A Global Reliability Assessment Method on Aging Offshore Platforms with Corrosion and Cracks

Corrosion and fatigue cracks are major threats to the structural integrity of aging offshore platforms. For the rational estimation of the safety levels of aging platforms, a global reliability assessment approach for aging offshore platforms with corrosion and fatigue cracks is presented in this paper. The base shear capacity is taken as the global ultimate strength of the offshore platforms. It is modeled as a random process that decreases with time in the presence of corrosion and fatigue crack propagation. And the corrosion and fatigue crack growth rates in the main members and key joints are modeled as random variables. A simulation method of the extreme wave loads which are applied to the structures of offshore platforms is proposed too. Furthermore, the statistics of global base shear capacity and extreme wave loads are obtained by Monte Carlo simulation method. On the basis of the limit state equation of global failure mode, the instantaneous reliability and time dependent reliability assessment methods are both presented in this paper. Finally the instantaneous reliability index and time dependent failure probability of a jacket platform are estimated with different ages in the demonstration example.

Reliability assessment on interfacial failure of thermal barrier coatings

Thermal barrier coatings（TBCs） usually exhibit an uncertain lifetime owing to their scattering mechanical properties and severe service conditions. To consider these uncertainties, a reliability assessment method is proposed based on failure probability analysis. First, a limit state equation is established to demarcate the boundary between failure and safe regions, and then the failure probability is calculated by the integration of a probability density function in the failure area according to the first- or second-order moment.It is shown that the parameters related to interfacial failure follow a Weibull distribution in two types of TBC. The interfacial failure of TBCs is significantly affected by the thermal mismatch of material properties and the temperature drop in service.

Bayesian method for system reliability assessment of overlapping pass/fail data

For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.

Probabilistic seismic response and reliability assessment of isolated bridges

Bridge seismic isolation strategy is based on the reduction of shear forcestransmitted from the superstructure to the piers by two means: shifting natural period andearthquake input energy reduction by dissipation concentrated in protection devices. In this paper,a stochastic analysis of a simple isolated bridge model for different bridge and device parametersis conducted to assess the efficiency of this seismic protection strategy. To achieve this aim, asimple nonlinear softening constitutive law is adopted to model a wide range of isolation devices,characterized by only three essential mechanical parameters. As a consequence of the random natureof seismic motion, a probabilistic analysis is carried out and the time modulated Kanai-Tajimistochastic process is adopted to represent the seismic action. The response covariance in the statespace is obtained by solving the Lyapunov equation for a stochastic linearized system. After asensitivity analysis, the failure probability referred to extreme displacement and the mean value ofdissipated energy are assessed by using the introduced stochastic indices of seismic bridgeprotection efficiency. A parametric analysis for protective devices with different mechanicalparameters is developed for a proper selection of parameters of isolation devices under differentsituations.

Probabilistic Methods for Reliability Assessment of Floating Structures Based on Extreme Wave Simulation

With the increasing application of floating platforms in deep waters and harsh environments,a proper assessment of the reliability of floating structures is important to ensure that these structures can operate safely during their design lives.This study outlines a practical methodology for reliability analysis of a semi-submersible platform based estimating the probability distribution of the extreme response in rough sea conditions(survival conditions).The Constrained NewWave(CNW)theory combined with Monte Carlo simulations was first applied to simulate the random wave surface elevation process in the time domain.A Gumbel distribution was the best fitting to describe the dynamically sensitive extreme response statistics under extreme waves(drift and mooring tension).The derived probability distribution of the extreme response was subsequently used in estimation of the associated limit state func-tion,and a reliability analysis of the floating structure was conducted using the Monte Carlo method.A semi-submersible platform in a water depth of 1500 m subjected to extreme wave loads was used to demonstrate the efficiency of the proposed methodology.The probability of failure of the semi-submersible when considering mooring lines tension is greater than considering drift.

Methods for Reliability Assessment under Irregular Time-Varying-Stress Degradation Testing

Degradation tests are often used to assess the reliability of products with long failure-time or few test units. Much of the previous work on reliability assessment methods has focused on constant-stress degradation test( CSDT) and accelerated degradation test( ADT), mainly under the constant, step or progressive stresses. However,in actual testing environments,some stresses are difficult to control and vary with time irregularly,which are quite different from the three stresses mentioned above. In this paper a new approach was presented for reliability assessment with degradation data under irregular time-varying-stress( ITVS).Firstly,the conventional degradation path modeling method was improved by taking into account the influences of the variable stress on the degradation variable. Then,an example was conducted to show the effectiveness of our improved model.

Integrated Grease-pouring System Design and Reliability Assessment of Engineering Equipment

Because of poor lubrication, the abrasion of the engineering equipment＇s key connection position （the hinge joint and so on） has become the major factor that affects the mechanical benefit. The excavator WY60 is taken as an example in this paper. The automatic grease-pouring, the working state test, and the alarm for fault of every lubricating point on the machine is accomplished by the control sub-system, the lubricating system and other correlated components. The principle and functions of the system are described. The work condition is analyzed. Finally, we present the plan of the software and the hardware of the system, and set up the fuzzy assessment model of reliability of the system.

A Branch-independence-based Reliability Assessment Approach for Transmission Systems

This paper proposes a branch-independence-based reliability assessment approach for transmission systems.The approach consists of branch decoupling and state-space partition techniques.By integrating an impact-increment-based reliability index calculation model and the proposed branch decoupling technique,a proportion of sampled contingency states no longer need to be analyzed using the time-consuming optimal power flow(OPF)algorithm.In this way,the technique speeds up the calculation of reliability indices.Since first-order contingency states have a high probability of being sampled,we propose a state-space partition technique to replace first-order contingency state simulation with first-order contingency state enumeration.Consequently,the calculation of reliability indices is further accelerated by avoiding a large amount of repetitive OPF analyses during simulation process without affecting reliability index accuracy.The validity and applicability of our approach are verified using the IEEE 118-bus and IEEE 145-bus systems.Numerical results indicate that the proposed approach can improve computational efficiency without decreasing accuracy.

Reliability Assessment of Integrated Electricity and Natural Gas Transmission Systems in Presence of Power-to-gas and Combined Heat and Power Technologies

Assessing the reliability of integrated electricity and gas systems has become an important issue due to the strong dependence of these energy networks through the power-to-gas(P2G)and combined heat and power(CHP)technologies.The current work,initially,presents a detailed energy flow model for the integrated power and natural gas system in light of the P2G and CHP technologies.Considering the simultaneous load flow of networks,a contingency analysis procedure is proposed,and reliability is assessed through sequential Monte Carlo simulations.The current study examines the effect of independent and dependent operation of energy networks on the reliability of the systems.In particular,the effect of employing both P2G and CHP technologies on reliability criteria is evaluated.In addition,a series of sensitivity analysis are performed on the size and site of these technologies to investigate their effects on system reliability.The proposed method is implemented on an integrated IEEE 24-bus electrical power system and 20-node Belgian natural gas system.The simulation procedure certifies the proposed method for reliability assessment is practical and applicable.In addition,the results prove connection between energy networks through P2G and CHP technologies can improve reliability of networks if the site and size of technologies are properly determined.

Reliability Assessment of a Cyber Physical Microgrid System in Island Mode

As cyber physical systems,microgrids(MGs),with distributed generations and energy management systems,can improve the reliability of power supply for customers in MGs.To quantify the reliability of isolated MGs,a cyber-physical assessment model is proposed.In this model,the circuit breakers and distributed energy resources are treated as the coupling elements between the cyber system and physical system,where the circuit breakers are uniquely modelled by using the Markov process theory based on the indirect interdependencies between cyber physical elements.For the cyber system,the reliability model of communication networks is formulated based on the link connectivity evaluation method.For the physical system,a system state generating method is presented to account for the optimal operation strategy,which considers the influence of the optimization strategy on the failure consequence analysis.With the proposed cyber and physical reliability models,the sequential Monte Carlo(SMC)simulation method is adopted to assess the reliability of islanded MGs.Simulations are carried out on a test system,and results verify the feasibility and effectiveness of proposed assessment method.Furthermore,one application of the proposed method is on the parameter setting of the cyber system,in terms of enhancing MGs reliability.

New concept and procedure for reliability assessment of an IEC 61850 based substation and distribution automation considering secondary device faults

Smart grid is a power grid consists of extensive monitoring systems which deal with the monitoring of attributes such as current, voltage, power, and energy at distribution transformers, substations transformers, distri- bution switching devices and smart meters. Smart grid with advanced communication technologies can be used for several purposes such as efficiency and reliability improvement. IEC 61850 is the core standard in the smart grid domain for distribution and substation automa- tion. This paper introduces a vision of modem substation and distribution systems using the IEC 61850. Network operators mainly assume that the modem substation and distribution systems based on the IEC 61850 are reliable for a long-time of operation. However, similar to any other systems, the implemented IEC 61850 might fail because of the operational failures or aging failures. This paper proposes a novel method for reliability evaluation of modem substation and distribution systems. A typical IEC 61850 based distribution and substation system is devel- oped and analyzed using the proposed method. The fault tree analysis （FTA） is used to quantify the reliability of the system. The technique is implemented and demonstrated on the Roy Billinton test system （RBTS）. The analysis is further extended on a 400/63 kV substation with a breaker- and-a-half configuration. In addition, the technique proves to be robust under different operations. The results verify the feasibility and applicability of the proposed method.

Imprecise Reliability Assessment for Heavy Numerical Control Machine Tools Against Small Sample Size Problem

Small sample size problem is one of the main problems that heavy numerical control(NC) machine tools encounter in their reliability assessment. In order to deal with the small sample size problem, many indirect reliability data such as reliability data of similar products, expert opinion, and engineers' experience are used in reliability assessment. However, the existing mathematical theories cannot simultaneously process the above reliability data of multiple types, and thus imprecise probability theory is introduced. Imprecise probability theory can simultaneously process multiple reliability data by quantifying multiple uncertainties(stochastic uncertainty,fuzzy uncertainty, epistemic uncertainty, etc.) together. Although imprecise probability theory has so many advantages, the existing natural extension models are complex and the computation result is imprecise. Therefore,they need some improvement for the better application of reliability engineering. This paper proposes an improved imprecise reliability assessment method by introducing empirical probability distributions to natural extension model, and the improved natural extension model is applied to the reliability assessment of heavy NC machine tool spindle to illustrate its effectiveness.

Data learning and expert judgment in a Bayesian belief network for aiding human reliability assessment in offshore decommissioning risk assessment

Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity.The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling.The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment.Initial analysis from well workover data,of a 5-node BBN provided insights on two different levels of severity of an accident,the’Accident’and’Incident’level,and on its respective profiles of the initiating events and the investigation-reported human causes.The initial results demonstrate that the data learnt from the database can be used to structure the BBN,give insights on how human reliability pertaining to well activities can be modelled,and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes.It is also proposed that the integrated treatment of various sources of information(database and expert judgement)through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.

Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing

The reliability of an Engine Electronic Controller(EEC)attracts attention,which has a critical impact on aircraft engine safety.Reliability assessment is an important part of the design phase.However,the complex composition of EEC and the characteristic of the Phased-Mission System(PMS)lead to the difficulty of assessment.This paper puts forward an advanced approach,considering the complex products and uncertain mission profiles to evaluate the Mean Time Between Failures(MTBF)in the design phase.The failure mechanisms of complex components are deduced by Bayesian Deep Learning(BDL)intelligent algorithm.And copious samples of reliability simulation are solved by cloud computing technology.Based on the result of BDL and cloud computing,simulations are conducted with the Physics of Failure(Po F)theory and Failure Behavior Model(FBM).This reliability assessment approach can evaluate MTBF of electronic products without reference to physical tests.Finally,an EEC is applied to verify the effectiveness and accuracy of the method.

A Reliability Assessment of an Integrated Energy System Based on Coupling Energy Flow and Thermal Inertia

With the wide application of integrated energy systems(IES),the degree of coupling between different types of energy sources is further strengthened,and the mechanism of fault development tends to be complicated.Therefore,in order to improve the accuracy and practicability of the reliability assessment of IESs,a sequential simulation reliability assessment method considering multi-energy flow and thermal inertia is proposed in this paper.In this method,the IES structure model is constructed with the combined cooling,heating and power(CCHP)unit as the core equipment,combining with the new energy source to realize the comprehensive energy power flow calculation considering any loss.Then,a load reduction optimization model is established,considering the importance of load and operational economy,in the system status analysis.Furthermore,the heat supply reliability index is corrected taking into account the transmission delay characteristics and terminal thermal inertia.A case study based on the 20-node and the analysis of the influence factors demonstrates the validity of the proposed method.

Impact Increment Based Hybrid Reliability Assessment Method for Transmission Systems

This paper proposes an impact-increment-based hybrid(IIHybrid)reliability assessment approach for power transmission systems.The proposed approach integrates the advantages of the impact-increment-based state enumeration method(IISE)and impact-increment-based Monte Carlo simulation(IIMC)to improve computational efficiency and accuracy.The IISE can efficiently assess the impacts of low-order contingencies.The accuracy is,however,sacrificed as highorder contingencies are usually neglected.The IIMC is more suitable for large-scale contingency spaces compared with IISE,although the calculation process is time-consuming.In this paper,the proposed IIHybrid takes advantage of its strengths while avoiding its shortcomings.The IISE and the IIMC are applied to lower and higher contingency spaces respectively.The high-order contingencies elimination technique proposed in our previous studies is still applicable to the IIHybrid.In addition,efficiency can be controlled by modifying the preset parameters to adapt to various scenarios.Case studies are performed on the IEEE 118-bus test system and PEGASE System.The results show that the proposed approach is more efficient and practicable than traditional methods.

Bayesian Assessment for Reliability of Binomial Components Based on Information Fusion of Similar Products

A modified Bayesian reliability assessment method of binomial components was proposed by fusing prior information of similar products.The traditional Bayesian method usually directly used all the prior data,ignoring the differences between them,which might decrease the credibility level of reliability evaluation and result in data submergence.To solve the problem,a revised approach was derived to calculate groups of prior data's quantitative credibility,used for weighted data fusion.Then inheritance factor was introduced to build a mixed beta distribution to illustrate the innovation of new products.However,in many cases,inheritance factor was determined by Chi-square test that could not give out exact result with respect to rare failures.To make the model more precise,Barnard's exact test was suggested being used to calculate the inheritance factor.A numerical example is given to demonstrate that the modified method is successful and rational,while the classical method is too conservative and the traditional Bayesian method is too risky.

Assessment of Multiple Working Condition System Reliability with Agent-Based Simulation Method

The mission reliability assessment plays a great role in logistics planning and supporting resource optimization of complex system.But the current problem,which is difficult to solve,is how to model and analyze the characters of system reliability under the complex mission profile.In order to solve the problem,an agentbased simulation method was used to assess reliability for complex systems with various random working conditions.A multi-working condition simulation agent(MA)was designed and used to simulate the random transferring process of working conditions of system,and it cooperated with system simulation agents(SAs)and unit simulation agents(UAs)to realize system mission reliability(MR)simulation.Through simulation experiments,effect of multiple working conditions mission on the reliability of system was analyzed by comparing with the basic reliability condition.Feasibility and efficiency of the method were proved through simulation experiments of the case system.The research result provides a viable and useful method and a solution for MR analysis and assessment of complex systems in multi-working conditions,which can help to evaluate the reliability of operating system orienting to the practical mission and environment,and it is meaningful for the reliability analysis and the design of complex systems.

Assessment of Operating Condition Dependent Reliability Indices in Microgrids

Reliability results are important for proper planning and operation of utility companies. At the base of this method of analysis is the failure rate of the system components. In the traditional method, this probability of failure is determined by the components’ manufacturer and is considered to be constant. This study proposes a dynamic modeling of failure rate, taking the system operating conditions into consideration. With this new consideration, an IEEE test system has seven of its reliability indices quantified for comparison. The inclusion of the newly modeled failure rate leads to a worsening of 11.07% in the indices, on average. A second analysis is performed considering the presence of DG sources within the microgrid, namely PV and wind based. An improvement of 0.71% on the indices is noticed, once the DG sources are introduced. Finally, the effects of storage systems in the microgrid are investigated through a third scenario, in which two 2 MWh battery systems are introduced, and an improvement of 3.05% is noticed in the reliability indices.

An Uncertainty Analysis and Reliability-Based Multidisciplinary Design Optimization Method Using Fourth-Moment Saddlepoint Approximation

In uncertainty analysis and reliability-based multidisciplinary design and optimization(RBMDO)of engineering structures,the saddlepoint approximation(SA)method can be utilized to enhance the accuracy and efficiency of reliability evaluation.However,the random variables involved in SA should be easy to handle.Additionally,the corresponding saddlepoint equation should not be complicated.Both of them limit the application of SA for engineering problems.The moment method can construct an approximate cumulative distribution function of the performance function based on the first few statistical moments.However,the traditional moment matching method is not very accurate generally.In order to take advantage of the SA method and the moment matching method to enhance the efficiency of design and optimization,a fourth-moment saddlepoint approximation(FMSA)method is introduced into RBMDO.In FMSA,the approximate cumulative generating functions are constructed based on the first four moments of the limit state function.The probability density function and cumulative distribution function are estimated based on this approximate cumulative generating function.Furthermore,the FMSA method is introduced and combined into RBMDO within the framework of sequence optimization and reliability assessment,which is based on the performance measure approach strategy.Two engineering examples are introduced to verify the effectiveness of proposed method.