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On the basis of the continuum damage mechanics, a new low cycle fatigue damage accumulation model, which takes the coupling effect of damage and strain into account, is proposed. The model has some practical values because of its clear physical meanings. With the analysis of low-cycle test data for a kind of material often used in aero engine, we know that the prediction data derived from this new model and the test data are in good agreement. Moreover, this new model is more accurate and has apparent superiority in fatigue life prediction and description of fatigue damage accumulation by comparing with the traditional linear model.

Failure Mode, Effects and Criticality Analysis (FMECA) is used widely in engineering practices such as aerospace, medical facilities, naval vessels and shipping, which improves the reliability and safety of the products significantly. It can eliminate potential failures, errors and problems from design, system and process. To overcome the drawback in the traditional Risk Priority Number (RPN) [1], Gargama, H. proposes a model based on the combination DM (Degree of Match) and fuzzy rule-base employing fuzzy logic method [2]. Consider the situation that the different expert may has different opinions and knowledge backgrounds, they will give different criteria. Due to the incomplete information, less data and various uncertainties, three traditional risk factors O (Occurrence), S (Severity) and D (Detectability) can be treated as fuzzy numbers, therefore, the fuzzy linguistic terms provides a very appropriate way for modeling this case. The degree of matching is used to estimate the matching between the experimental team members' assessed information and the fuzzy number by transformation the assessed information into convex normalized fuzzy sets. An engineering example, flight control system (FCS) in unmanned vehicle, is presented to demonstrate the proposed fuzzy FMECA method. The illustrated example has shown that the proposed method advances the reliability and safety for it can model various uncertainties effectively.

Fuzzy fault tree analysis of uncontained event for aero-engine rotor is performed. The theory of fuzzy fault tree analysis was introduced firstly. Then the fault tree of uncontained event for aero-engine rotor is established, in which the descending method is used to find minimal cut sets. Further more, the interval representation and calculation strategy is presented when the symmetrical L-R type fuzzy number was chosen to describe the failure probability, and the built fault tree is analyzed quantitatively.

It is very important to implement chaotic-fractal dynamic method in complex quality control processes because the transfer and control of quality characteristics variation of complex products can't be executed due to uncertainty and it's high coupling in the nonlinear quality systems. According to the complex product quality system's characteristics of multi-dimensional, multi-processes, nonlinear, coupling and so on, based on the analysis of chaotic evolution process of complex product quality characteristics variation, a chaotic transfer pattern is established. The chaotic characteristics of the quality characteristics variation are analyzed, such as the initial value sensitivity, density and fractal dimension. Furthermore, a new concept of quality system structure parameter, the parameter to determine whether the complex product quality system is in the stable control time domain or the chaos control time domain, is proposed. And deciding region of chaotic critical spot on structure parameters of complex quality system is discussed. Chaotic state-space mathematical model of quality characteristics variation transfer is developed, and an example for complex product assembly process is given. Finally, an instance related to direct-drive gear hobbing machine is given to demonstrate that the proposed model can solve quality characteristics variation transfer problems effectively.

Possibilistic reliability theory is the study of reliability based on the principles of possibility theory. In this paper, the possibilistic reliability function of multi-state system is redefined and analyzed utilizing the possibility distribution of system lifetime. In order to express the internal relationship between system state and residual lifetime, state corresponding most possible residual lifetime (MPRL) is defined as the system's most possible remaining lifetime under the state. Thus, addressing the system reliable level as keeping a specified state at a specified moment, the system possibilistic reliability can be obtained through the functional relationship between system state and the corresponding MPRL. As a result, a numerical example is developed to illustrate the theory.

Taking account of the influence of common cause failure (CCF) to system reliability, a method for reliability modeling and assessment of a multi-state system with common cause failure based on Bayesian Network (BN) is proposed by using the advantage of uncertainty reasoning and figurative expression of BN. The model is applied to a two-axis positioning mechanism transmission system to demonstrate its effectiveness and ability to directly calculate the system reliability on the basis of multi-state probabilities of elements. The comparison between the proposed method and the method without considering CCF verifies the efficiency and accuracy of the proposed method.

Redundancy technology is widely used as an effective design technique in improving task reliability and safety of system. However, the increasing units result in depression of basic reliability. To keep a balance between these two targets is one of the main aspects in redundancy design. In this paper, we establish game model of FCS reliability using static game theory with incomplete information, and generate corresponding Bayesian Nash Equilibrium, with which we can define redundant configuration under limited maintenance cost approximately. This paper provides the theoretical reference for FCS redundancy design.

In this paper, a unified uncertainty analysis method based on the extension universal generating function is proposed for engineering problems described by the mixture of random, interval and p-box variables. In the method, the univariate approximation approach is extended for mixed variables, and then the performance function is divided into three parts. Traditional universal generating function can only model the case that all variables exist in system are random variables. However, in order to calculate the bounds of system probability of failure under mixed variables, the mixed universal generating function is developed to extend the traditional universal generating function. The optimization models based on the mixed universal generating function are presented to calculate system probability of failure under mixed variables. An engineering example is used to validate the proposed method.

This paper proposes a new simulation model for emergency evacuation of civil aircrafts taking into account of influences from passengers' physical attributes. The new model uses the staggered mesh discretization method to generate evacuation map and the intelligent agent-based model to simulate evacuation process. With the proposed model, artificial passengers are able to enter the same cell if they have the same moving directions in the simulation process. In addition, the influences from physical attributes of passengers on the evaluation time are incorporated into the simulation. The effectiveness of the proposed method is demonstrated via a case study.

Maintainability analysis method combined with FMECA and FTA is proposed for the failure analysis of a type of diesel engine combustion system. Failure modes and cause analysis for this type of diesel engine combustion system are identified, and the hazard is analyzed. Finally, the real failure situation of this diesel engine is presented to validate the analysis results from the proposed method, which can be used to conduct the maintenance activities.

In this research, a stress-strength interference model is developed based on bi-variant Lagrange factor polynomial approach. The first discrete random variable is the independent stress. Thermal stress-dependent strength is represented by the second discrete random variable that has different conditional probability mean functions depending on different stress amplitudes. Then the approach is used to generate the bi-variant stress-strength interference model. In this model the thermal strength probability mean function is Rayleigh distributed, while the thermal stress probability mean function is Normal distributed. Accuracy of this method is investigated by specific aeroengine combustion cylinder.

Based on the understanding of the flight control system of built-in test (BIT) structure, it gets a summary about the cause of the flight control system about generating false alarms, and analyzes the sensing layer during the information processing of the BIT system. Then put forward a method for sensor fault diagnosis which is based on observer in the sensing layer, then isolate the fault timely and prevent the false alarm, so as to reduce false alarm rate of the whole BIT system. It will improve the performance of BIT and promote the research of BIT false alarm.

Many engineering systems are exposed to run in load-varying environment. Load has a significant impact on the deteriorating process of components and system. Distributing the loads optimally among components of a system can reduce the failure probability and prolong the system life. In this paper, an approach to load distribution for multi-state systems under selective maintenance strategy is developed. In addition to optimally allocating the limited maintenance budget among components, the proposed method also provides a feasible way to distribute loads imposed on components so as to maximize the probability of system successfully completing the next mission. The resulting optimization problem is solved by the genetic algorithm (GA). As observed from our comparative study, the proposed method always yields a better result that the existing method which doesn't take load distribution into account.

The increasing complexity of engineering systems has sparked rising interests in multidisciplinary design optimization (MDO). However, the main challenges in MDO are its computational expense and complexity structure. Therefore, to address these challenges, this paper focuses on a new MDO method, interaction prediction optimization approach, which is a design architecture applied in the multidisciplinary environment. In this approach, a complex system is decomposed hierarchically along disciplinary boundaries into a number of subsystems, which are taken multidisciplinary agreement into account by a system-level coordination process. A mathematical problem and an engineering design problem are presented to highlight the potential application of the new method in multidisciplinary design optimization.

Improved fatigue life prediction methods will assist in reducing life cycle costs and increasing the availability of mechanical components. These methods include Stromeyer function, Basquin law, Manson-Coffin law, frequency modified damage function (FMDF) model, strain range partition (SRP) and so on. However, different models are applied to assess the life under high cycle fatigue (HCF) and low cycle fatigue (LCF). This is a domain of concern in the development of fatigue life prediction models which contain combinations of HCF and LCF. In this paper, based on the Manson-Coffin law and an energy-based damage parameter, a general energy-based model is presented to predict fatigue life under both HCF and LCF conditions. By correlated with the energy-based damage parameter to the test data of superalloys GH901 and K403 in existing literatures, the lives predicted by the proposed model are compared with experimental results resulting in a good agreement.

Engineering experience reminds us of the importance of uncertainty analysis in fatigue reliability based design. In the present paper, according to the procedure of fatigue reliability analysis, parameter uncertainty and model uncertainty are studied and the corresponding solutions are reviewed. The aim of parameter uncertainty analysis is to obtain reliability index to quantify uncertainties imported by parameters. The current research focuses on defining reliability index and putting forward different methods to handle different variable expressions (random, convex, fuzzy set). Different from parameter uncertainty analysis, model uncertainty analysis can reduce uncertainties imported by model by modifying model structure or selecting best model. In application, both methods can effectively quantify and reduce uncertainty. The development on both methods will contribute to understanding fatigue process and its causes.

Reliability is one of the critical attributes for long life and high performance satellite. To deal with the limitation on satellite reliability analysis confronted in the past decades, a series of papers has been published recently by Saleh et al. In these papers, thousands of Earth-orbiting satellites successfully launched between 1990 and 2008 have been analyzed. This paper extends the statistical analysis of satellite reliability based on the same database. The satellite reliability is modeled using a modified Weibull extension distribution. The parametric estimation is carried out by utilizing the nonlinear least squares method. Finally, a comparative analysis is conducted on the fitting results of Weibull and modified Weibull extension distribution. The results demonstrate that the modified Weibull extension distribution is more accurate and flexible in modeling the satellite reliability than the traditional Weibull distribution. The accuracy and convenience of the modified Weibull extension distribution indicates its practical application in engineering designs.

Combining a nonlinear damage accumulation model, a probabilistic S-N curve and one-to-one probability density functions transformation technique, a general probabilistic methodology for modeling damage accumulation is developed to analyze the fatigue reliability in this paper. The damage accumulation is characterized as a distribution in a general degradation path, which captures a nonlinear damage accumulation phenomenon under multi-level loading conditions, its mean and variability change with time. Experimental data of 45 and LZ50 steels used in the railway vehicle are then used to validate the proposed methodology. The fatigue reliability is analyzed and demonstrated using probabilistic model of cumulative fatigue damage.

Knowledge-based diagnostic approaches are usually restricted to known systems because of the nature of knowledge representation in terms of empirical symptom-failure association. Model-based diagnosis methods to the expert systems offer substantial advantages in terms of flexibility and potentials of knowledge reuse. This paper describes a modeling method and diagnosis technique for the power system of the satellite using model-based diagnostic theory. In terms of the structure and behavior of the satellite power system, this paper focuses on building the power system model in a component-based modeling manner, which can achieve the reusability of the existed components and the scalability of system model. Each component in the model contains the relations between input and output, running states, and transitions between states. Experiments on the power system model are performed using simulation data to verify the correctness and effectiveness of the model and modeling method.

Reliability design of an aeroengine burner inner-liner is carried out using a bi-variant Lagrange factor polynomial approach. Thermal stress-dependent strength is represented by a discrete random variable that has different conditional probability mean functions under different thermal stress amplitudes. Then the LFP technique is developed to generate the stress-strength interference model with thermal stress-dependent strength. System reliabilities are computed under three operation modes of the aeroengine. As the structural reliability using GH3039 alloy is much higher than that using GH3030 alloy, it is selected in the part design of the burner inner-liner.

When there is limited objective failure data, a method for developing the possibility distribution by subjective assignment strategies is put forward, which is illustrated by the lifetime data from fatigue reliability tests. Based on a limited fatigue life data, a study on reliability analysis of 40 axle-steel is presented, by constructing the possibility distribution function of the locomotive axle steel fatigue life as well as calculating the possibility reliability of fatigue strength at any one time of the axle under a certain stress level.

Quantification of Margins and Uncertainties (QMU) is a methodology for assessing the confidence in the performance of complex systems, and has been applied to general engineering areas. In this paper, the concept and process of QMU is introduced at first, and then an implementation framework of QMU is proposed for structural analysis considering uncertainty. The framework is a synthesis of several algorithms, including a sampling method, surrogate model with uncertainty, and finite element analysis. An example is presented to illustrate the capability of the developed framework. The results show that the framework is feasible and suitable for the quantification of uncertainty in structural analysis.

Fault tree analysis (FTA) is a popular methodology for system reliability analysis. The occurrence probability of the basic event is accurate in traditional fault tree (FT). Owe to the lack of the reliability data, especially in early design stage; a large amount of uncertainties may be included in FTA. In this study, the Dempster-Shafer (D-S) evidence theory is employed to quantify the uncertainty of the FTA and to aggregate the different evaluation information of multiple experts on the occurrence probability of basic event. The evidential network (EN) is used to propagate the uncertainty in FTA. The FT of the aero-engine damage caused by low pressure of lubricating oil is analyzed.

The demand of kinematic accuracy is increasing for modern mechanical systems, while elastic deformation will lead to the decline in kinematic accuracy. This paper examines the application of finite element analysis (FEA) method to kinematic error analysis of elastic mechanisms based on Kineto-Elastodynamics model. The simulation of limit state equations of link mechanisms is performed with Back Propogation Neural Networks (BPNN), while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method for solving the reliability, of the elastic mechanism. The numerical example shows that the proposed analysis method is feasible and effective in improving the kinematic accuracy and can be applied to reliability analysis of other mechanisms in practical engineering.

Generally, multidisciplinary design optimization (MDO) is a deterministic design method, which does not takes uncertainty into account. However, uncertainty cannot be ignored since it has a significant influence on the design results of the engineering system. In this paper, in order to reduce the effect of uncertainty on system performance, First-Order Second-Moment based collaborative optimization (FOSM-CO), is proposed, where reliability index is used to evaluate the probability of successful operation. The proposed method is employed to conduct speed reducer design and the global optimal solution is obtained, which illustrates the feasibility of the proposed method.

Standard complex system reliability computation is an important topic for system engineers. Current approaches for reliability analysis are fault tree analysis, true value table or minimal path sets. FTA can not solve many complex problems. This paper introduces the method of Bayesian networks for reliability estimation. The complex systems bayesian networks can be built by a new method. Using the arithmetic of bullet elimination, every element of the systems can be analyzed.

In consideration of some drawbacks of stochastic reliability method in theoretical and practical aspects, as well as the existence of insufficient and vague data in actual equipments design, the possbilistic reliability models of typically general systems are constructed in this paper. Characterizing the systems(components) failure behavior by possibility measures in place of probability measures, treating the systems (components) lifetime as fuzzy variables in the possibility space, and introducing possibilistic approaches into system reliability modeling and analysis, the possibilistic reliability theories are advanced based on possibilistic measurements. By expanding the universe of discourse, the derivation of system reliability models is simplified without loss of the nature of the problems to be solved, and proofs and calculations of possibilistic reliability of typical systems including series, parallel, series-parallel, parallel-series, and cold standby systems are much more straightforward than the traditional ones. The detailed application examples in system reliability analysis verify a possibilistic design framework on the general reliability models background, which provide some technical supports and guarantees for possibilistic reliability design and possibilistic reliability management in the case of limited statistical data.

According to the necessity of reliability analysis for satellite system subjected to common-cause failures, the unreliability expression under a variety of common-cause condition is developed to analyze the unreliability of satellite system in this paper. Based on static fault tree analysis of satellite system, the binary decision diagram is used to evaluate the reliability considering common-cause failures. The importance of prevention of common-cause failures in large structured redundancy system is verified by using practical calculation example.

Reliability Based Design Optimization (RBDO) aims to optimize performance function under reliability constraints, which is not considered in the deterministic design optimization. During the past decades, with the advent of powerful computers, lots of literatures focused on developing the methods of RBDO for the accuracy and efficiency. However, with the application of RBDO in various cases, it is always that the reliability target for the whole system is regarded as the reliability target for each probabilistic constraint, and traditional RBDO did not consider system reliability. The RBDO model of crashworthiness for vehicle side impact is the origin where we find the limit of traditional RBDO formulation, which is also a typical model for RBDO. And the of the system reliability compute mode directly influences the formulation of probabilistic constraint function, especially when the number of probabilistic constraint is large-scale. Firstly, the probabilistic approaches for traditional RBDO are introduced, then, the new formulation of RBDO for system reliability is presented, with transforming the traditional RBDO into an expanded RBDO. One numerical example is presented and discussed to demonstrate the difference between expanded RBDO with the traditional RBDO in the mechanical applications.

This paper presents a new non-linear fatigue damage accumulation model based on continuum damage mechanics. The proposed model has been formulated to take into account the damage evolution of material under different loading levels and the effects of loading sequence on fatigue life. In additional, the model has also considered the mean stress in its damage evolution with fewer parameters. Experimental data of 30NiCrMoV12 under two-stress level loading in literature were used to verify the proposed model. Predicted fatigue lives using the proposed model show a good agreement with reported experimental data.