBD approach, as well as model for estimating the number of total and working system states are presented. The modular structure of the developed software suite makes it flexible and gives an opportunity to add and make modifications of modules fast and without significant program changes.

A sequence-based method for dynamic reliability assessment of MPD systems

Managed Pressure Drilling (MPD) system is widely used in the deepwater drilling operation. Reliability assessment plays a critical role in the MPD system in the management of drilling operation risk and the prevention of blowouts. However, the reliability assessment of the MPD system is challenged due to its sequential operations and multiple processes. Consequently, the present work proposes a sequence-based dynamic reliability assessment method, which focuses on the dynamic modeling of sequential operations for the MPD system by integrating GO-FLOW and dynamic Bayesian Network (DBN). GO-FLOW models are firstly used to define the time interaction between multiple phases for complex systems. A sequence-based mapping method is also proposed for the DBN to construct the reliability model of the MPD system throughout the entire drilling cycle. In the end, the case study analyzed by the proposed framework indicates that the reliability of the MPD system decreases with increasing drilling depth, and the reliability of “tripping in” is highest among four different phases, while the “drilling process” is the lowest. The method provides an important technique that can be implemented with online condition monitoring tools to assess and monitor the reliability of the MPD operation in real-time.

Reliability modeling and analysis for systems governed by multiple competing failures processes

Due to that the operating environment is becoming more and more complex and rigorous, the multiple competing failure modes for the reliability system is much commonly seen. In order to improve the system performance, a sensor-based degradation calibration policy (SBDC policy) is presented in this paper. The model considers the competing failure process which is described by the soft and hard failure modes. In detail, the soft failures occur when the degradation of the system exceeds the failure threshold, and the hard failures are caused by the same shock process. We use the Wiener process model to describe the soft failure and the shock process to describe the catastrophic failure. Meanwhile, in the shock process, the damage associated with the system is normal distributed which is related to the duration of the adjacent shocks. This extended model with calibrations has a good application value for the corresponding complex reliability systems which are subject to the dependent competing failure modes. By the model in this article, the system reliability and safety can be improved and the risk of the abrupt damage shall be reduced as the circumstance changes.